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25 June 2023, Volume 32 Issue 6

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Practical Demand and Theoretical Prospect of Data-driven Emergency Management: 2023 “Wanzhong Cup” Emergency Management Data Analysis Competition

ZHAO Ning, PEI Lei, LIU Dehai, LIU Ke

2023, 32(6):  1-11.  DOI: 10.12005/orms.2023.0175

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Motivation: The 20th National Congress of the Communist Party of China (CPC) has made strategic arrangements to “improve the level of public safety governance”, with one of the important tasks being to “establish a comprehensive safety and emergency framework and improve the public safety system”. Given China’s vast territory, large population, and diverse types of disasters, the task of preventing and responding to disasters is extremely demanding. The Chinese government adheres to the principle of putting people first in disaster relief work and fully leverages the institutional advantage of socialist “mobilizing resources for major undertakings”. Social rescue forces are an important complement to professional rescue forces and play a significant role in establishing a closely coordinated mechanism for emergency response to disasters. In recent years, China’s social rescue forces have continuously grown and developed, playing a crucial role in strengthening the foundation of disaster prevention, mitigation, and relief, promoting popular science knowledge, and engaging in emergency rescue operations. They have become an integral part of the emergency rescue system. However, the “14th Five-Year National Comprehensive Disaster Prevention and Mitigation Plan” (National Reduction Letter[2022]) still emphasizes that China currently faces issues such as the need to improve coordination and coordination mechanisms, the inadequacy of mechanisms such as social mobilization to adapt to new situations and requirements, and insufficient application of new technology and techniques. From the perspective of emergency management, the aforementioned issues reflect the imperfect mechanism and unclear path of social participation under the government-led disaster management model in the current stage. There is a need to optimize the management processes of social rescue organization and address issues related to the incomplete effectiveness of new technologies such as GIS and artificial intelligence. It is necessary to further strengthen organizational guidance, standardize rescue operations, enhance capabilities, and promote the greater role of social rescue forces.
Dalian Wanzhong Emergency Rescue Team was established in April 2015 and has been in operation for 8 years. Since its establishment, the team has been committed to emergency disaster relief, urgent rescue operations, social assistance, public promotion of disaster prevention and mitigation skills, and providing security for large-scale mass activities. Its goal is to promote the development of non-governmental public welfare rescue undertakings and become a professional, standardized, and standardized rescue organization covering various fields such as life rescue, humanitarian assistance, and disaster prevention. Over the years, it has become a collaborative unit of the Liaoning Provincial Public Security Department’s Patrol and Special Police Brigade, Liaoning Maritime Search and Rescue Center, Dalian Maritime Search and Assistance Center, provincial, municipal, and district emergency departments, 110 command centers, armed forces, maritime police, and border police. It undertakes emergency warehouse management services for the Jinpu New Area Development and Reform Bureau and has established cooperative relationships with several universities, including Dongbei University of Finance and Economics, Dalian Jiaotong University, and Dalian Community College. The team has basic equipment and various vehicles used for training in urban search and rescue, earthquake rescue, water rescue, mountain rescue, and pre-hospital emergency care, with a total value of over 5 million yuan. It currently has more than 3 000 active volunteers, including nearly 100 professional rescue personnel with national qualifications. Since the recording of rescue operations began in March 2018, as of May 2023, the team has carried out over 3 400 rescue operations, saving more than 300 people, and assisting in the search for nearly 2 000 missing persons.
The participation of social forces in emergency rescue under the framework of comprehensive security and emergency response has positive significance. Due to the potential negative impacts of human activities on disasters, the management of social rescue forces becomes particularly important. Firstly, the participation of social forces can expand the scope and scale of rescue resources. Not only government departments but also volunteer organizations, non-governmental organizations, businesses, and ordinary citizens can provide rescue resources and assistance, increasing the strength of rescue operations. Secondly, the participation of social forces can improve the response speed of emergency rescue. Social organizations and volunteers are often more flexible and agile, able to quickly organize actions and provide rescue and support in emergency situations, thereby shortening the response time. Finally, the participation of social forces can bring diverse professional knowledge and skills. Different organizations and individuals possess different professional backgrounds and skills, and have better acceptance of soft technologies such as GIS, artificial intelligence, and emerging equipment. They can provide a wider range and more comprehensive rescue services and support to meet various rescue needs.
In order to address the challenges of emergency data heterogeneity and lack of interdepartmental sharing of emergency business data in data-driven emergency management, and to fully leverage the role of social rescue forces in emergency response and rescue, the School of Public Administration at Dongbei University of Finance and Economics, Dalian Wanzhong Emergency Rescue Team, and the journal Operations Research and Management Science jointly initiated the “Wanzhong Cup” Emergency Management Data Analysis Competition. The goal is to promote the integration of cutting-edge theories in emergency management with practical rescue practices in China, and to leverage the advantages of universities, industrial enterprises, and journals to accelerate the development of a Chinese-style modern emergency management system and capacity building. Participants will have the opportunity to utilize the abundant emergency management data of Dalian Wanzhong Emergency Rescue Team, conduct in-depth data analysis, and reveal the patterns and trends of emergency management in specific contexts, providing scientific decision-making basis for emergency response and rescue work.
Data and Task Flow: Dalian Wanzhong Emergency Rescue Team is a nonprofit social organization, and the equipment needed for its daily operations is mainly acquired through self-raised funds. The rescue team currently provides two main services: Rescue operations and training. The information for rescue tasks comes from government departments and civilian requests for assistance. When responding to rescue tasks, the rescue team primarily mobilizes volunteers through a call-to-action approach. On the other hand, training tasks are carried out through signed training agreements with specific departments. In order to assist researchers in gaining a better understanding of the operational flow of the rescue team and analyzing potential scientific issues, the following sections will provide a detailed overview of the daily management process of the team and the available data.
Dalian Wanzhong Emergency Rescue Team is a collaborative rescue organization with a 24-hour hotline, and its daily management processes ensure efficient rescue operations. Different rescue tasks correspond to different sources of information. For example, search and rescue operations for missing persons are typically initiated by local 110 command centers or the relatives and friends of the missing individuals. Maritime rescue missions primarily originate from the Maritime Search and Rescue Center, Coast Guard, and Border Patrol. Mountain rescue operations are mainly sourced from local police stations or fire departments within the jurisdiction. The process begins with the transmission of distress signals, whereby victims report their requests for assistance either directly to the rescue team headquarters or through government agencies. Upon receiving the information, the rescue team headquarters notifies the sectional team leaders in the respective regions to ensure prompt response to the task. Subsequently, the duty secretary verifies the distress information and provides feedback to the sectional team leader. Through communication tools such as WeChat groups, the sectional team leader summons participating team members and other relevant personnel, who then assemble at designated locations before proceeding to the rescue site for the operation. Upon completion of the task, the secretary’s team assesses and archives the attendance and performance of the sectional team leader and team members.
Each step in this management process holds significant importance. The transmission of distress signals serves as the starting point for rescue operations. Timely and accurate information dissemination ensures swift response by the rescue team, thereby enhancing the efficiency and success rate of rescue missions. The verification of distress information is a crucial step to ascertain the authenticity and feasibility of the rescue operation, ensuring effective utilization of resources. The gathering of team members and relevant personnel is an essential stage to ensure organized actions by the rescue team. Efficient communication tools enhance the efficiency of information dissemination and improve communication accuracy. Lastly, the assessment and archiving process contributes to the summary of experiences and lessons learned from rescue operations, providing reference and improvement directions for future rescue work.
Dalian Wanzhong Emergency Rescue Team maintains comprehensive records of various rescue cases and task data before and after the rescue operations. The volunteer-based organization offers abundant data for research analysis. This includes nearly 5000 pieces of relevant data on rescue and training tasks from January 2020 to February 2023. Each year is represented by a separate Excel spreadsheet, and each spreadsheet is divided into six sub-tables based on the type of tasks, namely maritime rescue, urban search and rescue, mountain rescue, training support, epidemic prevention and control, and team organization and equipment maintenance. Each sub-table contains seven categories of information, including event ID, event summary, deployment time, number of deployed personnel, number of deployments, team name, and remarks. Through the analysis of this data, researchers can explore various scientific questions in depth. For example, based on the records of distress signals, researchers can study the distribution patterns of different types of disasters in different regions and the characteristics of emergency calls for help. This information can be used to formulate corresponding rescue strategies. Task assignment and scheduling records can be utilized to assess the response speed and resource utilization efficiency of the rescue team, providing references for improving the effectiveness of rescue operations. The data on team attendance and participation can be used to evaluate the enthusiasm of team members and the effectiveness of teamwork, thereby enhancing team management and training mechanisms. Additionally, the analysis of rescue resource allocation records can contribute to optimizing resource allocation and utilization, improving rescue efficiency and flexibility. The results of task execution can be used to evaluate the effectiveness of rescue command processes.
In summary, the daily management processes and provided data of Dalian Wanzhong Emergency Rescue Team offer researchers an opportunity to gain in-depth understanding of the operation of the rescue team and analyze potential scientific issues. Through the study and analysis of these processes and data, the scientific and efficient nature of rescue work can be promoted, enhancing the emergency response capabilities of the rescue team and better addressing various disaster events.
Potential Research Directions: Dalian Wanzhong Emergency Rescue Team has decided to provide detailed rescue data to support the 2023 “Wanzhong Cup” Emergency Data Analysis Competition. This article provides a detailed description of the dataset. Interested researchers can choose appropriate research methods based on their research questions and expertise. Qualitative research, econometric modeling, optimization modeling, social simulation, data-driven optimization techniques, and research on intelligent algorithms are all popular areas of study. Specifically, emergency rescue managers in the industrial sector strongly recommend researching the following issues.
Direction 1:Mechanism analysis in the context of government-led and social participation
First, the implementation pathway of disaster prevention and reduction at the grassroots level within the framework of comprehensive safety and emergency management.
With the increasing frequency of disasters and the growing demand for emergency response, it has become an urgent task to implement disaster prevention and reduction at the grassroots level within the framework of comprehensive safety and emergency management. In order to effectively address disaster risks, the research competition solicits research achievements on the pathways and approaches for disaster prevention and reduction at the grassroots level. Under this theme, participants are encouraged to explore the following aspects:
Collaboration between grassroots organizations and the government: Research on how to strengthen the collaboration between grassroots organizations and government departments, establish a collaborative working mechanism, and jointly promote disaster prevention and reduction efforts. This can include exploring the establishment of effective information sharing and communication channels, enhancing the emergency response capabilities of grassroots organizations, and the government’s support in resource allocation and guidance.
Training and capacity building for grassroots personnel: Explore how to enhance the training and skills development of grassroots personnel, enabling them to effectively organize and guide emergency response efforts during disasters. This can involve researching the development of tailored training programs for grassroots personnel, providing opportunities for practical experience exchange, and establishing training institutions and resource support systems.
Community participation and awareness enhancement: Study ways to enhance the disaster prevention and reduction awareness and engagement of community residents, encouraging them to take proactive actions when disasters occur. This can involve exploring the implementation of public education activities, organizing simulation drills, establishing community networks, and stimulating community residents’ participation and improving their emergency self-help capabilities.
Technological innovation and information support: Explore how to leverage modern technology and information tools to improve the efficiency and effectiveness of grassroots disaster prevention and reduction efforts. This can include researching the development of intelligent emergency management systems, emergency resource dispatch platforms, and disaster information platforms, as well as utilizing technologies such as big data and artificial intelligence to provide scientific decision-making support and real-time monitoring and early warning capabilities.
Second, emergency rescue task coordination between government departments and non-governmental organizations (NGOs) under goal disparities.
Under the goal disparities between non-governmental organizations (NGOs) and government departments, achieving coordination and collaboration in emergency rescue tasks is an important issue. This theme aims to solicit research achievements on the coordination of emergency rescue tasks between NGOs and government departments. The following aspects are encouraged to be explored:
Goal compatibility and cooperation mechanisms: Research on how to align the goals between NGOs and government departments and establish mechanisms for collaborative cooperation. This can involve exploring the establishment of cooperation agreements, resource and information sharing, as well as developing clear cooperation frameworks and division of responsibilities.
Role positioning and complementary advantages: Explore the role positioning and complementary advantages of NGOs and government departments in responding to disasters and emergencies. This can involve researching how to leverage the flexibility and innovative capabilities of NGOs, combined with the resource allocation and regulatory management of government departments, to form a joint force in addressing disasters and emergencies.
Communication and collaboration platforms: Research on how to establish effective communication and collaboration platforms to facilitate information exchange and coordination between NGOs and government departments. This can include considering the use of modern technological tools to establish platforms such as online collaboration platforms, social media, as well as organizing regular meetings, workshops, and other forms of communication and cooperation.
Legal regulations and policy support: Explore how to improve relevant laws, regulations, and policies to support the coordination of tasks between NGOs and government departments. This can involve researching the development of clear policy guidance, providing preferential policies and funding support, as well as strengthening legal protection and regulatory measures to promote cooperation between both parties, and safeguard corresponding rights and interests.
Direction 2: Optimization research on the operation mechanism of social forces
First, the participation pathways of social rescue forces within the framework of comprehensive safety and emergency response.
Under the framework of comprehensive safety and emergency response, the participation of social rescue forces is crucial. To promote the effective participation of social rescue forces, the scientific research and innovation competition is soliciting research results related to this theme. Under this theme, we encourage participants to explore the following aspects:
Cultivation and development of social rescue organizations: Research on how to cultivate and develop social rescue organizations, including non-governmental organizations, volunteer organizations, and corporate social responsibility projects. Consider providing training and guidance to stimulate the enthusiasm and professionalism of social rescue forces, as well as establishing cooperative networks and resource support systems for social rescue organizations.
Interdepartmental cooperation and coordination mechanisms: Explore how to establish mechanisms for interdepartmental cooperation and coordination, enabling social rescue forces to collaborate effectively with government departments, public safety agencies, and other relevant organizations. Research the formulation of cooperative agreements, mechanisms for information sharing and resource allocation to facilitate cross-sectoral cooperation and coordination.
Technological innovation and application: Study how to enhance the response speed and efficiency of social rescue forces through the use of modern technologies. Explore the application of technologies such as unmanned aerial vehicles, artificial intelligence, and big data to provide real-time disaster information and warnings, supporting rescue decision-making and resource allocation.
Social participation and public awareness: Explore ways to enhance public awareness and participation in social rescue forces, thereby improving the overall capacity of social rescue. Consider conducting public awareness campaigns, organizing public visits to rescue exercises, and sharing case studies to inspire public social responsibility and participation.
Second, optimization of the operational models of social welfare and rescue organizations in the Internet era.
In the Internet era, the operational models of social welfare and rescue organizations need to be optimized and innovated. To promote the development of social welfare and rescue organizations, the scientific research and innovation competition is soliciting research results related to this theme. Under this theme, we encourage participants to explore the following aspects:
Social media and public participation: Research how to utilize social media platforms and Internet tools to facilitate interaction and engagement between social welfare and rescue organizations and the public. Explore the establishment of online volunteer recruitment platforms, crowdfunding platforms for social rescue projects, and utilize social media for promotion and information dissemination.
Organizational structure and operational model innovation: Explore how to optimize the organizational structure and operational model of social welfare and rescue organizations to enhance their efficiency and sustainable development capabilities. Research the adoption of flat management structures, the introduction of specialized operational teams, and the establishment of partnerships with other charitable organizations to enhance organizational flexibility and professionalism.
Data management and decision support: Study how to utilize data management and analysis technologies to provide decision support and strategic planning. Explore the establishment of information-based data management systems to collect and analyze data relevant to social welfare and rescue, providing scientific decision-making basis for optimizing resource allocation and project management.
Risk management and supervision mechanisms: Discuss how to establish sound risk management and supervision mechanisms to enhance the transparency and trustworthiness of social welfare and rescue organizations. Research the establishment of assessment and monitoring systems, strengthen supervision and evaluation of rescue projects and the use of donations to safeguard the rights and interests of the public and ensure the effective utilization of donations.
Direction 3: The paradigm-shifting of emergency rescue driven by new technologies
First, emergency data governance: openness and sharing of government data and resources.
The openness and sharing of government data and resources play a crucial role in emergency data governance. To promote innovation and development in emergency data governance, the research and innovation competition is seeking research outcomes related to this topic. Under this theme, participants are encouraged to explore the following aspects:
Mechanisms for data openness and sharing: Research how to establish mechanisms for government data openness and sharing to facilitate data sharing and collaboration among different departments. This may involve establishing unified data standards and interfaces to promote data interoperability while ensuring data privacy and security.
Multi-source heterogeneous data fusion and big data analysis: It is necessary to explore how to integrate the multi-source heterogeneous data emerging in the process of emergency rescue, and use data analysis technology to extract valuable information and management insights from the “infodemic”. Research on establishing an integrated platform for emergency data that enables real-time data collection, storage, and analysis to provide scientific basis for emergency decision-making.
Data governance and policy regulations: Study how to establish a sound data governance system and develop relevant policies and regulations to promote compliant and effective data usage and management. Consider the government to promote the establishment of a reward mechanism for data sharing and openness, and clarify the rights and responsibilities of data.
Data application and innovation in emergency rescue scenarios: Explore how to effectively apply government data to all aspects of emergency rescue, and promote the intelligence and precision of emergency decision-making and resource allocation. Research and develop a data-driven emergency management information system for specific emergency rescue scenarios, provide real-time disaster/public opinion monitoring and early warning, and optimize the allocation of emergency resources and response capabilities.
Second, optimization of measures for tracking and search of missing persons in the data-driven context.
Data-driven smart emergency management enables more accurate tracking and search operations for missing persons. The Emergency Rescue Team has been involved in search and rescue operations for Alzheimer’s patients and runaway teenagers, accumulating a wealth of cases and data. In order to enhance the intelligence level of the search and rescue work of missing persons, participants are encouraged to explore the following topics worthy of further study.
Data-driven tracking of missing persons: Research how data technologies such as GPS positioning, facial recognition, and mobile communications can enhance the localization and tracking capabilities of missing persons. Discuss the establishment of real-time databases and tracking systems for missing persons to provide fast and accurate location information.
Multi-party cooperation mechanism and resource integration platform: Explore how to realize cross-departmental and cross-organizational cooperation mechanisms and resource integration platforms to improve the efficiency and coverage of search and rescue for missing persons. Research on the establishment of an information cooperation platform for search and rescue of missing persons, and promote the organic collaboration of police, volunteers, communities and other forces.
Early warning mechanisms and emergency response: Study how to establish early warning mechanisms for identifying missing person risks in advance and implementing rapid and effective emergency response measures. Explore the establishment of intelligent warning systems that leverage artificial intelligence and big data technologies to provide accurate prediction and warning information about missing person risks.
Information dissemination and public participation: Explore how to expand the coverage and resources for searching and rescuing missing persons through information dissemination channels and public participation. Research the establishment of platforms for publishing missing person information and strengthen cooperation with media and social media to increase public awareness and participation in search and rescue efforts for missing persons.
Third, optimization of urban emergency medical resource allocation based on GIS.
With the support of Geographic Information Systems (GIS), the optimization of urban emergency medical resource allocation can be improved. To promote the optimization of urban emergency medical resource allocation, the research and innovation competition is seeking research outcomes related to this topic.
Spatial layout of emergency medical resources: Research how to use GIS technology to strategically allocate urban emergency medical resources, expanding their coverage and ensuring mutual coordination. Explore the use of GIS analysis methods to determine the most reasonable location of emergency centers, ambulance stations, AED devices, and other facilities to optimize resource allocation and distribution.
Traffic network and emergency route planning: Explore how GIS technology can be used to optimize emergency route planning for urban emergency medical resources. Research on route navigation systems based on real-time traffic information to provide the shortest time and optimal routes, thereby improving response times of emergency medical resources.
Spatial data analysis and resource scheduling: Study how to use GIS technology to perform spatial data analysis and resource scheduling for urban emergency medical resources. Explore the establishment of real-time resource scheduling systems that allocate and dispatch emergency medical resources based on the spatial distribution and demand of disasters and emergencies.
Public participation and information sharing: Explore how public participation and information sharing can enhance the effectiveness of optimizing urban emergency medical resource allocation. Research the establishment of interactive platforms for public reporting and seeking assistance, promoting close connections between the public and emergency medical resources, and improving resource utilization efficiency.
Through these research outcomes, we hope to promote emergency data governance through government data and resource openness and sharing, optimize measures for tracking and searching missing persons under data-driven approaches, and optimize the allocation of urban emergency medical resources driven by GIS. These contributions will provide innovative ideas and practical methods for the development of emergency management and public safety fields. Those interested in participating in the competition should contact the Secretariat of Higher Education Administration Branch, China Double-method Society, scopehe@163.com. Application deadline is September 1, 2023.
Research Objectives: The goal of this competition is to promote the deep cooperation between the theoretical research of emergency rescue and the practical needs of the industry based on the rich data of emergency rescue practice. Participants will have the opportunity to use the emergency rescue data of Dalian Wanzhong Emergency Rescue Team to conduct in-depth data analysis and mining in order to reveal the rules and trends and provide scientific decision-making basis for emergency rescue work. The joint organization of the School of Public Administration at Dongbei University of Finance and Economics, Dalian Wanzhong Emergency Rescue Team, and the journal editorial department of Operations Research and Management Science brings together the comprehensive advantages of university education, practical experience and academic research to this competition. Universities, as educational and research institutions, can provide theoretical guidance and academic support. Emergency enterprises or social organizations, as emergency rescue practitioners, can provide actual data and cases to strengthen the connection between theory and practice. Academic journals, as platforms for knowledge spreading and academic exchanges, can facilitate the publication and communication of academic achievements. Through this competition, not only can promote the research and innovation in the field of emergency management, but also promote the deep cooperation between academia, universities and social organizations, and jointly promote the digital intelligence of emergency management and the modernization of social governance. At the same time, this provides researchers with an opportunity to understand the operational processes and management models of Dalian Wanzhong Emergency Rescue Team, conduct in-depth analysis of potential scientific issues, and provide valuable references and suggestions for enhancing emergency rescue capabilities and efficiency. Researchers, university teachers, graduate students and emergency rescue practitioners are welcome to actively participate in this scientific research competition, jointly promote the practical development and theoretical innovation in the field of emergency management, accelerate the construction of a safer, more sustainable and more intelligent Chinese modern emergency management system, and ensure a new development pattern with a new security pattern.

Theory Analysis and Methodology Study

Multi-objective Location Optimization of VTS Radar Station Based on Gradual Coverage

HUANG Chuan, LYU Jing, AI Yunfei, ZHU Xuebin

2023, 32(6):  12-19.  DOI: 10.12005/orms.2023.0176

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Vessel Traffic Service (VTS) is a complicated facility system to enhance the safety level of waterway vessel traffic, improve traffic efficiency and protect the water area environment. The principle of its role is to use communication facilities such as radar stations, automatic ship identification system base stations, VHF calls and shipboard terminals to realize the monitoring of ships in the supervised water area and to provide ships with the navigation safety information required for navigation to ensure the safety of vessel navigation. Since its introduction into China, it has been playing an important role in guaranteeing vessel traffic safety, improving efficiency of waterway vessel traffic.
As an important tool for the maritime department to achieve waterway traffic safety management, its construction and use can effectively maintain the stability of the waterway traffic safety situation, improve the efficiency of waterway navigation, and promote the good and rapid development of the shipping economy. In accordance with the general requirements of China’s construction of a strong transportation country and a strong maritime country, as well as the Ministry of Transportation and Communications Maritime Bureau to promote the construction of ‘land, sea, air and sky’ integrated waterway transportation safety assurance system, and at the same time to meet the new waterway supervision needs caused by the large-scale ship and the growing flow of ship traffic, the competent maritime authorities need to build a large number of VTS systems to meet the regulatory needs, to enhance the supervision, management, emergency security capabilities and abilities of China’s jurisdictional water area, international water area. Among them, VTS radar station, as the core component of VTS system, plays a crucial role in monitoring the waterway under navigation, and its location deployment and radar configuration affect the function of the whole system, so it is necessary to study the location selection and radar configuration of VTS radar station.
In view of the current VTS radar station location optimization study, which does not consider the actual number of proposed radar stations and the attenuation of radar radio wave propagation in the air, the attenuation function measurement mechanism is introduced. And the bi-objective location optimization model of VTS radar station is proposed based on the idea of gradual coverage in terms of station construction cost and coverage rate with consideration of radar configuration, and the adaptive weight particle swarm algorithm is designed to solve the model. The results show that the final solution can achieve the optimal location program under the condition of satisfying the constraints, which provides a solution idea for the location optimization of VTS radar stations.
However, the location selection of VTS radar stations in the actual environment is a systematic project that requires consideration of many factors, so the location optimization can be decided from several differential factors, such as the density of ship navigation routes, etc. Meanwhile, the algorithm used in this paper can be further improved to improve the accuracy and scientificity of the solution method.

Automatic Vertiport Location of Unmanned Aerial Vehicle Based on Partition Optimization

REN Xinhui, WANG Liu, WANG Jiaxue

2023, 32(6):  20-26.  DOI: 10.12005/orms.2023.0177

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With the flourishing development of e-commerce and new retail, the demand for logistics and delivery has been continuously increasing. With the improvement of technology and policies, UAV urban delivery will become a trend in future development. Currently, it is being promoted and applied in cities such as Shenzhen and Hangzhou for express delivery and medical blood sample delivery, etc. With the special safety requirements in the background of the COVID-19 pandemic, the concept of “contactless delivery” has emerged. The fully automated vertiport is a completely contactless service, and UAV delivery expands the delivery range of instant delivery, improves delivery speed, and innovates urban delivery modes.
This paper studies the location selection problem of UAV urban delivery. Though the method of improved grid partitioning and the Voronoi polygon, optimizing the partition vertiport service area and the distribution of demand points is more balanced, and the goal of global optimization is achieved.
Firstly, this study identifies the UAV delivery operational scenarios and constructs a vertiport location model. The study focuses on automatic vertiport (Robort Hub) of Hangzhou Xunyi Network Technology Co., Ltd., which provides ground infrastructure supporting UAV vertical takeoff, landing, parking, charging, and maintenance. The location model for the vertiport is constructed by considering the factors affecting logistics UAV urban operations, the need for vertiport coverage the whole area, and the distance from the merchants to the vertiports. And the objective is to minimize the total operating time of the UAV vertiport delivery mode.
Next, the study of UAV urban delivery partition and vertiport location is conducted in Heping District of Tianjin City as an example. First, by crawling the block data of Heping District’s catering merchants and using the clip tool in ArcMap to clip the merchant data points, 2190 pieces of catering merchant data in Heping District are obtained. Second, based on the actual data of the merchants, an improved grid partition method is used to partition the demand point area. The Counting Cliques algorithm is used to find out the location of vertiport. Since the improved grid method may result in demand points being too concentrated in the partitioned areas and demand points in adjacent grid areas being closer to other area vertiports, the study proposes the use of the Voronoi polygon method to redistribute the demand points in each grid to ensure that the distance from the demand points within the area to the vertiports is minimized, and the distribution of demand points is more balanced, achieving the global optimal solution. Furthermore, the possibility of peak congestion during delivery is considered, and the “anti-mobility” index is analyzed to determine whether to increase the number of vertiports in a given area.
Finally, though the application of the model and the partitioning methods, the results of optimal partition and vertiport location in Heping District of Tianjin is obtained.
Using an improved grid partitioning method, the demands of merchants in the Heping District are partitioned into 74 effective grid partitions. Based on the location model, the vertiport location is determined. By using the Voronoi polygon method, demand points are reassigned to achieve a more even distribution. After analyzing the “anti-mobility” index, it is found that adding new UAV vertiports in areas with higher metric values would be beneficial and could accelerate logistics speed.
The partition optimization method allows for a more reasonable service area for the siting of UAV vertiport, this method is also applicable to other urban areas suitable for UAV delivery, providing a basis for ground facilities and equipment support for Urban Air Mobility (UAM) systems.
In this study, the service area for siting UAV vertiports is optimized. Although the problem of local optimization has been solved, when determining the final location of the UAV vertiport, it is necessary to further consider factors such as obstacles, building heights, distance from buildings, and energy consumption of UAVs in the actual environment. In addition, the impact of actual demand of urban UAV delivery on the location and number of vertiports should be explored.

Practical Logic and Risk Response of Artificial Intelligence Embedded in Social Security Governance

SHI Tuo, ZHANG Junhui, LI Danyang, SONG Zhiyang

2023, 32(6):  27-32.  DOI: 10.12005/orms.2023.0178

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As artificial intelligence becomes increasingly ingrained in the realm of public security and governance, its practical application transcends unidimensional technological innovation. It now encompasses the development of a multi-stakeholder collaborative governance model, compels the upgrading of supporting public security systems, and drives the transition of governance methods towards more technologically advanced means. Simultaneously, the rapid advancement of intelligent technologies has resulted in structural imbalances among governance actors, the inadequacy of governance systems, and technological alienation, giving rise to risks such as social inequality, disarray within governance systems, and an overreliance on technology. Therefore, it is crucial to elucidate the evolving interplay among various actors in the context of artificial intelligence embedded in public security governance, while strengthening forward-looking prevention, constraint guidance, and formulating effective risk mitigation strategies. This approach is of both theoretical and practical value for harnessing artificial intelligence to enhance public security management more effectively.
This article establishes an evolutionary game model involving three key stakeholders: Government departments (represented by public security agencies), artificial intelligence companies, and the general public. And then it analyzes behavior strategies of the tripartite stakeholders in the scenario of artificial intelligence embedded in social security governance. By examining the strategic combinations chosen by these three interest groups, a game payoff matrix is constructed. Through the analysis of three-party replicator dynamic equations, the equilibrium points and stability of the evolutionary game among the three parties are determined.
The findings indicate that government departments, exemplified by public security agencies, must proactively coordinate the structural relationships among various stakeholders in the realm of artificial intelligence integration. It is crucial to foster a collaborative governance framework involving multiple entities, enhance and refine supporting systems, and expedite the development of an AI technology governance structure centered on “social good governance”. This approach will comprehensively facilitate the positive progression of AI integration within social security management.In particular, government departments represented by public security agencies need to take the initiative to comprehensively coordinate the structural relationship among multiple subjects under the embedding of artificial intelligence, promote the formation of a multi-subject collaborative governance pattern, follow up and improve supporting systems, and accelerate the construction of artificial intelligence for “good social governance”. The intelligent technology governance system can comprehensively promote the benign development of artificial intelligence technology embedded in social security governance.
This paper delves into the optimal risk response strategy within the evolutionary game of the interests of three key stakeholders, beginning with the logic of security management and adopting a more comprehensive and systematic perspective. Nevertheless, there exist certain research limitations. Future studies will further deepen the focus in two areas: First, incorporating relevant regulatory authorities to establish a four-party evolutionary game model, in order to develop more effective multi-stakeholder governance strategies; Second, investigating the creation of a more practical game payoff matrix, offering superior solutions for boosting the technical security and stakeholder collaboration of AI integration in social security management.

Multi-stage Emergency Materials Scheduling Based on Multimodal Transportation under Uncertainty

YUAN Ruiping, WANG Wei, LI Juntao, ZHAO Qi

2023, 32(6):  33-39.  DOI: 10.12005/orms.2023.0179

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Sudden natural disastersare inevitable, but usually cannot be predicted in time, so emergency rescue work after disasters is particularly important. A feasible scheduling scheme is the key to ensuring the timely supply of emergency rescue materials. Scholars have done a lot of research and analysis on the scheduling of emergency materials during the emergency rescue period, and some literature has considered the phases of emergency rescue. However, the division of stages is not detailed enough to fully integrate the characteristics of each stage for material scheduling. And there is less consideration given to the issue of material transportation caused by the inability of emergency supplies to meet the needs of the affected areas in the later stages of emergency rescue.
According to the characteristics and priorities of each stage of the disaster emergency rescue period, considering the uncertain factors such as material demand and road damage,this paper proposes a multi-stage multimodal transportation scheduling framework of emergency materials. In the first stage, considering the strong timeliness of emergency rescue work, minimizing emergency material allocation time is taken as the optimization goal. In the second stage, material allocation time and satisfaction is taken as the optimization goal. In the third stage, considering the material dispatch demand, material allocation time, cost and satisfaction is taken as the optimization goal. Taking the number and load capacity of transportation vehicles, minimum demand satisfaction rate and transportation network damage as model constraints, the multimodal transportation scheduling model of emergency materials at each stage is constructed respectively. According to the characteristics of these model, a heuristic hybrid solution algorithm combining genetic algorithm and simulated annealing is proposed, which introduces the retention strategy of non-optimal solution of simulated annealing into the genetic algorithm, so that the evolved subpopulation and the potential excellent individuals in their neighborhood are combined again. It can not only further enhance the local search ability of the algorithm, but also maintain the strong global search characteristics of the genetic algorithm.
Taking the emergency rescue of Ya’an earthquake as an example, the effectiveness of the models and algorithm is verified. The algorithm iteration diagram for solving each stage model shows that the algorithm proposed in this paper can quickly converge to obtain scheduling results, including the multimodal transportation scheduling scheme considering the damage of the transportation network in the first and second stages, and the dispatch scheme from the dispatch areas to the rescue areas and the scheduling scheme from the rescue areas to the disaster areas in the third stage.
The innovation of this paper lies in: (1)According to the characteristics and work priorities of each stage of the emergency rescue period, a multimodal transportation dispatching model based on multi-stage is proposed, considering uncertainties such as material transportation needs and road damage ; (2)A heuristic hybrid solution algorithm combining genetic algorithm and simulated annealing algorithm is proposed according to the characteristics of the multi-stage emergency material dispatching model, and the effectiveness of the model and algorithm is verified by a real case.

Retrospective Analysis of the Demand of Emergency Supplies Based on SEIRD Dynamic Model: Case Study by Taking COVID-19 Epidemic in Wuhan as An Example

SHAN Zidan, SHENG Chenhui, HAN Xiangyu, HOU Cheng

2023, 32(6):  40-45.  DOI: 10.12005/orms.2023.0180

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At present, the prevention and control of major outbreaks has caused a huge impact on the public system, and the characteristics of sudden, emergency, diffusion and high uncertainty are becoming more and more prominent, and the emergency management problems of major outbreaks need to be solved urgently. For example, during the COVID-19 epidemic, the shortage of protective materials, the imbalance of material distribution, and the asymmetry of supply and demand of emergency supplies have caused supply and deployment of epidemic prevention materials to be seriously insufficient. Therefore, in order to accurately respond to the surge of material demand in major disasters, the balanced distribution and cross-regional deployment of materials are extremely important. Based on this research background, the SIR model is established in this study, and the model parameter combination is obtained through software simulation. On this basis, the dynamic data set of the material demand subject is established, and then the supply and demand quantity of emergency supplies is determined according to the quantity of research samples. At present, a large number of scholars at home and abroad have predicted and analyzed the demand for emergency supplies in emergencies such as the COVID-19 epidemic. For example, some scholars use ARIMA and adaptive filtering methods to predict the demand of medical services. In addition, another study has established an adaptive evolutionary support vector regression model to predict post-earthquake emergency blood demand. On this basis, scholars propose an improved GM(1,1) model to predict the demand of emergency supplies after flood disaster, or use CBR technology to predict the demand of emergency supplies after the earthquake. The case reasoning technology needs to find the appropriate source case to achieve more accurate prediction; The gray prediction model can be predicted by a small amount of incomplete information. But the major outbreak is different from all previous kinds of infectious diseases, in which the information contains high uncertainty and complexity, so the existing research analysis model is lack of applicability. Considering the COVID-19 as an infectious disease, the infectious disease system dynamics, and its obvious difference from previous similar diseases, the improved dynamic analysis model is used. According to previous studies, many scholars have used the infectious disease model to study the development trend of the epidemic situation, and few scholars have linked it to the demand for emergency supplies.
In view of this, in order to more accurately describe the demand for emergency supplies during the Wuhan epidemic, the material needs are described according to the national classification standards, and the traditional model is improved according to the true spread of the epidemic. Wuhan COVID-19 epidemic history data are collected through COVID by parameter calibration and parameter comparison experiments. In this study, the traditional infectious disease model is modified and set innovatively on this basis: (1)According to the notice of the diagnosis and treatment plan issued by the National Health Commission, the incubation period is infectious, and the isolation observation period, incubation period and average disease period are set. (2)The population during the incubation period is redefined. The incubation period includes two types of isolation and unisolated observation, which are divided into carrying and not carrying the virus. The virus population will become the key groups of the spread of the epidemic due to the lack of isolation measures. (3)The cured patients will not be transmitted by the existing patients (the possibility of positive recovery is very low). (4)Inpatients are treated in a strictly controlled isolation ward, depending on the part of the population that is unable to infect susceptible patients. The results show that: (1)The change in the number of contacts, infection rate, detection rate, virus proportion and other indicators will directly affect the number of people infected by major epidemics. (2)Changes in the number of contacts, infection rate, discovery rate and other indicators will have an indirect impact on material demand. (3)During the epidemic period, China has adopted the strictest control measures such as urban and family quarantine to effectively reduce the spread range of the epidemic, and improve the detection rate with big data and other technologies. The parameter comparison experiments effectively verify that these measures are beneficial to relieving the huge supply pressure of emergency supplies. By establishing a dynamic model system of infectious diseases in the epidemic period, the inevitable connection between material demand and epidemic prevention and control is effectively verified, and the purpose of dispatching cross-regional emergency supplies and accurately matching supply and demand is achieved through analysis, providing an important reference for dealing with major disasters such as similar epidemics. On the basis of balancing the supply and demand of emergency supplies during the epidemic period, constructing a dynamic model of infectious diseases and the demand analysis model of emergency supplies can further broaden the path of dealing with the demand of similar emergencies, so as to provide important theoretical reference for similar emergencies. In addition, there are still some research limitations in this study, that is, the deployment of materials among specific regions after the end of the outbreak needs further research.

Multiple Preemptive Project Scheduling Optimization Based on Time Window Delay Scheme

WANG Min, ZHANG Zhuanxia

2023, 32(6):  46-52.  DOI: 10.12005/orms.2023.0181

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With the development of economy, the projects of enterprises are generally large, diversified and complicated. And there are many uncertainties accompanying the project execution process. In many actual projects, the execution of activities is often interrupted due to uncertainties. Activity interruption can change the state of resources, execution duration of the activity and the logical relationship between activities, and how to schedule the project after activity interruption has become a new topic, gradually forming a preemptive resource constrained project scheduling problem, namely PRCPSP.
However, most of the existing studies on PRCPSP are based on the assumption that the number of preemptions is one or more at fixed nodes, which is not entirely consistent with the realistic background. With the changing complexity of the environment, the execution of projects is becoming increasingly complex, especially for large and emergency projects, where there are more uncertain factors. Random multiple preemptions have become an effective way to alleviate resource conflicts and shorten project time. In this context, studying project scheduling with random preemption has become particularly important. Therefore, based on the existing research, this paper studies the stochastic preemptive project scheduling problem in uncertain environments, constructs a model of preemptive resource-constrained project scheduling problem in which any activity is allowed to be interrupted at any time node, and designs a heuristic algorithm with time window delay scheme, which provides a new approach to solving stochastic preemption problem.
In the algorithm, the transformation rule of active network graph is first introduced, and then, based on the depth-first and breadth-first solution strategies, a scheduling generation scheme based on time-window delay scheme is produced. By invoking the PSPLIB (Project scheduling problem library) datasets, setting different parameters, and designing several groups of computational experiments. The computational results show that, compared with the non-preemption problem, the algorithm performs better in solving the project scheduling problems with preemption, especially for large-scale projects, which verifies the effectiveness of the algorithm. Moreover, when other parameters are constant, with the increase of project network complexity (NC), resource constraint degree (RC), and resource coefficient (RF), and compared to non-preemptive resource-constrained project scheduling problem, this algorithm obtains better project duration in finite time. At the same time, compared with the basic exact algorithm, this algorithm is more efficient. The solutions can provide support for the decisions of actual project scheduling.
However, in this study, it is assumed that execution after activity interruption does not consume costs and additional resources. However, considering the complexity of the actual project, how to consider the cost and resource consumption of re-execution after activity interruption in stochastic preemptive project scheduling is worthy of further study. At the same time, it is worth further exploring how to effectively embed the heuristic algorithm designed in this paper into the meta heuristic algorithm and design efficient algorithms suitable for more complex problems.

Study on the Optimization of Emergency Materials in Disaster Rescue Based on User Equilibrium

FENG Chun, FENG Yujie, LUO Mao, YU Bao

2023, 32(6):  53-60.  DOI: 10.12005/orms.2023.0182

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For the past years, large-scale natural disasters and emergencies have occurred frequently. When a large-scale disaster (e.g., earthquake or tsunami) occurs, it usually results in a large number of casualties and destroys infrastructure such as power, transportation and communications. For example, the 2008 Sichuan earthquake devastated 273 areas, killing 235,000 people and affecting 214 million people. After the occurrence of large-scale disaster events, the demand for emergency relief materials in the affected areas has surged, especially for food, medicine, rescue equipment, and other materials, leading to an oversupply of resources in a short period of time. In view of such phenomena, this paper studies the question of how rescue organizations can optimize the allocation of emergency materials under the condition of limited resources and time, so as to improve the efficiency of emergency rescue, meet the needs of disaster victims to the greatest extent, and maintain stability in disaster areas.In this paper, we change the perspective of the emergency supplies distribution problem, and transforms the “top-down” emergency material distribution problem into “bottom-up” disaster victims’ demand for emergency materials from the perspective of the difference in the needs of disaster victims, and in the case of given emergency material distribution center, the disaster victims consider their road travel costs and expectations to meet the demand costs, and construct an emergency material distribution model based on user balance, reflecting China’s “people-oriented” rescue purpose, thereby alleviating the psychological panic of disaster victims. The model has certain theoretical and practical significance for humanitarian-based emergency relief.
The sudden disaster usually generates a large amount of material demand instantaneously, and how to allocate the limited emergency materials scientifically and reasonably is the key to meeting the survival needs and recovery and development of disaster victims in disaster areas. In order to solve this problem, this paper constructs an emergency material distribution model based on user equilibrium from the perspective of the differences in the needs of disaster victims. The model optimizes the “bottom-up” distribution network of emergency supplies, in which the victims make decisions that maximize their own interests, and finally all the victims form an equilibrium state. According to the characteristics of the model, based on the Frank-Wolfe algorithm, each disaster victim is the most satisfied with the emergency material distribution strategy in the equilibrium state.In the end, taking the 2013 Sichuan Ya’an Lushan earthquake as the research background, the geographical network of the severely affected Lushan County and its surrounding disaster areas is selected as the transportation network for the arithmetic analysis, the townships with serious disasters and large populations are selected as the disaster areas(PC)for the study analysis, Ya’an City, Hanchuan County and Tianquan County are selected as the emergency material distribution centers(DC)of the study case, and the validity of the model and algorithm is verified by numerical arithmetic examples.
The results of the study show that when the cost per unit of unmet need is small, if the emphasis on meeting the needs of the victims is reduced, the decision making of the victims will be more volatile, which is contrary to the principle of “people-oriented” relief in China. As the cost per unit of unmet need gradually increases, the study finds that the number of unassisted victims gradually decreases and the unmet need of the victims gradually decreases.In a nutshell, if the level of attention to the unmet needs of victims for supplies is increased during the relief process, the effectiveness of the distribution of emergency supplies can be improved, thus the number of unassisted victims can be reduced and the role of relief centers can be fully utilized.
This paper studies the problems related to the distribution of emergency supplies from the perspective of disaster victims, but there are still many tasks that need further improvement, including: (1)The assumptions in the model need further deepening research. For example, this paper assumes that the disaster victims have equal demand for emergency supplies, and the subsequent research can be considered for situations such as unequal demand, uncertain demand, and diversity of demand types, so that the model is closer to the real relief situation. (2)The research on the distribution of emergency supplies mainly focuses on the solution of the distribution results, but does not consider the vehicle path problem in the distribution process. Further research can combine the user equilibrium theory and the vehicle path problem, so as to enrich the model at the level of emergency relief operation.

Task Allocation Optimization of Robotic Mobile Fulfillment System Considering Dynamic Task Cost and Seeding Wall Capacity

ZHANG Jingtian, HU Xiao, WENG Xun, MA Ying, YU Xiao

2023, 32(6):  61-67.  DOI: 10.12005/orms.2023.0183

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In the era of “Industry 4.0”, robot technology has become an important method to assist the transformation and upgrading of labor-intensive industries. For the past years, the ministry of industry and information technology has pointed out that, it is necessary to build intelligent logistics systems with a focus on robots and improve the digital level of commercial logistics. Robotic mobile fulfillment system (RMFS) is a new type of “goods to picker” sorting system based on robot technology. Due to its high flexibility, high storage density, high efficiency, and high responsiveness, RMFS is widely used in industries such as e-commerce, retail supermarkets, pharmaceuticals, and has become a research hotspot for scholars. Task allocation is a key decision-making problem that affects the efficiency of RMFS. The task allocation problem of RMFS is to decompose a batch of orders into a set of transportation tasks and then allocate them to a group of robots according to certain rules. At present, the researches on the task allocation problem of RMFS mainly focus on innovation in model transformation and algorithm optimization, ignoring the gap between the model itself and the actual system. These issues include: 1)For business scenarios with differentiated customer rating characteristics, there are differences in timeliness and importance between customer orders, and distribution centers need to prioritize orders with high customer ratings. Therefore, it is necessary to introduce customer order priority constraints into mathematical models. 2)The acceleration, deceleration, turning, lifting shelves, queuing and waiting of robots during task execution can bring significant time cost which dynamically changes with different task allocation methods. Existing literatures often use Manhattan distance or Euclidean distance when calculating make span, without considering the impact of dynamic task cost, resulting in significant deviation from reality. 3)In the actual operation process, the seeding wall capacity in the picking station is limited, and it is one of the key parameters that restrict the efficiency of the picking station. However, most studies ignore the seeding wall capacity when modeling.
Based on the above issues, this article investigates the task allocation problem of RMFS that considers dynamic task cost and limited seeding wall capacity. A centralized task allocation method based on a hybrid heuristic algorithm for business scenarios with differentiated customer rating characteristics is proposed.First of all, a task allocation optimization model with the goal of minimizing the maximum make span is established under the constraint of customer order priority. Secondly, considering the dynamic task cost caused by acceleration, deceleration, turning, lifting shelves, queuing and waiting of robots during the task execution and the constraint of the seeding wall capacity, the maximum make span generation scheme is designed. The generation scheme dynamically corrects the cost of sub tasks under the given task allocation method, and determines whether the task allocation method meets the constraint of the seeding wall capacity.Then, the memory elite population based catastrophe adaptive large neighborhood search algorithm (MEPCALNS) is proposed to solve the optimization model, which improves the search depth and efficiency of the traditional adaptive large neighborhood search algorithm (ALNS). On the basis of ALNS, MEPCALNS dynamically maintains a search population based on catastrophe operator and an elite population with memory. The search population uses ALNS for neighborhood search, and actively abandons continuously unimproved solutions through catastrophe operators to prevent falling into local optima. The elite population adopts a probability-based update mechanism to preserve the high-quality solutions generated by the search population, compensating for the memoryless nature of the ALNS search process. By using catastrophe strategies, these high-quality solutions can re-enter the search population and search in new directions, which improves the search depth of ALNS.Finally, numerical experiments are conducted to demonstrate the effectiveness and stability of the algorithm.
MEPCALNS is compared with dispatch rules method and ALNS, and each heuristic algorithm is independently run 10 times to obtain the best result. The experimental results show that ALNS and MEPCALNS outperform dispatch rules method in all cases. Therefore, the dispatch rules method is used as a benchmark to discuss the optimization capabilities of the two algorithms. For all cases, the optimization performance of MEPCALNS is superior to ALNS with an average increase of 3.3%. Among them, the improvement effect is most significant at RAND3-3, with an increase of 5.27%. Also, the experimental comparative analysis has demonstrated that the characteristics of dynamic task cost and the constraints of seeding wall capacity are essential for task allocation problems.
This article does not consider conflicts and deadlocks between robots when designing the maximum make span generation scheme. In practice, conflicts and deadlocks may occur when the number of robots exceeds a certain value leading to an increase in task cost, resulting in a deviation between problem modeling and the actual situation. Therefore, incorporating conflicts and deadlocks into the maximum make span generation scheme to make the model more practical is the focus of future research.

A Periodic Home Health Care Routing and Scheduling Problem with the Consideration of Patient Preference on Time Windows

XIANG Ting, LI Yanfeng, XU Guoxun

2023, 32(6):  68-74.  DOI: 10.12005/orms.2023.0184

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The decline in fertility and the increase in average life expectancy have led to the rapid aging of the global population. In 2019, the proportion of the global people aged 65 and older reached 11%, and it is expected to increase to 16% by 2050. China is one of the countries with the most serious aging population. The results of the 7th Chinese census in 2020 show that the population aged 65 and older accounted for 13.5%, which is close to the deep aging level of 14%.To improve the community pension system and meet the demands of most patients for long-term and continuous health care services in China, family doctor contract services are developed by the government at the grassroots level. In this service, doctors can provide medical services to patients at their home, such as medical tests, wound care, therapy services, and care visits. It is especially suitable for the “key population” such as elderly, pregnant women, and patients with chronic diseases.It has become an important way to safeguard people’s health in China.
Home health care routing and scheduling problem (HHCRSP) is constructing routes for doctors to provide the patients’ services, which directly determines the scheduling activities and is generally modeled as an extension of the vehicle routing problem. According to the scheduling time horizon, HHCRSP can be categorized as either a single period (such as a single day) optimization problem or a multi-period (such as a week or a month) optimization problem. By analyzing related existing studies, it can be found that: (1) Most existing studies have focused on the single-period HHCRSP,while few studies have related to periodic scheduling problem. (2) Most existing studies assume that patients’ service days are fixed. However, in practice, patients are not willing toreceive service on each day, and every patient may have different preference satisfaction for the different days. (3) In the multi-period HHCRSPs, most studies have considered the patients’ preference satisfaction from the service continuity, which are captured by minimizing the total number of different doctors visiting the same patient during the planning horizon, or minimizing the relative frequency of patients receiving services between the current and last visit. No research has considered patients’ preference satisfaction from the perspective of service continuity between different days. Therefore, this paper introduces a periodic HHCRSP with the consideration of patient preference on time windows. The main contributions are as follows:
First, this paper assumes patients have fixed service frequencies but have different preference satisfaction for different time windows to accept services. By considering constraints about the skill requirements of patients, time windows, patients’ service date regulations and doctors’ working regulations, the problem is formulated as a mixed-integer linear programming model to minimize the total travel costs and maximize the patients’ preference satisfaction.The patients’ preference satisfaction captures the served time window, and service continuity between different days.
Second, since our problem is NP-hard and the exact method can only get the optimal solution of small instances, this paper chooses tabu search as the backbone and proposes a tailored hybrid tabu search (HTS) to get the approximate solution of more instances in a reasonable computation time. In this algorithm, a tailored initial solution construction procedure and a new solution generation method are designed. A shake procedure is also embedded to improve and diversify the search.
Finally, by numerical experiments,this paper analyzes the problem properties and parameter sensitivities, and tests the performance of HTS. The results demonstrates that: (1) The travel cost and objective function value decrease when the maximum skill deviation increases. (2) With the increase of time window preference weight and doctor-patient matching preference weight, the travel cost increases and the objective value decreases. (3) The shake procedure and neighborhoods can effectively improve the efficiency of HTS, and the proposed HTS can effectively solve instances of various scales.
The research conclusion has practical guiding significance for the scheduling decision of doctors in family doctor contract service. Under the medical resources in short situation, to some extent this paper can promote the development and improvement of family doctor contract service. In the future, the problem can be extended to more realistic dynamic situations (e.g., some patient requests are suddenly canceled or added, or the time window for patients to receive service is modified suddenly, etc.).

Multi-objective Three-level Network Optimization of Infectious Medical Waste during the Epidemic Period

LI Xin, CHEN Xi

2023, 32(6):  75-81.  DOI: 10.12005/orms.2023.0185

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COVID-19 has been the sixth global public health emergency so far, with a wide transmission range, high infectivity and multiple transmission modes, making hospital infection control difficult. During the epidemic outbreak period, the discharge of the infectious medical waste increases rapidly. Hospitals, medical waste transfer stations and medical waste treatment centers are facing multiple pressures of waste collection, transportation and treatment, as well as serious impacts on human health and social environment. Strengthening the medical waste management, providing advance warning, and improving medical waste disposal capabilities can effectively curb the epidemic spread and reduce environmental pressures. It is necessary to optimize the transport network between hospitals, transfer stations, and treatment centers, reduce the socioeconomic stress and social infection risk, and improve the transportation efficiency. For the past years, the problem of transportation routes selection for infectious medical waste has attracted scholars’ attention, and some research results have been achieved. However, there is a lack of study that integrates effects of the social infection risk, transportation efficiency and socioeconomic stress. Therefore, optimizing the transport network of infectious medical waste based on the actual situation during the COVID-19 pandemic, and ensuring the safety, timeliness and economy of the collection and transportation process, are realistic research problems that need to be addressed.
The problem concerned in this paper is how to transfer the infectious medical waste generated in hospitals to transfer stations as soon as possible, and then to treatment centers under certain constraints. To make it more concrete, this paper proposes a multi-objective three-level transport network optimization model in order to effectively solve the problem. The multi-objective three-level transport network optimization model of infectious medical waste involves four major steps: (1)Giving the function of time satisfaction to avoid waste accumulation. (2)Establishing a cost optimization function to relieve socioeconomic stress, such as transportation cost, sterilizing cost, disposal cost, etc. (3)Adopting the prospect theory to assess the anti-risk capability of medical waste treatment centers. (4)Based on the time satisfaction function, cost optimization function and anti-risk capability evaluation results, the multi-objective optimization model is established, and the appropriate transfer stations and treatment centers are selected. To transform them into a single objective optimization model, the multi-objective problem is resolved by the weighting method. Finally, an illustrative example is solved using optimizing software packages to verify the method’s feasibility and effectiveness.
Several main conclusions are drawn: (1)The research subject has typical characteristics such as infectivity, which is a relatively novel research field. (2)The paper is analyzed from a systematic and global perspective rather than a single perspective. The research subject is a three-level transportation network and the research objective comprehensively considers the safety, timeliness and economy, which makes results more in line with the actual behavior of decision makers. (3)The characteristic of decision makers’ bounded rationality is considered in the model. (4)The sensitivity analysis of important parameters in the example shows that the transport network during the pandemic may have been influenced by contagious characteristics. Furthermore, there are still many fields for future researches. For example, monetizing the infection risk during the pandemic is an important reference for quantitatively measuring the level of waste risk management. The data size can also be expanded and a variety of intelligent optimization algorithms can be introduced, so as to accelerate the solving speed and the results’ accuracy.

Research on Two-parameter Optimization of DGGM(1,1) Model and Its Application

WANG Fang, WANG Zhizhong, LIU Qiming

2023, 32(6):  82-88.  DOI: 10.12005/orms.2023.0186

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As one of the main contents of grey system theory, grey prediction is widely used to predict short-time series data because it only needs a small sample and poor information to perform well. Among them, the GM(1,1)model is the core and foundation of grey prediction theory. Scholars have carried out systematic research on the GM(1,1) model to improve its prediction accuracy. They have researched its application range, background value and boundary value correction, response function optimization, and the construction of a generalized discrete GM(1,1) model.
Due to the influence of seasonality or statistical system, the characteristics of seasonal cycle fluctuation generally exist in the time series data of social and economic fields. However, lacking the characterization of seasonal periodic fluctuations in time series, the original GM(1,1) model has poor performance in predicting time series data with seasonal periodic fluctuations. Therefore, some scholars preprocessed original time series data with seasonal fluctuations based on the data grouping method and proposed a model of DGGM(1,1)(data grouping-based grey modelling) to characterize the characteristics of seasonal periodic fluctuations.
However, in practical applications, DGGM(1,1) model may have an obvious overfitting phenomenon on some data sets, which leads to the fact that the model still cannot predict the time series data with seasonal periodic fluctuations. Aiming at the problem of unstable performance of the traditional DGGM(1,1) model in practical application, this study proposed a new PSO-ESM-DGGM(1,1) model to expand the application range of the original DGGM(1,1) model. First, the ESM-DGGM (1,1) model is constructed by introducing smoothing coefficient α and using the exponential smoothing method (ESM) to select appropriate smoothing coefficients for time series with different seasonal fluctuation characteristics. On this basis, to solve the problem of setting the smoothing coefficient α and the background value weight e, the Particle Swarm Optimization (PSO) algorithm is used to optimize the smoothing coefficient α and the background value weight e, aiming at the minimum average absolute percentage error. At the same time, the early stop method is introduced in the process of parameter optimization, and the training samples are divided into three subsets: Training subset, confirmation subset, and test subset. The generalization loss of the confirmation subset is observed in the training process, to reduce the time cost in the process of parameter optimization and avoid the phenomenon of overfitting of the model. Finally, taking the data sets of refrigerator export volume and the LI Keqiang index of China as examples, this paper compares and analyzes the DGGM(1,1) model and its improved form.
The numerical experimental results show that the average absolute percentage error of the PSO-ESM-DGGM(1,1) model compared with the suboptimal model in the test set decreases by 27% and 5%, respectively. It is verified that the model can improve the prediction accuracy on the premise of ensuring that the model can accept the fitting error and shows the feasibility and effectiveness of the model. However, the applicability of different types of DGGM(1,1) models has not been theoretically discussed in this study, which is worthy of further study in the future.

Probabilistic Dual Hesitant Fuzzy Aggregation Operators Based on Archimedean Copula and Its Application to Multiple Attribute Group Decision Making

LIU Xi, CHEN Huayou, ZHOU Ligang

2023, 32(6):  89-96.  DOI: 10.12005/orms.2023.0187

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Ecological civilization construction theory plays a very important role in the theoretical system of Chinese style socialism. While focusing on economic and social development, China pays more attention to the protection of natural resources and realize the harmonious coexistence of humans and nature. On December 12, 2016, the National Development and Reform Commission and other four departments released the Evaluation Index System for Ecological Civilization Construction. The public eco-environment satisfaction has been clearly defined as the main indicator to reflect public satisfaction with the ecological environment quality. Because of the complexity of evaluation and difficulty of quantifying information, probabilistic linguistic information becomes an effective form applied in ecological environment evaluation. Constructing new evaluation models and methods of probabilistic linguistic information turns to be a key scientific problem with both theoretical and practical significance in the evaluation decision-making.
When hesitancy fuzzy sets are used for decision evaluation, the importance degree of each membership degree is the same. However, in reality, the importance degree of each membership degree should be different due to the personal preference of decision makers and the difference in the number of decision makers in group decision making. In order to solve this problem,ZHU(2014) combined probabilistic information with hesitant fuzzy set and introduced probabilistic hesitant fuzzy set which consists of several possible membership values associated with their corresponding probabilistic information. To expand information representation dimensions, HAO et al.(2017) proposed the probabilistic dual hesitation fuzzy set, which not only considered multiple membership values of elements and their corresponding probabilistic information, but also paid attention to the non-membership of elements and their respective importance degrees.
Operational laws are the core problem of multi-attribute decision making. From the existing literature, the information operational laws of the probability dual hesitation fuzzy sets are all based on Archimedean norm, such as the probability dual hesitation fuzzy information operational laws based on Algebra norm and the probability dual hesitation fuzzy information operational laws based on Einstein norm. However, after the operation of the above two algorithms, the number of membership degree and non-membership degree elements of the integration result will mostly increase, thus greatly increasing the computation amount. At the same time, the corresponding probability of membership degree and non-membership degree of integration results will decrease with the increase of the number of operations, resulting in the distortion of probability information. Therefore, it is necessary to further explore the operation rules of the probabilistic dual hesitation fuzzy information and construct related integration operators.
In this paper, some operational laws of probabilistic dual hesitant fuzzy information are defined based on Archimedean copula and corresponding co-copula, which provide a new idea for the fusion of probabilistic dual hesitant fuzzy information. These operational laws can not only keep the closure of the operation, but also reflect the relationship between probabilistic dual hesitant fuzzy information. Based on the further study of the properties of these operational laws, the Archimedean copula weighted probabilistic dual hesitant fuzzy arithmetic average aggregation operator and the Archimedean copula weighted probabilistic dual hesitant fuzzy geometric average aggregation operator are proposed, and their properties are also discussed. Finally, the multiple attribute group decision making approach of probabilistic dual hesitant fuzzy information based on these operators is developed. By this approach, a formula can be used to determine the weighting vector of attributes. An instance of public eco-environment satisfaction evaluation is used to illustrate the effectiveness of the proposed approach. In addition, the comparison with existing methods is discussed.

Evolutionary Game Analysis and Simulation Research on Introducing Strategic Investors into State-owned Enterprises under the Guidance of Government Policies

ZHANG Qian

2023, 32(6):  97-103.  DOI: 10.12005/orms.2023.0188

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Since the Third Plenary Session of the 18th Central Committee of the Communist Party of China explicitly proposed to actively develop the mixed ownership economy, the reform of mixed ownership has sped up and achieved remarkable results. As one of the approaches, the introduction of strategic investors has attracted widespread attention. From the perspective of existing research, insufficient attention has been paid to the interests of participants in the reform of mixed ownership, either ignoring the policy factors, resulting in a certain disconnect between theoretical research and practice, or considering the participants incompletely. In view of this, the paper focuses on the following issues: What are the behavioral logic and strategic choices of both sides of the game in the process? How do their strategic choices dynamic evolve under different policy incentives? Is there a heterogeneity? The study and discussion of the above issues help clarify the behavioral logic of participants, improve the effectiveness of introducing strategic investors and optimize policy supply. Compared with existing research, the possible contributions include: Firstly, in terms of research methods, an evolutionary game model is constructed to analyze the strategic choices of Chinese state-owned enterprises and strategic investors, enriching and expanding the research on the strategic interactions of heterogeneous actors in the new round of mixed reform. Secondly, focusing on how policy incentives affect system evolution not only expands relevant research, but also provides a useful reference for optimizing policy supply.
This paper analyzes the strategic choice behavior of both sides in the process of introducing strategic investors into state-owned enterprises by constructing an evolutionary game model, and solves the stable equilibrium strategy. The strategic choice behavior of game players in different initial states and the dynamic evolution process affected by government incentive policies are simulated. The results show that the government incentive policy will affect the strategic choice of game participants.When the initial intention is at a low level, the probability of state-owned enterprises choosing to introduce strategies increases with the increase of government incentives. And the optimal strategy choice of strategic investors turns to “participation” when the initial intention is at a high level. When the initial intention of state-owned enterprises is high, the policy inclination has the promotion effect. Relatively the policy inclination slows down the convergence speed. When the initial intention is relatively high, the decline of the probability of government incentive policy supply may change the players’ optimal strategy choice fundamentally.
There is still room for further expansion in this study. For example, in the practice of mixed reform, there may be multiple strategic investors which are different in entry order, negotiation ability, and other aspects. In the future, we plan to explore the strategic interaction between state-owned enterprises and multiple strategic investors, in order to provide useful ideas for solving the problem of strategic investor selection; How to allocate the equity ratio of mixed ownership enterprises is a core issue, and heterogeneous equity allocation can be considered next.

Game Analysis of Central-local-enterprise Environmental Regulation Evolution from the Perspective of Public Participation

PAN Feng, LIU Yue, WANG Lin

2023, 32(6):  104-110.  DOI: 10.12005/orms.2023.0189

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In China, the relevant subjects of the environmental governance system are mainly the central government, local governments and enterprises. In recent years, with the continuous progress of industrialization, the problem of environmental pollution has become increasingly serious. Pollution incidents in some areas have occurred repeatedly, especially haze, water pollution and other problems, which have aroused public concern and participation in the environmental crisis. The increasing awareness of public participation has led to the continuous improvement of the degree of public participation in environmental governance, which leads to the question of whether public participation will affect the strategic choices of the relevant subjects of environmental governance, and if so, what is the extent of its impact? The comparative analysis of public participation in the basic tripartite evolutionary game model of environmental regulation in the form of parameters can answer this question clearly and intuitively, and at the same time, it has important practical significance on how to stably play the role of public participation in environmental governance, control the uncertainty of public participation as much as possible, and then optimize the environmental governance strategies of relevant subjects and improve the efficiency of environmental governance. First of all, taking the mainstream forces in the environmental governance system—the central government, local governments and enterprises as the main body, this paper constructs a basic framework of a tripartite evolutionary game model, and explores how the three parties choose environmental regulation strategies without considering public factors. Then, on the basis of the above model, the relevant parameters of public participation are introduced to construct a central-local-enterprise tripartite evolutionary game model under public participation to investigate the influence mechanism of public participation on environmental governance. Finally, drawing lessons from the parameter setting methods in the existing literature, in the case of changing the relevant parameters of public participation, MATLAB simulation is carried out to simulate the evolution path of each subject strategy choice in the system, so as to explore more intuitively the influence of public participation on the choice of tripartite subject strategy of central land and enterprises. It is found that: (1)Public participation has an inhibitory effect on illegal pollution discharge by enterprises and, to a certain extent, plays a substitute role in the implementation of environmental regulation by local governments; Public participation plays a positive role in promoting the active implementation of environmental regulation by local governments, and to a certain extent, it plays a substitute role in the supervision of the central government. The increase of public reporting incentives will promote the probability and speed of evolution of local governments to active implementation, improve the probability of evolution of the central government to non-strict supervision, and reduce the probability and speed of enterprises’ evolution to illegal pollutant discharge; Theimprovement of enterprise energy saving and emission reduction technology plays a positive and positive role in promoting both enterprises and local governments. (2)The effect of the environmental protection policy implemented for the public is better than that for the local government and enterprises, and the direct reporting reward to the public can accelerate the stability of the system. This promoting effect is more obvious than that produced by the implementation of environmental protection policies for local governments and enterprises. (3)There is an inverted “U” relationship between the public reporting reward and the steady state of the system: When the public reporting reward level does not exceed the inflection point, increasing the public reporting reward level will help to achieve the stability of the system, but when the public reporting reward level exceeds the inflection point, if we further improve the public reporting reward, its role will gradually shift from promotion to suppression, which will hinder the stability of the system. On the one hand, the central government needs to strengthen the incentive and restraint on the behavior of local governments by the combination of evaluation incentive and reputation punishment mechanism, so as to improve the enthusiasm of enterprises to reduce emissions, and on the other hand, it should establish and improve the system of public participation in environmental protection, encourage the public to actively participate in the governance of the ecological environment, and require the public to exercise their corresponding rights in accordance with the law on the basis of meeting a certain incentive intensity, in order to avoid disorderly and ineffective participation caused by too many people participating in environmental governance in reality.

Stochastic Evolutionary Game of Government Industry University Collaborative Innovation in Green Intelligent Manufacturing Ecosystem

LI Wanhong, LI Na

2023, 32(6):  111-118.  DOI: 10.12005/orms.2023.0190

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With the accelerated evolution of the new technological revolution characterized by “green and intelligent”, the integration of green manufacturing and intelligent manufacturing has become the key to China’s high-quality economic development. The key to the integration of green manufacturing and intelligent manufacturing lies in the continuous innovation and promotion of major common technologies in green intelligence. Innovation in green intelligent manufacturing technology requires a sound and complete ecosystem, and the establishment of a green intelligent manufacturing ecosystem that relies on “government guidance, industry entities, university promotion”, exploring the dynamic evolution laws of the three parties in maintaining the stability of the green intelligent manufacturing ecosystem has become a necessary entry point for promoting the integration of green and intelligent manufacturing.
This paper uses a stochastic evolutionary game model to analyze the evolution law between government-industry-university in the green intelligent manufacturing ecosystem, providing effective decision-making support for adaptive ecosystem management in uncertain environments. The marginal contributions of this paper are as follows. Firstly, this paper studies the evolution mechanism of the government-industry-university collaboration in the green intelligent manufacturing ecosystem, making contributions to the synergetics theory, and helping to solve the barriers to collaborative innovation between subjects. Secondly, this paper discusses the impact of market mechanism and government regulation on the collaborative R&D of green intelligent technology, which helps the Chinese government to use policy and regulatory tools scientifically to remedy market failure, and truly realize the coordination between the government and the market to maintain the stability of the green intelligent manufacturing ecosystem. Thirdly, this paper introduces Gaussian white noise to construct a stochastic evolutionary game model, and analyzes the stability conditions of government-industry-university behavior strategy under the interference of random factors.
The research results indicate that in the market mechanism, a high default amount and the allocation ratio of innovation benefits and R&D costs are key factors affecting the green intelligent manufacturing ecosystem. At the critical value of default amount, industry-university’s behavior strategy is highly susceptible to random factors. At this point, industry-university’s innovation benefits and R&D cost allocation ratio have a significant impact on industry-university’s behavior strategy, and industry-university’s innovation benefits and R&D costs have the best allocation ratio. Under the regulation of government participation, the government’s reward and punishment intensity is positively related to the stability of the green intelligent manufacturing ecosystem, and the reward and punishment mechanism plays a more significant role in the stability of the green intelligent manufacturing ecosystem than a single mechanism, which can not only save the government’s financial expenditure, but also achieve the desired effect. At the same time, government regulation can effectively avoid industry-university opportunism behavior, enable industry-university to maintain long-term effective collaborative research and development of green intelligent technologies.
To better maintain the stability of the green intelligent manufacturing ecosystem, this paper proposes the following suggestions. Firstly, improve the liability system for breach of contract. Industry-university signed a cooperation agreement in the collaborative research and development of green intelligent technology, specifying the liability for breach of contract and the amount of compensation for breach of contract, eliminating speculators’ speculative behavior, and thus maintaining the stability of the green intelligent manufacturing ecosystem. Secondly, develop a reasonable profit and cost allocation plan. Develop a reasonable mechanism for profit distribution and cost sharing, so that the industry-university profit distribution and cost sharing ratio reach the optimal value for both parties, thereby enhancing the enthusiasm of industry-university in collaborative research and development of green intelligent technology. Thirdly, fully leverage the government’s guiding role. According to the fact that industry primarily focus on economic interests and academic research primarily focuses on social interests, the government should increase its policy orientation towards academic research, increase its willingness to participate, and then attract a wide range of industry to participate through academic research.

Live Streaming Window Selection Based on Intuitionistic Fuzzy Linguistic Set

LI Pengyu, WU Chong, ZHANG Zhiyun

2023, 32(6):  119-125.  DOI: 10.12005/orms.2023.0191

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Live shoppingis different from traditional e-commerce sales. Live shopping eliminates the time difference between consumers and product displays, allowing consumers to experience product quality more intuitively and accurately in the live streaming room. Live streaming not only provides convenience for consumers, but also provides a suitable platform for retailersto promote products. Utilizing professional streamers to promote products has become the most popular form of product sales at present. How to choose the most suitable professional live streaming room is the primary decision-making issue faced by retailers.The quality of streamers is generally measured by popularity, and the prices of streamers with different popularity also vary. Streamers with high popularity often have high prices, and inflated indices may be caused by fake popularity. From the perspective of the affordances of live streaming window functions, the differences in design elements of live streaming rooms can also have different impacts on consumers’ purchasing intentions. Therefore, when making choices, merchants cannot solely rely on the popularity of the streamer but need to start from multiple perspectives and gather the wisdom of multiple people to use group multi-attribute decision-making methods to select the optimal live streaming room.However, previous research on intuitionistic fuzzy language sets has encountered problems such as aggregation operators not meeting basic mathematical properties, expert weight calculation neglecting subjective weights, and inheritance operators missing the calculation of hesitancy.
This study utilizes the improved intuitionistic fuzzy language set and combines it with the similarity calculation formula between intuitionistic fuzzy numbers. The topsis method is used to compare the results and determine the most suitable live streaming window for retailers. In theory, this study proposes a novel intuitionistic fuzzy aggregation operator, which compensates for the deficiency of previous operator information and does not meet basic mathematical properties. Second, the calculation method of expert weights in group multi-attribute decision-making has been improved, considering both subjective and objective weights of experts. When calculating subjective weights, the calculation formula for similarity between intuitionistic fuzzy numbers has been improved. Once again, when using integration operators for integration, this study considers that the original score function lacks the calculation of hesitation, and thus proposes a new score function. Finally, based on the above improved methods, this paper proposes a novel group multi-attribute decision-making method based on intuitionistic fuzzy language sets. In practice, this article starts from the current hottest issue of live streaming with goods and considers the selection of live streaming rooms for goods from the perspective of retailers. In previous literature, four functional design elements of live streaming rooms that affect consumers’ purchasing intentions were summarized, and six attributes of live streaming rooms that affect merchant decisions were summarized based on their popularity and delivery prices. And utilizing the optimized intuitionistic fuzzy group multi-attribute decision-making method, it provides an effective means for merchants to choose suitable live streaming windows.

A Combined Prediction Model of Total Retail Sales of Social Consumer Goods Based on VMD-DE-LSSVM Error Correction

JIANG Cuiqing, LI Yuanshen, DING Yong, WANG Zhao

2023, 32(6):  126-131.  DOI: 10.12005/orms.2023.0192

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Consumption is one of the “three carriages” driving economic growth. As China’s economy has entered the new normal, our economic mode is gradually shifting to the consumption-driven growth, making consumption the primary driver of economic expansion. The total retail sales of social consumer goods, as a direct representation of domestic consumption demand, is an important economic indicator for measuring the level of market prosperity. Therefore, scientifically and effectively forecasting its development trend is helpful in understanding social consumption levels and consumption growth trends, and can provide important references for the formulation of macroeconomic policies by the national government. Currently, domestic and foreign scholars have conducted extensive research on the forecasting of total retail sales of social consumer goods, and the proposed forecasting methods have played a significant guiding role in practical work. However, there are still some limitations. The total retail sales of social consumer goods rely on dynamic data from the consumption market and exhibit non-linearity and non-stationarity characteristics, making it difficult for linear forecasting methods to reveal its underlying patterns. In addition, the prediction error contains valuable information, which is often ignored by the existing forecasting methods.
Therefore, this paper follows the modeling idea of “decompose first, then integrate” and introduces the error correction method to construct the error correction combination prediction model based on VMD-DE-LSSVM. Firstly, the original sequence is decomposed by Variational Mode Decomposition (VMD), and then the decomposed sub-sequences are predicted by using the least squares support vector machine (LSSVM) optimized by the differential evolution algorithm (DE), and the predicted results are integrated to obtain the preliminary predicted values. Then the VMD-DE-LSSVM model is used to synchronously predict the initial prediction errors. Finally, the initial prediction value is corrected by the error prediction value. The study focuses on the year-on-year growth rate data of total retail sales of social consumer goods in Anhui Province from January 2010 to December 2020. In terms of selecting forecasting variables, relevant studies are referenced, and indicators are selected based on comprehensiveness, accessibility, and reliability. The selected indicators cover various aspects such as residents’ income, price levels, consumption, industry development, and fiscal conditions. There are 15 economic indicators specific to Anhui Province and 28 national economic indicators, making a total of 43 forecasting indicators. To ensure the usability of experimental data, missing values and outliers are handled, and dataset S₁ is constructed. Due to the impact of the COVID-19 pandemic, there are significant fluctuations in the data during February and March 2020, resulting in many outliers. The local outlier factor algorithm (LOF) is employed to detect the outliers, which are then treated as missing values. Subsequently, the K-nearest neighbor algorithm (KNN) is used to fill in all missing values.
According to the experimental results, the following conclusion can be drawn. First, the introduction of VMD effectively addresses the non-linearity and non-stationarity of total retail sales of social consumer goods during the initial forecasting. Second, the combined forecasting model based on VMD-DE-LSSVM with error correction method fully utilizes the valuable information embedded in the error sequences, leading to a significant improvement in the accuracy of the corrected forecast. Third, the forecasting accuracy of this model is significantly better than other control models, indicating that this model can provide a new modeling method and research idea for the forecasting of total retail sales of social consumer goods.
However, the model proposed in this article still has room for improvement. In the preliminary predictions, only publicly available government statistical indicators are considered, which has certain limitations in terms of timeliness and accuracy. Subsequent research can expand to include non-governmental statistical indicators that possess strong timeliness.

Single Machine Online Scheduling with Linear Deterioration Effect to Minimize Total Actual Processing Time

MA Ran, XU Juannian, ZHANG Yuzhong

2023, 32(6):  132-137.  DOI: 10.12005/orms.2023.0193

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In typical wire rod production lines, the square billets are transported to a heating furnace to be heated to the desired rolling temperature, and then are hot-rolled into wire rods on a rolling machine. However, the rolling time of a square billet on a rolling machine is not a constant value and it relies on its starting temperature. In general, the temperature of the heated square billets drops by degrees, as the waiting time for rolling increases. This results not only inthe wire head being prone to the cracks during the rolling process but also in a longer rolling time, as well as a greater rolling torque and rolling power, leading to large amounts of raw materials and energy consumption. The phenomenon that rolling time of a square billet increases as its waiting time for rolling increases is referred to as “deterioration effect”. In addition, wire orders from customers arrive unexpectedly and remain unexposed until they are placed with the steel plants, which is considered “online over time” or “online” for short in this work. How to schedule the heated square billets from “online” orders in rolling operation in order to mitigate the deterioration effect of square billets has become a crucial issue for wire manufacturers.
In this work, we consider an interesting online scheduling problem on a single machine with linear deterioration effect.Especially, there are irrelevant jobs arriving online over time and the fundamental knowledge of each job J_j, such as its basic processing length b_j, is not revealed for the scheduler until it is released at time r_j. Also, the jobs become available for execution upon their respective release times. The actual processing time P_j of job J_j is assumed to be a linear increasing function of its starting time S_j, i.e., P_j=b_j+KS_j, where K>0. Note that at most one job can be executed on the machine at every point of time and interruption is disallowed. The objective of this scheduling problem is to devise an online schedule algorithm such that the total actual processing time of all jobs is minimized. Studying the online scheduling problem of minimizing the total actual processing time of wire rods with deterioration effects can enable preheated billets to be started as early as possible, avoiding quality issues caused by low-temperature rolling. Moreover, it can minimize the total actual processing time for wire rodes to reduce the resource consumption. Thus, it is of great practical significance for wire manufacturing enterprises.
For this problem, the lower bound is proved to be 2 by means of adversary method. Subsequently, we present an algorithm Delayed Shortest Basic Processing Time (DSBPT) and analyze its several rigid conditions which remain a large obstacle to conducting performance analysis of DSBPT. To make this problem tractable, an auxiliary flexible online algorithm, known as Flexible Delayed Shortest Basic Processing Time (FDSBPT) is designed, which is a flexible version of algorithm DSBPT. Owing to the flexibility of FDSBPT, the performance analysis of FDSBPT becomes easier, and more crucially, FDSBPT may generate several different schedules with respect to an instance, involving the schedule created by the algorithm DSBPT. This indicates that the algorithm DSBPT is a special description of FDSBPT in a particular condition, namely that the competitive ratio of DSBPT is no more than that of FDSBPT. In general, the competitive ratio of an online algorithm is generally achieved in the nondominated instances, and the competitive analysis of online algorithms proceeds more smoothly if the structure of nondominated instances is more regular. In view of this, we apply the novel “peeling onion” analysis technology to seek such nondominated instances with regular structure. Specifically, we choose an instance at random, and adjust progressively this instance to make its structure more regular in the case of a nondecreasing competitive ratio, while removing the dominated instances successively. Based on the nondominated instances with regular structure that we find, we explicitly derive that the algorithm FDSBPT achieves a competitive ratio of 2, which implies that the competitive ratio of DSBPT is also 2. In other word, the algorithm DSBPT is the optimal online algorithm for the problem we investigate.
In the end, combining a numerical example, we can summarize that the algorithm DSBPT can effectively reduce the rolling time in the rolling operation and provide effective decision-making information for wire enterprise managers. The results obtained in this paper are applicable not only for the wire manufacturing enterprises but also for those enterprises in which deterioration effect is included in the actual production process.
In future work, we will consider the online scheduling of multiple rolling furnaces, which is also appealing and worth the effort.

Study on Tourism Supply Chain Cooperation in Destination Branding

SUN Yong, FAN Jie, SUN Zhongrui, QIAO Qin

2023, 32(6):  138-144.  DOI: 10.12005/orms.2023.0194

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Collaborative development of destination branding enhances the core capabilities of a region. While much research has focused on stakeholder collaboration in tourism destinations, relatively little attention has been paid to supply chain collaboration in destination branding. In this study, we construct a differential game model for the cooperative game between scenic spots and tourism enterprises in the process of destination branding. We obtain the optimal trajectory of brand building efforts, destination brand competitiveness and supply chain profits for different decision contexts. Furthermore, we compare and analyse the brand building efforts and supply chain profits of these two entities under different decision scenarios. Finally, we use MATLAB to simulate and validate the results. We draw several important conclusions.
(1)The brand competitiveness of the tourism destination under the centralised decision mode is significantly higher than that of the simultaneous and master-slave decisions, and the total profit under the centralised decision is 72.20% higher than that of the simultaneous decision and 63.54% higher than that of the master-slave decision; The brand competitiveness under the centralised decision scenario is 112.77% and 98.02% higher than that of the simultaneous and master-slave decisions, respectively. In the process of tourism destination brand building, cooperation between upstream and downstream in the tourism supply chain is an effective way to improve brand competitiveness.
(2)Profit sharing between scenic spots and tourism enterprises has no effect on the overall profit of the supply chain under centralised decision making, but has a greater effect on simultaneous decision making and master-slave decision making, and has a threshold effect on the overall profit under simultaneous decision making mode; with the increase of profit sharing ratio, the overall profit of the supply chain increases, but when it reaches a certain level, the overall profit of the supply chain tends to decrease.
(3)Different decision models show that the brand building efforts of scenic spots and tourism enterprises can significantly contribute to the improvement of destination brand competitiveness; The natural decay rate of brand competitiveness has a negative impact on brand competitiveness; Changes in the initial value of brand competitiveness do not affect the stable value of destination brand competitiveness. In addition, the coefficient of influence of the degree of brand building efforts of scenic spots and tourism enterprises on the overall profit of the destination supply chain has a positive influence on the overall profit of the supply chain; with the continuous upgrading of consumption, the coefficient of influence of brand competitiveness on the overall profit of the supply chain increases, and the promotion of brand competitiveness on the overall profit of the supply chain becomes more prominent.
Through a cooperative and non-cooperative game analysis, we have explored the destination brand cooperation behavior of tourism supply chain enterprises and expanded the multi-stakeholder perspective in destination branding research. The research findings can provide strategy guidance and participation suggestions for stakeholders engaged in destination brand building. However, this study also has limitations, as local governments and residents play a significant role in the process of destination branding. Future studies will aim to expand our research to analyze the contributions of local governments and residents to destination brand building.

A Rosenblatt Transform Method Based on Copula Function and Dual Neural Network for Structural Reliability Analysis

DU Juan, LI Haibin

2023, 32(6):  145-151.  DOI: 10.12005/orms.2023.0195

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When there are correlated variables in the structure, the problem of solving structural reliability becomes very complex. One way to deal with correlation problems is to convert correlated variables into independent variables. The commonly used methods include the Nataf transform, Rackwitz-Fiessler transform, and Rosenblatt transform. When the distribution type of variables or the joint distribution function between variables does not follow a Gaussian distribution, the Nataf transformation method will have significant computational errors. In the process of Rackwitz Feessler transformation, it is necessary to assume that the correlation between variables remains unchanged, or to ignore changes in correlation. This processing method will result in significant calculation errors in the calculation results.
Under the condition that the joint distribution function of variables is known, Rosenblatt transformation is transformed by using the conditional density function of variables and the marginal probability density function. In the process of Rosenblatt transformation, the conditional probability density function can accurately reflect the correlation between variables. Therefore, this transformation is not affected by the distribution type of variables and whether the correlation is linear. It is an accurate transformation method. In the process of Rosenblatt transformation, it is necessary to know the joint distribution function of random variables. However, in practical engineering, it is difficult to obtain the joint distribution function of random variables. Therefore, it brings great limitations to the application of Rosenblatt transformation.
It is difficult to solve the joint probability density function or conditional cumulative distribution function of variables in Rosenblatt transformation. This article proposes a Rosenblatt transformation method based on Copula function and dual neural network. The copula function is introduced to construct the joint probability density function of correlation variables. The selection of the optimal Copula function can be based on AIC or BIC criteria. In addition, we construct a dual neural network model to solve the integration equation. One of the neural networks learns the integrand part of the integral equation. It is called an integrand network. Based on the specific connection with the integrand network in terms of weight and activation function, another neural network is used to construct the original function of the integrand in the integral formula. It is called a primitive function network.
The primitive function network is a three-layer neural network composed of input layer, hidden layer, and output layer. By taking the derivative of the variables in the functional relationship of the original function network, the vector form of the functional relationship between the output and input variables of the integrand network can be obtained. In addition, the integrand network is also a three-layer neural network. In order to improve the computational efficiency of the model, dsigmoid and sigmoid are used as the activation function of the integrand network and the original function network respectively. In the integrand network, variables are divided into N equal parts within the interval and crossed to form input samples for learning. According to the relationship between network parameters in dual neural networks, the network parameters of the original function network can be calculated. Thus, the original function network of the dual neural network is obtained. This achieves integration of variables. Furthermore, the solution of the conditional cumulative distribution function can be achieved. This method breaks the limitations of Rosenblatt transformation in solving structural reliability. It expands the scope of use of the Rosenblatt transformation method.
We apply this method to the reliability calculation of steel beam structures, foundation pile structures, and infinite slope structures. From the calculation results of the examples, it can be seen that the method proposed in this paper is very close to the calculation results of the Nataf transformation method and the orthogonal transformation method. This verifies the effectiveness of the method proposed in this paper. Therefore, the method proposed in this paper can be used to deal with structural reliability problems with correlated variables.

Application Research

Research on the Influence of Geopolitical Risk on the Cryptocurrency Market Volatility: Empirical Analysis Based on GARCH-MIDAS Model

BAI Jiancheng, ZHANG Lixia, YAN Xiang, ZENG Huilin

2023, 32(6):  152-158.  DOI: 10.12005/orms.2023.0196

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Cryptocurrency has emerged as a new form of liquidity and investment since their market value exploded in 2017. In the short term, the market fluctuation of cryptocurrency is affected by investor sentiment, economic environment, political situation and other uncertain factors. The market fluctuation trend is extremely unstable and is still in an invalid or weak effective state. The bubble caused by the malicious speculation of speculators in the immature cryptocurrency market will seriously affect the current financial order, even induce risks in the financial system and affect the overall national security. Based on the fundamental needs of “preventing and defusing major financial risks” and “adhering to the overall national security concept”, it is particularly important and necessary to build an effective regulatory system for the financial risks that may rise from digital currencies in advance, promote the healthy, orderly and sustainable development of digital finance, and ensure the stability of the financial system and the overall national security in the new era.
How to discover the factors driving the volatility of digital currency market from the uncertainties has become the focus of scholars. One of these uncertainties is geopolitical risk, which not only reflects tensions among countries, but also affects various economic activities such as finance and investment. The change of geopolitical risk is reflected in the financial market, which most obviously causes the price fluctuation of financial assets. As an emerging financial asset, digital currency may also be affected by geopolitical risk. Therefore, it is necessary to start from the perspective of geopolitical risk, explore the impact of the uncertainty caused by geopolitical risk on the volatility of digital currency market, verify the prediction effect of the change of geopolitical risk index on the price volatility of digital currency, and then judge the market trend of digital currency, so as to provide the basis for the policy making of regulatory authorities and rescue regulators.
In this regard, to examine the impact of geopolitical risk, an exogenous variable, on Bitcoin and cryptocurrency market volatility, as well as the predictive power of different geopolitical risk indices, based on GARCH-MIDAS, this paper constructs new extended models which include Geopolitics Risks (GPRs), Geopolitical Threat (GPT), Geopolitical Action (GPA) and geopolitical risk indices of five countries (Russia, Brazil, Turkey, China and Indonesia) with more active cryptocurrency transactions. The results of the whole sample show that geopolitical risk is an important factor affecting the volatility of Bitcoin and cryptocurrency market. Except Indonesia, the rest of geopolitical risk indices negatively impact Bitcoin market volatility, and the influence of different geopolitical risk indices on the volatility of cryptocurrency market is heterogeneous. The result of out-sample forecasts shows that GPA and GPT have the best predictive power for volatility in the Bitcoin andcryptocurrency market, respectively.
Overall, our results suggest that geopolitical risk is a key exogenous variable of volatility in cryptocurrency market, and regulators should focus on changes in geopolitical risk to avoid affecting domestic financial market due to unconventional fluctuations in cryptocurrency market. The results of this paper provide some enlightenment for potential investors and regulators. On the one hand, according to the geopolitical risk indicators of different types and regions, investors can use them as an important judgment basis to predict the future fluctuations of digital currencies, which can be used to improve portfolio strategies to avoid the blindness of investment, resulting in a large number of market bubbles. On the other hand, geopolitical risk, as an external factor driving the volatility of the cryptocurrency market, can be regarded as the judgment basis for predicting the trend of the cryptocurrency, and regulators can pay close attention to its changes and make a good early warning mechanism to prevent the unconventional fluctuations of the cryptocurrency market from affecting the domestic financial market and endangering the security of the financial system.

Studying on the Impact of Government Subsidies on Remanufacturing Based on Three Remanufacturing Modes

XIA Xiqiang, ZHU Qinghua, LU Mengyuan

2023, 32(6):  159-165.  DOI: 10.12005/orms.2023.0197

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In order to achieve the dual objectives of carbon peak and carbon neutrality, the “14th Five-Year Plan” has clearly proposed that China’s manufacturing industry should establish a green manufacturing system and pursue the path of ecological civilization development. Remanufacturing is an effective way to achieve green manufacturing. Although remanufacturing can effectively facilitate the recycling of waste resources and promote environmental protection, original equipment manufacturer (OEM) retains the intellectual property rights of used products. The law does not tolerate any infringement of intellectual property rights by remanufacturing, despite its potential benefits in reducing carbon emissions. At the same time, the OEM is generally reluctant to engage in remanufacturing due to the lack of proprietary technology for it. However, the OEM may choose a third party for remanufacturing through intellectual property protection. Independent remanufacturing, authorization remanufacturing and outsourcing remanufacturing are the three most common remanufacturing models. Meanwhile, the government adopts subsidy strategies for remanufacturers to promote the development of the remanufacturing industry. The study of the impact of government subsidies on manufacturing/remanufacturing is in line with the strategic needs of national circular economy development. Theoretically, it can improve the theories related to remanufacturing market competition. In practice, it can provide a decision-making basis for the government to improve the subsidy policy and for the OEM to choose the optimal remanufacturing mode. In order to analyze the influence of government subsidies on different remanufacturing modes, based on existing literature and related theoretical studies, firstly, three remanufacturing game models are constructed based on the government’s adoption of subsidy policies for remanufacturers. Secondly, the influence of government subsidies on the optimal solutions of the three remanufacturing modes is compared and analyzed. Finally, the model is validated through simulation analysis using a remanufactured engine as a real case. Additionally, the effect of consumer preferences on the optimal solutions is further analyzed. It has been found that government subsidies will reduce the retail price per unit of remanufactured products and increase the sales volume. Market competition, in turn, causes the market share of new products to decrease. In independent remanufacturing mode, the revenue of the OEM decreases. The OEM can generate revenue from remanufacturing by charging authorization fees based on authorization remanufacturing mode. This can help increase its overall revenue. In outsourcing remanufacturing mode, the OEM transfers a portion of the remanufacturing revenue to the remanufacturer through outsourcing fees, but the revenue of the OEM does not decrease. The revenue of the OEM that outsources remanufacturing is optimal. When the consumer preference is greater than 1/2 and the scale parameter for recycling used products is above a certain threshold, the outsourcing remanufacturing model yields the highest profit for both the OEM and the remanufacturer. The used product recycling rate is greater, and the outsourcing remanufacturing model has the least negative environmental impact when the carbon emissions per unit of remanufactured products are smaller than new products. Government subsidies will promote the development of the remanufacturing industry, but the government should also consider the impact of consumer preferences and carbon emissions of both products on remanufacturing activities, because if the carbon emissions per unit of remanufactured products are smaller than that of new products, but consumer preference favors new products, government subsidies can still reduce the negative environmental impact of both products and promote the development of the remanufacturing industry. The government can increase the sales of remanufactured products by increasing the procurement and promotion of such products on one hand, and by encouraging remanufacturers to improve relevant technologies to reduce the environmental impact of remanufactured products on the other hand. Further research will discuss the situation of information asymmetry between the OEM and the remanufacturer.

Research on Retailers’ Pre-sale and Return Strategy under the Influence of Advertising and Return Period

SHI Baoli, XU Qi, SUN Zhongmiao

2023, 32(6):  166-171.  DOI: 10.12005/orms.2023.0198

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With the rapid development of e-commerce and the Internet, the pre-sale model has promoted the transformation, upgrading, and huge changes of the retail industry in the era of “new retail”. The complex and changing market environment has made the selection of enterprise marketing strategies and pricing decisions increasingly complex. Consumers are gradually becoming rational when facing the “fancy marketing”, frequent promotions, and price reductions of companies, and they are exhibiting strong strategic behavior. On the one hand, in order to reduce the risk of uncertain pre-purchase valuation for consumers and attract them to purchase in advance, retailers usually provide return services when selling pre-sale products, and there are differences in the way different retailers handle returned products. Some merchants do not resell returned products, such as Qiandama and Baiguoyuan, which consider all returned fruits as store losses; While some merchants repackage returned products for resale, such as Guomei Online and Kuba.com, which provide a seven-day unconditional return service and reprocess products that are unused and do not affect resale for a second sale. Although retailers providing pre-sale return services can improve the utility of consumer pre-purchases, they also face a large amount of return risk. Therefore, in order to obtain more revenue, the decision of whether retailers should adopt pre-sale strategies and provide return services, return policy selection, and optimal pre-sale prices, return prices, return deadlines, and order quantities are important issues that retailers face when formulating marketing strategies. On the other hand, retailers can further expand the potential market size of products during the pre-sale stage through advertising investment, and pre-sales have an indispensable role in the product’s promotion.
This article focuses on the situation where retailers have limited return periods, taking into account factors such as consumer valuation uncertainty, demand uncertainty, consumer strategic behavior, demand information updates, and advertising investment, and attempts to address the following questions: What are the prerequisites for retailers to adopt a pre-sale strategy? What are the prerequisites for advertising and promoting pre-sale information? Under what circumstances do retailers provide limited return period return services? When retailers adopt pre-sale or pre-sale return strategies, what are the optimal pre-sale prices, return prices, return deadlines, order quantities, and advertising investment optimization decisions? Additionally, how do consumer product experiences, retailer return ratios, advertising investment, and return deadlines affect the choice of pre-sale return strategies? Therefore, this study considers that enterprises can expand the potential market size of the presale phase through advertising investment and constructs a presale-return policy model for retailers under the influence of advertising investment and return deadlines: Four presale strategies including no presale, no return, return but not resell, and return and resell, are analyzed. The study solves the retailer’s optimal presale price, return price, order quantity, return deadline, and advertising investment, and further compares and analyzes the advantages and disadvantages of the four presale strategies and their critical conditions. The profitability of advertising in product presale information and the retailer’s advertising decision-making are also analyzed.
The research finds that presale strategies can reduce the inventory risk of retailers. Retailers offering return services with a return deadline are always better than selling strategies without return services, and the retailer’s optimal return deadline needs to be determined based on the customer’s return ratio and the product’s life cycle. Whether retailers resell returned products mainly depends on the impact of presale prices, product return deadlines, and return prices. If the presale price under the situation of reselling returns is lower than that under the situation of not reselling returns, the retailer should adopt the presale strategy of reselling returns; Otherwise, it should not. If the presale price is higher than the threshold , then the retailer should adopt the presale strategy. At this point, if the total market size is large, the retailer should invest in advertising to promote presale. If the presale price is lower than the threshold , then the retailer should not invest in advertising, and the presale should only be carried out when the total market size is small. Otherwise, the retailer will only sell during the spot period. Future research will aim to investigate the multi-period collaborative issues involved in the retail sales operations. Specifically, the advertising strategies implemented by retailers in the previous period for certain products may still exert a significant influence on the subsequent sales of those products. As such, it is necessary to delve more deeply into the presale and return strategies of retailers’ multi-period sales processes.

Financing Strategies for Capital Constrained Manufacturer in the Dual-channel Supply Chain

ZHAO Lin, ZHANG Keyong, GAO Yin

2023, 32(6):  172-178.  DOI: 10.12005/orms.2023.0199

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The rapid development of platform economy has greatly promoted economic development and social activities. Correspondingly, retailing channels have been increasingly diversified with the prosperous development of e-commerce platform. With the boom of new channels and the increasing of market share, manufacturers will face great financing pressure and it is harder to get loans from banks. The e-commerce finance provides a new financing option in supply chain. Some large e-commerce sellers have provided e-commerce factoring financing services for platform users. Should capital constrained manufacturers choose bank loan financing or e-commerce factoring financing? What should decision-makers do in making the pricing strategies under different financing modes? These all become urgent problems to be solved in reality. In recent years, supply chain financial services have become a hot topic of academic research, but most of the existing literature focus on the traditional financing modes inside and outside the supply chain, and there are few discussions on the financial role of online retailers. In particular, the financing role of e-commerce platform in the dual-channel supply chain really catches little attention. This study can provide theoretical support for the development of online supply chain financial services.
The foundation of this study is the business process of e-commerce factoring financing services. First, the manufacturer under financial constraint apply for factoring financing from online retailers according to the size of accounts receivable. Secondly, the e-commerce financial service platform approves loans according to the financing ratio. Finally, the manufacturer returns the remaining accounts payable after deducting the principal and interests after sales are realized. Focusing on the e-retailer’s dual roles of distribution and financing offerings, we aim at conducting a comparative study on the e-retailer’s e-commerce factoring financing with bank loan financing. In a dual-channel supply chain consisted of single manufacturer and online retailer, Stackelberg game model is constructed, in which the retailer is the leader while the manufacturer is the follower. Through backward induction method and numerical simulation, the optimal financing strategy of manufacturers, the applicable conditions of principal and the optimal pricing decisions in different financing modes are explored. On this basis, the influence of e-commerce finance on channel demand and decision makers’ income is analyzed.
Our research comes to the following conclusions. (1)The e-commerce factoring financing can improve the overall market demand of supply chain and help manufacturers to develop and expand offline direct marketing channels. In contrast, bank loan financing will reduce the overall market demand of the supply chain and guide consumers to shift to online. (2)The e-commerce factoring financing services are profitable for online retailers. The increased profits come mainly from financing interest income which is enough to offset the lowered online retail revenue. (3)The manufacturer will adopt different financing modes under different financial constraints. When the initial capital level makes both financing modes available, e-commerce factoring financing contract with preferential interest rate and high financing ratio is a better choice to achieve a win-win situation. In addition, our findings have several practice relevant implications. First, Online retailers should fully sympathize with the enterprises with limited capital, and only in setting up preferential financing contracts can we achieve a win-win situation. Secondly, manufacturers with limited capital should reasonably choose financing modes according to the initial capital level, and they should make relatively optimal financing choices according to their own needs.
There are still many deficiencies in this paper to be further studied. This paper compares two financing modes including e-commerce factoring and bank lending. Other financing modes funded by e-commerce platforms are also worth studying. In addition, horizontal comparison with internal financing modes such as prepayment also needs further discussion.

Modeling Risk Spillover Effects of the Financial Market Using High-dimensional Dynamic Vine Copula Model

WU Fei, LIU Mengmeng

2023, 32(6):  179-185.  DOI: 10.12005/orms.2023.0200

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With the acceleration of financial globalization, the characteristics of risk spillovers in financial markets have become more and more obvious. Clarifying the risk spillover effects of the international oil market, the international gold market, and the international exchange rate market on the stock markets has important theoretical value and practical significance for the asset allocations of international investors and risk managers, and the policy formulation of regulators. In order to analyze the risk spillover effects of international financial markets on Chinese financial market, we select Chinese stock market, international oil market, international gold market, and international foreign exchange market as research objects.
Our research has the following two main implications. First, the combination of CoVaR method and high-dimensional dynamic Vine Copula model can not only characterize the dynamic nonlinear dependence structure between high-dimensional financial markets, but also measure the time-varying risk spillover effects of international financial markets on Chinses financial markets. Secondly,we propose a novel stress testing method based on the high-dimensional dynamic Vine Copula model, which extends the traditional single-market risk spillover to the pressure of multi-market scenarios and provides a new idea for the study of risk spillover effect between high-dimensional financial markets.This paper uses WTI crude oil prices, international gold prices, US dollar index, and CSI 300 index to represent various financial markets, and the relevant data are all from the Wind database. The sample spanned from July 2, 2009 to July 2, 2020.In practice, we first construct the joint distribution of high-dimensional financial markets based on the dynamic Vine Copula model and describe the dynamic nonlinear dependence relationship between financial markets. Then, the CoVaR of the Chinese stock market under different financial market conditions is calculated to analyze the risk spillover effects of the international financial market on the Chinese stock market. Finally, the Stress Testing method is used to simulate the changes of the quantile of the conditions of the Chinese stock market when the risk occurs in the joint financial market composed of the internationaloil market, the international gold market, and the international foreign exchange market. The heterogeneity, sensitivity and asymmetry of the joint financial market under different pressure scenarios are investigated by calculating the corresponding risk spillover index.
The empirical results show that: First, the international financial markets have significant positive risk spillover effects on the Chinese financial market, but there are differences in the risk spillover intensity of different financial markets.The risk spillover effects of the international oil market on the Chinese stock market are significantly greater than those of the international gold market and the international foreign exchange market. Second, the upside and downside risk spillover effects of the international financial market on Chinese financial market are asymmetric. Chinese financial market is more sensitive to the positive impact of international financial market. Third, the Stress Testing method based on the dynamic Vine Copula model can effectively characterize the risk spillover effects of multiple financial markets on a single financial market, and the risk spillover intensity of multiple financial markets at the same time is greater than that of any financial market, indicating that there is superposition effect on the risk spillover of the financial market.
The conclusions of this paper can provide an important reference for the decision-making behavior of different financial market entities. For regulators, they should pay attention not only to the internal risks of Chinese financial market, but also to the risk spillover effects of the international financial market. When the extreme risk occurs in the international financial market, it is necessary not only to prevent the systemic risk impact of the international financial market on Chinese financial market, but also to curb the occurrence of financial speculation. In addition, regulators should carry out differentiated risk prevention according to the degree of risk contribution of different financial markets to the Chinese financial market and focus on strengthening the resolution of risk shocks in the international oil market. For investors and risk managers, they should fully consider the risk spillover effects of the international financial market on the Chinese financial market, pay more attention to indicators such as the net long position in the international oil market, and flexibly establish and adjust investment portfolios.

Research on Systemic Financial Risk Early Warning Based on Integrated Classification Algorithm

TANG Chun, LIU Xiaoxing

2023, 32(6):  186-191.  DOI: 10.12005/orms.2023.0201

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With the accelerated integration of financial institutions such as banks and securities, the possibility of cross contagion of systemic financial risks is significantly increasing. How to effectively prevent and resolve systemic financial risks has become an important issue currently facing China. The key to solving this problem lies in precise measurement and effective warning of systemic financial risks. The existing research on systemic financial risk measurement is relatively mature, but there is still relatively little research on systemic financial risk warning. In limited early warning research, data processing is relatively rough, and risk classification often overlooks the peak and thick tail features of financial data, and early warning results are limited by the inherent defects of a single algorithm. The above greatly limits the effectiveness and applicability of early warning.
In view of this, this article further improves the research on systemic financial risk warning. We have constructed a relatively comprehensive early warning system that includes systemic financial risk measurement, extreme systemic risk warning, and analysis of the importance of warning indicator characteristics. Specifically, first, we introduce the isolated forest algorithm into the classification of systemic financial risks, which has better applicability in the classification of systemic financial risk sequences. Second, the rigor of assumptions and the singularity of models are two key factors that constrain the current research on systemic financial risk warning. This article attempts to break through the limitations of hypothesis conditions through the application of machine learning methods, and overcome the limitations of a single algorithm through the integration of classification algorithms, in order to better achieve early warning effects. We select 7 mainstream classification algorithms as base classifiers and combine them with fuzzy evaluation methods to construct an integrated classification algorithm based on multi algorithm voting. Third, this article provides guidance on how to prevent systemic financial risks from the perspective of the importance of indicator characteristics. Compared with previous studies, analyzing the influencing factors of systemic financial risks from the contribution of individual indicators to early warning results is a new perspective. In addition, we also analyze the impact of feature importance indicators on systemic financial risk from both static and dynamic perspectives.
The research conclusions of this article mainly include the following four aspects: Firstly, multiple methods including CoVaR, MES, and VaR can effectively characterize China’s systemic financial risks, and their changing trends are consistent. Secondly, the isolated forest algorithm can be well applied to the classification of systemic financial risks. The duration of extreme systemic financial risks is the longest during financial crises, followed by 2015 stock market disaster. Thirdly, the integrated classification algorithm constructed in the article is an effective technical means for systemic financial risk warning, and with the help of this algorithm, extreme systemic financial risks can be accurately warned. Data dimensionality reduction can further improve the accuracy of early warning and is more suitable for warning extreme tail events, while an increase in lag phases and classification numbers will significantly reduce the accuracy of early warning. Fourthly, systemic financial risks are closely related to the stock market, banking system, and external markets. The potential drivers of extreme systemic financial risks are fluctuations in stock prices at high levels, high credit risks for banks, and large-scale outflows of cross-border capital. At the same time, stock market fluctuations have a gradually decreasing positive impact on systemic financial risks, while the negative lag impact of interest rate spread changes on systemic financial risks has significantly weakened after the 2015 exchange rate reform.

Credit Card Post-loan Risk Rating Model and Empirical Research Based on GA-BP Neural Network

LU Hao, WEI Yi, JIAO Liudan

2023, 32(6):  192-198.  DOI: 10.12005/orms.2023.0202

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In recent ten years, with the change of consumption habits of young groups, the concept of advanced consumption has been deeply rooted in people’s minds, and the consumer credit market has also achieved considerable development. By the end of 2020, the outstanding balance of bank card loans had grown from less than 500 billion yuan in 2010 to 7.91 trillion yuan, and the outstanding balance of consumer loans had risen to 50 trillion yuan. Young low-income people between the ages of 18 and 35 are rapidly becoming the new blue sea of consumer finance, and the problems of overdue and default risk become increasingly prominent. The post-loan supervision of consumer loans is becoming a new issue that needs to be solved urgently. Compared with mortgage loan and car loan, consumer loan has significant characteristics of small amount, high frequency and no collateral. The existing risk management research focuses on personal credit identification before credit granting, while the literature on post-loan risk management is still relatively rare. With the massive development of consumer loans, personal credit risk management after credit granting is becoming a new problem faced by banks. The traditional model that only relies on default rate, overdue times and other post-index is difficult to achieve effective control of post-loan risk.
From a data-driven perspective, this paper combines genetic algorithm and BP neural network to establish a consumer loan post-loan risk grade evaluation model, then compares the performance differences of customer groups with different risk levels. The data comes from the consumer loan, 230,923 flow record of a bank in western China in 2019, involving 199,603 consumer loan customers. The results show that the post-loan risk grade evaluation model based on GA-BP neural network can effectively classify customer credit risk grade. High-risk V customers that should be monitored mainly account for 0.037%. Higher risk customers (Grade IV) account for 0.61% of customers. Risk I, II and III customers with very low default rates account for more than 99 percent. In other words, in the process of post-loan risk monitoring, only 0.637% of accounts can be monitored in real time to effectively reduce the overall delinquency rate and default rate, thus significantly reducing the cost of post-loan risk monitoring. Further comparison of the performance differences of customer groups with different risk levels shows that although the subjects have passed the pre-loan personal credit screening, their post-loan risks show significant differences. The behavioral differences of customer groups with different credit risk levels can be reflected from three dimensions: From the fund flow dimension, the more active the deposit of funds, the stronger the account liquidity, because of the lower credit risk. The higher the spending level, the higher the credit risk. From the income level dimension, the income level of customers with different credit risk levels presents U-shaped distribution. The more stable the income is, the less credit risk the customer has. From the repayment tendency dimension, the higher the minimum repayment ratio, the lower the credit risk has, while the higher the expenditure ratio, the higher the credit risk is. The management revelation is that hierarchical control can effectively realize the dynamic monitoring of the borrower’s consumption behavior after credit granting and timely prevent the post-loan credit risk.

Optimal Reinsurance and Investment Based on SV Model under Variable Interest Rate

XIA Dengfeng, YUAN Weijie, FEI Weiyin

2023, 32(6):  199-204.  DOI: 10.12005/orms.2023.0203

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The insurance company is an enterprise operating and managing risks. In order to avoid huge losses or even bankruptcy caused by excessive risks in the future operation of insurance companies, on the one hand, insurance companies will use reinsurance to share its risks. On the other hand, insurance companies will make reasonable and effective investment strategies to increase the stability of their own operations. For the past decades, many experts and scholars have studied the optimal reinsurance and investment problems for insurance companies. In general, the optimal reinsurance-investment strategies of insurance companies are considered under three kinds of objective functions. The first kind aims to minimize the ruin probability of insurance company. The second is to maximize the expected exponential utility of terminal wealth of the insurance company. The third type is the mean-variance criterion.
For the past years, many scholars have found that stochastic volatility is an important feature of stock price models, as it can better explain the volatility smile, the thick-tailed nature of return distribution, and other features of stock prices. In general, some papers consider the optimal reinsurance and investment strategies of an insurer, where the prices of risk assets are described by GBM (Geometric Brownian Motion) model, O-U (Ornstein-Uhlenbeck process) model and CEV (Constant Elasticity of Variance) model. Compared with the Heston’s SV (Heston’s Stochastic Volatility) model, none of these models contain full-fledged stochastic volatility assumptions. Moreover, on the one hand, insurance companies have uncertainty in their risk selection preferences when making optimal portfolios. On the other hand, it is difficult to accurately estimate the rate of return of risky assets and the expected surpluses in portfolio management. Therefore, relative to theAmbiguity-Neutral Insurer (ANI), Ambiguity-Averse Insurer (AAI) will look for a way to deal with this uncertainty by considering some alternative models close to the estimated model to arrive at a robust optimal reinsurance and investment strategies in the event of model uncertainty. And this systematic approach has been successfully implemented in portfolio selection and asset pricing, and already has some applications in insurance.
In order to better fit the investment situation of insurers in real financial markets, we consider the optimal reinsurance-investment issues of AAI based on Heston’s SV model under variable interest rate. The paper assumes that the AAI, after taking the proportional reinsurance into account, allocates its surplus between the risk-free asset and the risky asset, where the price process of the risk-free asset follows the differential equation derived by deterministic interest rate function and the price process of the risky asset follows the Heston’s stochastic volatility model, respectively. The goal of our optimal problem is to minimize the maximal expected exponential utility of terminal wealth on a set of absolutely continuous probability measures. Firstly, we describe the alternative model by the probability measure that is equivalent to the probability measure of the reference model in the case of model uncertainty. By Girsanov transformation, the equivalent wealth process of an insurer under the alternative model is derived. Then, we establish the corresponding HJB (Hamilton-Jacob-Bellman)equation by applying the stochastic dynamic programming approach, which measures the ambiguity of model uncertainty with different preference parameters with state dependence. And explicitly expressions of the optimal robust reinsurance and investment strategies are derived by solving HJB equation with the CARA (Constant Absolute Risk Aversion) utility function. Finally, the impact of each parameter on the optimal reinsurance and investment is obtained through numerical simulations and the corresponding economic analysis is presented.
The innovation of this paper is twofold. Under variable interest rates, we establish a robust optimal reinsurance and investment model framework for AAI based on Heston’s SV model. In this framework, we not only consider variable interest rates, but also consider the influence of different state preference parameters describing model uncertainty on the optimal reinsurance and investment strategies for an insurer. The results show that the expression of the optimal reinsurance and investment strategies are more accurate than that of the model using the same preference parameter. In addition, the Heston’s SV model that we think about at variable interest rates is more consistent with the real financial environment. Through the numerical simulations, the results and corresponding economic analysis can provide some theoretical guidance for insurers to carry out reinsurance and investment in the financial markets. Furthermore, options can be considered in the investment process of risky assets to enrich the types of investment. Taxes and transaction costs can also be added to make the model more suitable for the actual financial environment. Moreover, we can also consider the optimal reinsurance and investment strategy problem under Heston’s SV model in the case of inflation. This is also the problem that we will consider in the future.

Business Cycle, Local Debt Risk and Risk Taking of City Commercial Bank

ZHENG Changjun, CHEN Shiying, QIAN Ningyu

2023, 32(6):  205-211.  DOI: 10.12005/orms.2023.0204

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In recent years, the world economy has become more volatile, with the wave of nationalism and anti-globalization spreading from the United States to Asia and Europe, and then sweeping the world. At the same time, the frequent occurrence of global risky events such as the COVID-19 pandemic and the Russia-Ukraine conflict has exacerbated global economic turmoil. Global risky events and cyclical economic downturns force governments to adjust their economy through debt expenditure, which intensifies the government debt burden, while downward economic growth limits the government’s financing capacity and solvency. Local governments play a very important role in the counter-cyclical regulation and control of finance. Most of the local government debt funds come from banks. Counter-cyclical regulation under the background of recession is bound to increase the scale of local debt, which will affect bank risks and cause financial instability, which in turn restricts the effect of counter-cyclical regulation and may form a vicious circle. Therefore, it is a subject of important theoretical value and practical significance to clarify the relationship between local debt risk and bank risk taking in the context of business cyclical fluctuations, so as to prevent and resolve systemic risks.
With the change of economic environment, the risk of local debt will inevitably change. As the most important holder of local debt, the adjustment of asset allocation in response to business cycle will inevitably lead to the change of risk taking of city commercial banks. At the same time, the value of various assets of city commercial banks is constantly changing in the economic fluctuations. This complex set of processes provides insight into the dynamics of their relationships through subtle changes in the data. In this paper, based on the establishment of an empirical model and the confirmation of the significant relationship among the three, panel vector autoregressive model is adopted to further analyze the dynamic relationship between the local debt risk and the risk taking of city commercial banks under the business cyclical fluctuations. The results show that:(1)The impulse of local debt risk increases the risk taking of city commercial banks in the long run; On the contrary, the impulse of risk taking of city commercial banks reduces the risk of local debt in both the short and long run. This shows that, as the most important holder of local debt, city commercial banks become the “buffer” of local debt risk, but local debt becomes the “accelerator” of city commercial banks risk. (2)Local economic growth may stimulate city commercial banks to increase risk taking in the short term, but sustained economic growth will eventually reduce the risk taking of city commercial banks. (3)The impulse of local debt risk makes city commercial bank increase the investment of construction credit, which proves that the capital of city commercial bank flows to urban investment company when the risk is amplified. As an important means of government counter-cyclical fiscal regulation, infrastructure investment “bundles” the risks of city commercial bank and local debt risks to some extent. (4)The risk of urban investment bonds is counter-cyclical, which further proves that urban investment companies have part of the counter-cyclical regulation function.
Through the empirical results, it can be concluded that local governments have an important impact on local economic growth, and infrastructure investment is one of the most important channels. Urban investment companies are responsible for most of the local infrastructure construction tasks, while city commercial banks are one of the most important sources of financing for local governments. They are involved in the risk system dominated by local governments, and the risk transmission between them is particularly obvious during the economic recession, along with the counter-cyclical fiscal regulation. Therefore, counter-cyclical fiscal regulation and control should be timely and appropriate, banks should also take counter-cyclical risks into full consideration when investing, and local governments should control the scale of debt, prevent the spread of debt risks, and prevent systemic risks.

Probabilistic Linguistic LEC Risk Assessment Method Based on Prospect Theory and LOWA Operator

SUN Yufeng, GUO Shuo, DAI Xia

2023, 32(6):  212-218.  DOI: 10.12005/orms.2023.0205

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Operating condition risk assessment method (LEC) is a semi-quantitative evaluation method for risk assessment of safety hazards in a potentially hazardous environment, and has become one of the most commonly used safety risk assessment methods due to its simple calculation principle and easy operation. The LEC method uses the risk value (D) to measure the degree of danger of safety risks, and its assessment criteria mainly include the possibility of hazardous events (L), the frequency of exposure to hazardous environments (E) and the possible consequences of hazardous events (C). For past years, although the LEC method has been widely used in the fields of building construction, mining and water conservancy construction, there are still certain limitations. In order to further improve the accuracy of safety risk assessment, this paper intends to improve the traditional LEC method from the following four aspects: (1)The shortage of evaluation information omission due to the inability of precise values to accurately portray the hesitancy of decision maker. (2)The deficiency of low accuracy of safety hazard ranking results caused by the large and discontinuous span of evaluation values. (3)The traditional LEC method applies equal distribution of indicator weights, ignoring the priority order of indicators. (4)There is no consideration of the psychological characteristics of limited rationality of decision makers in the security risk assessment process, such as loss avoidance, reference dependence, and other psychological behavioral factors.
To effectively remedy the inherent defects in the above traditional LEC methods, this paper proposes a probabilistic linguistic LEC risk assessment method based on prospect theory and the linguistic ordered weighted average (LOWA) operator. Firstly, the method uses probabilistic linguistic term sets (PLTS) instead of exact discontinuous values to portray the uncertainty of makers’ evaluation evaluation information. Secondly, we use probabilistic linguistic multiplicative analytic hierarchy process (PL-MAHP) and two-way projection minimum deviation model of positive and negative ideal points to determine the subjective and objective weights of indicators, and the subjective and objective weights are integrated and optimized based on the optimization idea of game theory. Then, considering the psychological behaviors of loss avoidance and reference dependence of decision maker in the process of risk assessment, the prospect theory is introduced into the traditional LEC method, and the LOWA operator is used to determine the prospect reference points to reduce the subjectivity of reference point selection, thus realizing the risk ranking of safety hazards. Finally, in order to test the rationality and effectiveness of this method, we apply it to the risk assessment calculations of safety hazards of hazardous chemical road transportation. On this basis, we verify the feasibility, effectiveness and superiority of the method through the sensitivity analysis of subjective parameters and the comparison analysis with the existing decision-making methods and reference point selection methods.
This paper summarizes and analyzes the shortcomings of the traditional LEC method based on three perspectives: The description forms of evaluation information, the weight model of indicators and the decision-making method of limited rationality. In order to adapt to the language expression habits of experts and effectively avoid the omission of evaluation information, the LEC method is extended to the probabilistic linguistic environment to characterize the hesitancy and uncertainty of decision maker. Considering the influence of positive and negative ideal points on decision points, this paper constructs a minimum deviation weight model based on the two-way projection closeness of positive and negative ideal points. In view of the finite rationality characteristics of decision maker, the prospect theory and the LOWA operator are introduced into the LEC method to improve the objectivity of reference point selection, and finally achieve good risk assessment results.

Management Science

Effects of Exploitative Leadership on Technology Innovation Network Bootleg Innovation: Moderated Mediation Model

WEI Long, DANG Xinghua, LI Lixu

2023, 32(6):  219-224.  DOI: 10.12005/orms.2023.0206

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With the requirements of the Chinese path to modernization and the goal of building an innovation country, the development of innovation network is becoming increasingly important. The decision to integrate into a global innovation network is the key to innovation-driven strategy to catch-up. However, there exists a paradox between standardization and autonomy in the process of continuous innovation, which inevitably induces hidden bootleg innovation behavior. Most of the existing studies have analyzed the influencing factors of bootleg innovation based on the team level, but ignored the primary cause of external destructive leadership, especially for the exploitative leadership. The existing studies have ignored the influencing factors of bootleg innovation in network organization. Therefore, it is urgent to reveal the bootleg innovation mechanism in innovation network based on the perspective of exploitative leadership.
In view of the above background and research, this research integrates the theory of innovation network and bootleg innovation in order to uncover the mechanism of bootleg innovation in technology innovation network. This research constructs the theoretical framework of ‘exploitative leadership-strategic deviation-bootleg innovation’, and analyzes the impact of exploitative leadership on bootleg innovation in technology innovation network. In addition, this research further examines the mediating effect of strategic deviation and the moderated effect of routines updating. Then an empirical test is made with the survey data of high-tech enterprises embedded in the computer industry collaboration network in China. The empirical test is made by using social network analysis and multiple regression analysis.
The results show that: Exploitative leadership, strategic deviation and routine updating are the key conditions for bootleg innovation in technology innovation network. Exploitative leadership is mainly characterized by being exceedingly self-interested and exploitative of others, which has a significant positive effect on bootleg innovation for technology innovation network organization. The strategic deviation has meditative effects between exploitative leadership and bootleg innovation in technology innovation network. The routine updating positively moderates the effect between exploitative leadership and strategic deviation, but has no significant effect between strategic deviation and bootleg innovation. There exist moderated mediation effects.
This research not only enriches the micro mechanism of destructive leadership behavior, but also extends the practical implications to construct a moderately loose innovation ecosystem. On the one hand, this research breaks out of the existing paradox of constructive and destructive effects of bootleg innovation, and also expands the supporting conditions of negative leadership behavior with bootleg innovation. This research proposes and integrates the micro decision making process of organization strategy deviation, which enriches the multi-dimensional co-evolution mechanism of bootleg innovation. This paper analyzes the contingency effect of routines updating in strategy deviation, and also expands the boundary conditions of bootleg innovation. On the other hand, enterprise managers need to think outside the box, so as to construct cognition of bootleg innovation, and design the reward and punishment mechanism. The managers need to pay more attention to strategic deviation, and to break the industry or previous standard by radical or risky decisions, which do not depend on the perspective of normative enforcement. The research results also suggest that firms should focus on the context dependent nature of bootleg innovation, and create a dynamic environment with the constant updating of organization routines.

Corporate Financialization, Financing Constraints and M＆A Behaviors

WANG Fangyun, HU Wenxiu, LIU Li

2023, 32(6):  225-232.  DOI: 10.12005/orms.2023.0207

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In recent years, due to the declining market demand and the ongoing decline in profit margins of domestic real enterprises, real enterprises have become increasingly inclined to shift their investment enthusiasm from the real economy to the financial asset field, resulting in an increasingly obvious trend of domestic real enterprises shifting “from real to virtual”. The corporate financialization is a micro manifestation of the problem of “from real to virtual”. Excessive financialization causes non-financial enterprises to keep unreasonably high levels of financial assets, which increases their dependence on the returns from financial investment. In a long term, it will not be helpful for the growth of their main businesses and value enhancement in the future, and will pose a challenge to whether China’s economic growth can smoothly complete the shift from “factor driven” to “innovation driven”. According to the existing research, the financialization of enterprises has a “crowding out” effect and a “reservoir” effect on real enterprises. The “crowding out” effect is the decrease in the rate of industrial investment caused by the increase in corporate financialization. The “reservoir” effect is that the financial asset allocation of real enterprises can mitigate their financial difficulties and further promote industrial investment. The corporate financialization can also affect their business performance, investment efficiency, asymmetric cost behavior, and innovation behavior.
Mergers and acquisitions (M&A) are not only a capital operation method for enterprises to recombine assets and expand asset scale, but also a crucial means for enterprises to seek strategic layout, look for development opportunities, assist in rapid growth, and enhance competitiveness by strengthening resource integration. They play a crucial role in encouraging industrial structure upgrading and promoting long-term development of enterprises. As a high-risk economic activity, mergers and acquisitions often face failure risks from various aspects such as agency issues, financing constraints, lack of experience, and information asymmetry during the process. Correspondingly, this process itself is also an assessment of enterprise’s full range of capabilities. The enterprise manager serves as the primary decision-maker in investment decisions involving mergers and acquisitions, and the varied traits of the manager directly impact on such decisions. Many studies have been conducted from the perspective of management, suggesting that factors such as demographic and irrational characteristics of executives have a significant impact on corporate mergers and acquisitions behavior.
We consider that M&A are a high-risk activity, and the occurrence of M&A activity is influenced by various factors. Because of the increasingly visible phenomenon of excessive corporate financialization, it is not favorable to the future development of the main business of real enterprises and the improvement of long-term value. This result may negatively impact the M&A behavior of enterprises. Consequently, this paper examines the mechanism of impact of the corporate financialization on M&A activity from that perspective. The research conclusion broadens the discussion about the factors influencing M&A activity, and can provide a valuable reference for listed companies to initiate M&A activity. Based on the above analysis, this paper selects A-share listed companies in Shanghai and Shenzhen from 2007 to 2019 as research samples, empirically analyzes the impact of corporate financialization on M&A activity, and further constructs a mesomeric effect model to test whether financing constraints have a mediating role in the above relationship.
The empirical results indicate that there is a significant inverted U-shaped relationship between corporatefinancialization and M&A activity. When corporate financialization is in a lower range, the increase in corporate financialization will promote M&A activity. On the contrary, excessive corporate financialization will do otherwise. Mechanism testing shows that corporate financialization affects M&A activity through financing constraints. Moreover, financing constraints exhibit a masking effect between corporate financialization and M&A. After considering possible endogeneity issues, changing the model, and changing the measurement methods of the main variables, the results of this paper are still robust.
This study has the following implications: Firstly, a correct understanding of the impact of corporate allocation of financial assets on M&A activity. Financial assets have the dual characteristics of high risk and high return. Although financial assets can be quickly realized in the short term, improving the profitability of enterprises, alleviating financing constraints, and providing financial support for enterprise M&A activity, excessive allocation of financial assets can lead to a shift in the expansion strategy of enterprises towards financial profits, resulting in a dilemma of idle resources in the financial sector and ultimately affecting corporate M&A activity. As the main body of investment decision-making, the management should comprehensively consider various factors of the enterprise, based on the actual development needs of the enterprise, carefully allocate financial assets, and avoid blindly pursuing excess profits of financial assets and deviating from the main business development. Secondly, it is necessary to pay high attention to the “crowding out” effect of corporate financialization. The financialization of enterprises usually focuses on the “reservoir” effect, but there is also a certain “crowding out” effect on the real economy. If the “crowding out” effect is not taken seriously, as the degree of corporatefinancialization increases, the investment in the real economy of enterprises decreases, and the risk of excessive financialization increases. Therefore, when formulating macroeconomic policies, the government should pay high attention to the “crowding out” effect that the virtual economy brings to the real economy.

The Effectiveness of Commercial-scale Demonstration: The Role of Actor Network Management and Knowledge Absorptive Capacity

CAI Ying, LIN Jun, ZHANG Ruxin

2023, 32(6):  233-239.  DOI: 10.12005/orms.2023.0208

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It is a challenging process for innovation achievements to move from laboratory to market, which requires not only improving the level of technological readiness, but also solving the problems of market acceptance, institutional constraints and resource investment. A large number of technologically proven R&D achievements are trapped in the “valley of death” due to lack of funds and paying users before they are released onto the market. As the last hub between technology development and commercial application, commercial-scale demonstration can effectively combine technology drive and market drive, integrate key resources and knowledge along the value chain, and redevelop, demonstrate and promote technologies to create more complete commercial solutions and shorten the time of technology commercialization. Therefore, commercial-scale demonstration is related to whether the R&D achievements in the laboratories can eventually succeed in being on the market. Actor network provides a platform for knowledge exchange and cooperation in this process. However, in the process of knowledge transmission and sharing, due to the lack of unified and coordinated knowledge sharing mechanism, differences in professional fields of all actors and information asymmetry, the knowledge acquired by all actors is ambiguous and difficult to understand, and actors reduce their willingness to share knowledge. It makes it difficult to effectively flow knowledge between actors. Therefore, whether the knowledge distributed in the actor network can be transferred to the enterprises and transformed into the enterprise’s ability to be applied to the commercial-scale demonstration depends on whether the actor network management triggers the effective absorption of knowledge in the enterprises. Therefore, it is necessary to explore the mechanism of enterprises to improve the effectiveness of commercial-scale demonstration from the perspectives of actor network management and knowledge absorptive capacity, and try to partially solve or reduce the knowledge gap that hinders the commercialization of technology, so as to provide reference for the formulation of policies and the decision-making of related enterprises and project managers.
In this study, new energy automobile enterprises are selected as the research objects, and 10 research hypotheses are proposed by constructing a theoretical model among actor network management, knowledge absorptive capacity and commercial-scale demonstration effectiveness. Then the scale is designed and a predictive test is carried out to ensure the stability of the structure and content of the scale. Subsequently, a large-scale questionnaire survey is conducted, and 206 valid questionnaires are finally collected.
AMOS24.0 is used to construct the structural equation model to test the overall structural fit and path relationship of the model. The results show that: Activation (or deactivation) of actor strategy, goal-achieving strategy, and interaction guiding and organizational arrangements strategy in actor network management have positive impacts on the effectiveness of commercial-scale demonstration. Activation (or deactivation) of actor strategy, goal-achieving strategy, and interaction guiding and organizational arrangements strategy in actor network management have positive impacts on knowledge absorptive capacity. The Bootstrap method is used to test the mediating effect. The results indicate that: Knowledge absorptive capacity plays a partial mediating role in the relationship between activation (or deactivation) of actor strategy, goal-achieving strategy, interaction guiding and organizational arrangements strategy and commercial-scale demonstration effectiveness.
The results reveal the mechanism of actor network management affecting the effectiveness of commercial-scale demonstration. A more comprehensive explanation of the effectiveness of actor network management in promoting commercial-scale demonstration has been obtained, and the views by Hellsmark et al., Soderholm et al., that actor network management can promote knowledge exchange and absorption and promote the commercial application of new technologies have been verified, which is helpful to further clarify the process, system and structure of commercial scale demonstration. In addition, the research conclusions also provide decision-making basis for improving the quality and efficiency of the transformation of scientific and technological achievements.