Table of Content

25 May 2024, Volume 33 Issue 5

Previous Issue    Next Issue

Theory Analysis and Methodology Study

Reliability Evaluation of Phased Mission System Based on Triangular Intuitionistic Fuzzy Numbers

HUANG Chao, WU Xiaoyue, LIN Mingwei, XU Zeshui

2024, 33(5):  1-8.  DOI: 10.12005/orms.2024.0139

Asbtract ( )   PDF (1283KB) ( )  

References | Supplementary Material | Related Articles | Metrics

In practical applications, due to the uncertainty of the working environment of the Phased Mission System (PMS) and the limited amount of measured fault data it is a challenge to obtain accurate and sufficient information about the failure of components, which causes difficulties with evaluating the reliability of the PMS under the actual operating environment. To address these difficulties, existing studies commonly rely on experts to qualitatively assess the reliability parameters of components, leveraging their extensive professional knowledge and research experience. However, experts have limited knowledge and experience and may have an unclear understanding of the degradation laws of systems or components to provide uncertain assessments for the performance of components. Furthermore, the PMS, which consists of multiple consecutive and non-overlapping phases, may experience different mission environments at each phase, causing changes in the reliability parameters of components. Existing research mainly focuses on single-stage mission systems, where the failure probability of components is directly obtained via the subjective assessment of experts, which cannot be applied to PMSs and cannot solve the reliability assessment problem of the complex and variable PMS.
To address the problem of insufficient and imprecise information in the reliability assessment of the PMS, this paper proposes a reliability assessment method for the PMS based on the Triangular Intuitionistic Fuzzy Number (TIFN) and the Binary Decision Diagram (BDD) model. Firstly, the exponential operation of TIFN is defined, its related conclusions are presented, and the properties it satisfies are proved. Secondly, to consider the lifetime distribution of components, we require multiple experts to evaluate the Mean Time to Failure (MTTF) of components instead of directly evaluating the failure probability of components, and employ TIFN to describe the evaluation information of the experts. By comparing pieces of the assessment information of multiple experts pairwise to calculate the similarity degree of the assessment information between various experts, we construct a method to determine the membership and non-membership degrees of TIFN by denoting the coincidence of the degree of the assessment information of an individual with that of the other individuals as the membership degree of TIFN, and the non-coincidence degree as the non-membership degree of TIFN. On this basis, an intuitionistic fuzzy failure evaluation model is proposed to determine the intuitionistic fuzzy failure rate of the component. Then, the fuzzy failure function of components is defined according to the proposed exponential operation of TIFN and the exponential distribution function. Further, a reliability evaluation model of PMS is established based on the BDD model. Finally, an example of reliability assessment of a geosynchronous orbit satellite control system is presented to illustrate the implementation process and effectiveness of the proposed reliability evaluation model for PMS. Furthermore, we compare and analyze the traditional PMS-BDD method, the fuzzy fault tree method, and the intuitionistic fuzzy fault tree method with the proposed model. From the comparison results, it is obvious that the traditional PMS-BDD method is a special case where the proposed method only considers the most probable reliability parameters of components and cannot handle uncertainty information. The fuzzy reliability values of the fuzzy fault tree and intuitionistic fuzzy fault tree are higher than the one calculated by the proposed method, which is explained by the fact that they regard the cross-stage component as multiple different components and the three phases of the PMS as three independent phases and they are based on the bottom event probability to derive the top event probability of the fault tree.
The comparison analysis results illustrate that the proposed method is a more general fuzzy reliability assessment method, which can better handle the uncertain and fuzzy information of components in the PMS than the traditional PMS-BDD method. Meanwhile, the proposed method can better process the correlation of the cross-stage components and more accurately calculate the reliability of the PMS than the fuzzy fault tree method and the intuitionistic fuzzy fault tree method.

Research on the Influence of Dissemination and Interaction of Public Security Affairs Douyin Accounts Based on Information Ecology Theory and Algorithm Recommendation

DOU Yunlian, ZHANG Peng, LIU Jing, LAN Yuexin, ZHANG Jiyang

2024, 33(5):  9-15.  DOI: 10.12005/orms.2024.0140

Asbtract ( )   PDF (970KB) ( )  

References | Supplementary Material | Related Articles | Metrics

As Internet and mobile intelligent terminal technologies continue to advance, short video software focusing on music creativity, such as TikTok, Kwai, WeChat Video, and others, is experiencing a surge in popularity. These platforms attract numerous audiences due to their multi-sensory experience, fragmented communication, and instant posting features. TikTok and other short video platforms utilize data mining to push videos to users, thereby revolutionizing information communication and significantly enhancing the accuracy of information dissemination and user loyalty. Notably, a substantial number of government affairs accounts, represented by “Chang’anjian of the Political and Legal Affairs Committee of the Central Committee of the CPC”, have entered short video platforms, aligning with the developmental trend and promoting the contemporary theme while spreading positive societal energy. Among these, short videos focusing on public security and government affairs have demonstrated exceptional performance. According to statistical data from www.cpd.com.cn and related platforms, during the first half of 2021, short video accounts related to public security and government affairs on the TikTok and Kwai platforms collectively posted more than 300,000 videos, accumulating over 70 billion views in total. Short videos focusing on public security and government affairs effectively fulfill the role of government affairs services and play a crucial role in promoting mainstream values. Conducting an in-depth study of the factors influencing communication interaction among TikTok accounts related to public security and government affairs is of significance for enhancing their communication influence.
This paper introduces the concepts of information production, consumption, organization, communication, and management, integrating them with information ecology theory. By incorporating the algorithm recommendation mechanism of the TikTok platform, it outlines the subject behaviors within the short video information ecology, focusing on information production, consumption, and operation. In these 3 dimensions, this paper establishes a model of influence factors affecting communication interaction among TikTok accounts related to public security and government affairs. Specifically, factors such as the video title, writing style, background music, video duration, purpose, style, type, comment responses, video associations, hashtags, collections, external support, and other elements influence the communication interaction of such TikTok accounts.
Collaborating with TikTok, www.cpd.com.cn calculates the CPDI index by combining data from various dimensions and publishes the list of Kwai accounts and TikTok accounts related to public security and government affairs every two months. This paper focuses on the data from the 31st issue of the general list of the National Ranking List of TikTok Accounts of Public Security and Government Affairs (May-June 2021) for analysis. It considers 486 short video works posted by the Top 10 TikTok accounts related to public security and government affairs in May and June as the research samples.
The Qingbo Big Data Platform has launched the Douyin Communication Index (DCI) V1.0, which takes into account the communication influence of TikTok accounts related to government affairs across various dimensions, including the posting index, interaction index, and coverage index. Due to constraints on objective conditions, determining the number of new works and new followers poses a challenge. Therefore, this paper mainly focuses on the communication interaction index of TikTok accounts related to public security and government affairs, also referred to as the C index. This communication interaction C index constitutes 76% of the overall communication measurement and calculation. The higher weighting signifies the communication influence of TikTok accounts related to public security and government affairs to a significant extent.
In this paper, the degree of influence of various factors on the dependent variable C index of TikTok accounts related to public security and government affairs is examined using the SPSS non-parametric test method. Through this analysis, it is revealed that factors such as background music, video purpose, video style, video type, and so on, significantly impact the communication interaction of TikTok accounts related to public security and government affairs. Conversely, factors including video title, writing style, video duration, comment responses, video associations, hashtags, video collections, external assistance, and so on, have no significant influence on the communication interaction of such TikTok accounts.
Based on the current state of development of TikTok accounts related to public security and government affairs, as well as empirical research findings on the factors influencing their communication interaction, this paper presents optimization strategies to enhance their communication influence. These strategies encompass content enhancement, talent development, and operational proficiency, aimed at augmenting the communication impact of such TikTok accounts. Specifically, the first strategy involves intensifying vertical and in-depth exploration of short video content and enhancing the service efficiency of TikTok accounts related to public security and government affairs. Secondly, promoting the effective integration of User-Generated Content (UGC) and Professionally-Generated Content (PGC) models and nurturing talent in the production of short videos pertaining to public security and government affairs are essential. Thirdly, we should operate TikTok accounts related to public security and government affairs in alignment with the popular elements of short video platforms in daily activities.

Collaborative Optimization on Container Liner Slot Allocation and Empty Container Repositioning Based on Booking Online and Overbooking Strategies

WANG Wenmin, CAI Jiaxin, WANG Xiaohan, DIAO Cuijie, LI Mengyu, JIN Zhihong

2024, 33(5):  16-21.  DOI: 10.12005/orms.2024.0141

Asbtract ( )   PDF (1261KB) ( )  

References | Supplementary Material | Related Articles | Metrics

According to the World Economic Outlook released by the International Monetary Fund (IMF), the global GDP growth rate would reach 4.4% in 2022. World economic growth will inevitably result in an increase in global trade transportation demand. At present, global trade transportation mainly relies on container liner transportation. Thus, the demand for container liner shipping will grow significantly. However, in the case of limited capacity in the shipping market, the sharp increase in demand for liner transportation has led to a series of difficulties in the shipping market, such as soaring container prices and difficult booking of container slots. Therefore, in the face of strong demand and fierce competition in the shipping market, liner companies should allocate the container slots reasonably and effectively, on the basis of meeting the needs of shippers and their own operations, to maximize the transportation benefits.
At the same time, empty container repositioning has become a key problem affecting the operation of container liner companies in recent 10 years. So, it is necessary to consider the collaborative optimization of empty container reposition in the allocation of container slots. Furthermore, “Internet+shipping” has gradually become the key research direction of the shipping industry. A series of shipping e-commerce platforms have emerged in the shipping market. They have many obvious advantages. Firstly, the shipping e-commerce online platforms enrich the booking channels of shippers. Secondly, they have the advantages of real-time information interaction and dynamic allocation. Last but not least, online booking is an important way to improve the competitiveness of liner companies. In summary, to maximize the revenue of shipping liner companies and promote the long-term development of the shipping industry, the study of collaborative optimization of container slot allocation and empty container repositioning under the environment of shipping e-commerce has important practical significance.
Although some literature considers the empty container repositioning while studying the container slot allocation problem, most of them don’t consider the empty container operation needs of liner companies. They just regard empty containers as a type of container classification. Therefore, the research on the container slot allocation considering the shipper’s loaded container transportation demand and the empty container operation demand of liner companies has not yet been done. In addition, as the “online booking” mode has just emerged, the academic research on container slot allocation in the online booking mode is very few. To the best of our knowledge, this study is the first attempt to comprehensively consider the shipper’s demand for loaded container transportation and the empty container operation demand of liner companies based on the overbooking theory. To some extent, this study fills up this gap.
The container slot allocation problem is a multi-stage dynamic resource allocation problem, and the empty container repositioning problem is a multi-cycle inventory scheduling problem. Both are revenue management decision-making problems that the shipping industry needs to face. The former focuses on current interests, while the latter on the future. In this study, the above two problems are collaboratively optimized. Based on the overbooking strategy in revenue management, in accordance with booking online mode, this paper compares the collaborative optimization scenario with the traditional container slot allocation optimization scenario.
Two mixed-integer programming models are established for the different scenarios respectively. Scenario 1 model is a mixed integer programming model for slot allocation based on the overbooking strategy in the e-commerce environment. The objective function is to maximize the benefits of liner companies in multiple voyages and cycles, which consists of container slot booking benefits, container transportation costs, customer churn costs, and ship operating fixed costs. Likewise, the scenario 2 model is a mixed integer programming model for collaborative optimization. The objective function consists of seven parts, namely, the booking income of loaded container slots, the income of empty container slots which are required to meet the daily operation of liner companies, the loaded container transportation costs, the empty container transportation costs, the empty container storage costs, and the ship operation fixed costs.
The two models are linearized to obtain exact solutions. And the exact solution is obtained by using the commercial software named CPLEX. In this paper, the PCN route of a liner company is taken as an example for the numerical experiment. The following conclusions can be drawn from numerical experiments. First of all, the total revenue of scenario 2 is significantly higher than scenario 1. In the second place, the utilization ratio of scenario 2 is significantly higher than scenario 1, and the distribution shows a uniform trend. Finally, under scenario 1 and scenario 2, the volume of loaded container booking is higher than the volume of transportation, which is the inevitable result of the overbooking strategy. The validity and practicability of the models are verified by numerical experiments. The results show that collaborative optimization can improve the utilization of liner slots and improve the operating revenue of liner companies. Further research will focus on the collaborative optimization of empty and loaded container conversion and transportation system within the port cluster.

Optimal Location Model of Hydrogen Refueling Station Considering Uncertainty of Traffic Flow and Driver’s Behavior

SHEN Weijie, LI Hua, ZENG Ming, XIE Chuansheng

2024, 33(5):  22-27.  DOI: 10.12005/orms.2024.0142

Asbtract ( )   PDF (1144KB) ( )  

References | Supplementary Material | Related Articles | Metrics

Hydrogen is a kind of secondary energy with abundant source, green and low-carbon, and wide application situations. It is gradually becoming one of the most important carriers for global energy decarbonation, and has great application potential in transportation, industry, power generation and energy storage. China has formulated the Medium and Long-term Plan for the Development of Hydrogen Energy Industry (2021—2035), pointing out that the number of hydrogen fuel cell vehicles should reach about 50,000 by the end of 2025, and deploys the construction of hydrogen refueling stations to build a more comprehensive hydrogen energy infrastructure network. In this context, how to maximize the utilization rate and service quality of hydrogen refueling station through scientific and reasonable location method has become a critical problem in the development of hydrogen industry.
Existing works to some extent ignored the uncertainty of traffic flow and customer behavior. To this end, this paper considering both the uncertainty of traffic flow and users driving behavior, introduced the chance constraint programming (CCP) theory and proposed a bi-level optimization model for hydrogen refueling station location problem. In the upper level, the leaders are hydrogen refueling station operators, aiming at capturing most traffic flow. In the lower level, the followers are the drivers, aiming at maximize the possibility of being served. The behavior of driver impacts the distribution of traffic flow and further impacts the location decision of hydrogen refueling station operators, thus forming a bi-level iterative problem. The bi-level decision iterates between each other and jointly determines the location scheme of hydrogen refueling station. Finally, the model was reconstructed as second order cone programming (SOCP) through KKT conditions and deterministic equivalence to achieve efficient solution.
A case study was conducted using the transportation network in the Yangtze River Delta region as an example. The results showed that: (1)Using probability distribution function to tackle traffic flow uncertainty, the proposed model reduced the sensitivity of traffic flow parameters. In the meanwhile, it considers the interactive feedback decision-making process between drivers and operators, making the planning results more reasonable. (2)As the budget of constructing hydrogen refueling station increases, the growth rate of the captured traffic flow will slow down to a plain, thus the construction budget should not be expanded blindly. (3)As the confidence level increases, the hydrogen refueling station operators will behave more conservative, they will only choose the path with higher traffic flow for the construction of hydrogen refueling station, which will cause low utilization rate of constructions, resulting in a reduction in the total captured traffic flow. Therefore, the operator of hydrogen refueling station needs to balance various factors, so as to meet driver’s demand for to the greatest extent.

Multi-mode Resource-constrained Repetitive Scheduling Model Based on Constraint Programming

ZOU Xin, RONG Zhuang, ZHANG Lihui, ZHANG Qian

2024, 33(5):  28-34.  DOI: 10.12005/orms.2024.0143

Asbtract ( )   PDF (1318KB) ( )  

References | Related Articles | Metrics

The multi-mode resource-constrained project scheduling problem (MRCPSP) is a classical optimization problem in the field of project management. The objective is to identify a schedule with the shortest possible project duration by assigning an execution mode and a start time to each activity. When dealing with the MRCPSP of repetitive projects (MRCPSP-RP), it is necessary to consider logic relations between units, as well as the continuity requirement and scheduling strategy of each activity. If an activity requires work continuity, all sub-activities of the activity must be executed continuously without interruption. Otherwise, allowing work interruptions can increase scheduling flexibility. Three scheduling strategies are available for activities in the MRCPSP-RP: 1)the invariant mode strategy, which requires that all sub-activities in the same activity use the same execution mode; 2)the disordered mode strategy, which allows all sub-activities in the same activity to choose their execution modes at their own discretion; and 3)the ordered mode strategy, which allows an activity to gradually select faster or slower execution modes during the execution.
A number of models and algorithms have been proposed for solving the MRCPSP-RP, yet they still have the following shortcomings. Firstly, the assumption is made that all activities must satisfy the continuity requirement, which cannot deal with the situation in which activities can be interrupted. Secondly, the scheduling strategy of each activity is assumed to be known in advance, with the optimization of this scheduling strategy for the activities themselves overlooked. The aim of this paper is to develop a constraint programming (CP) model for solving MRCPSP-RP that fully considers the continuity requirements of different activities and the optimization of activity scheduling strategies.The CP model defines each sub-activity in terms of interval variables and describes the objective function and constraints using CP expressions. The implementation of CP involves two main steps: problem specification and solving. The first step aims to reformulate the problem as an equivalent constraint satisfaction problem (CSP), while the second step aims to solve the CSP using a search algorithm and constraint propagation mechanism. In comparison with the MILP model, the CP model exhibits a notable reduction in the size of variables and constraints.
This paper validates the effectiveness of the CP model using a residential construction project. The case study involves four scenarios, where Scenarios 1 to 3 require all activities to adopt an invariant mode strategy, an ordered mode strategy, and an unordered mode strategy, respectively, and maintain work continuity. Scenario 4 requires all activities to adopt a disordered mode strategy and allows for work interruptions. The results demonstrate that, for a given level of resource availability, the use of the ordered or disordered mode strategy can significantly reduce the total project duration in comparison with the invariant mode strategy. The average reduction in the project duration is 10.31% and 11.91%, respectively. Furthermore, if work interruption is further considered on the basis of the disordered mode strategy, the project duration can be reduced by 10.99% to 23.01%. Moreover, the computational performance of the CP model is evaluated through numerical experiments, in which the test problems are classified into seven categories based on their size, from small to large: A, B, C, D, E, F, and G. The results demonstrate that the CP model is capable of finding feasible solutions to all the cases within a limited time.All cases in the problem sets, A, B, and C, and some in the problem sets, D, E, F, and G,can be solved accurately. In contrast, the MILP model can only handle the smaller problem sets, A, B and C, with only 56.7% of cases solved exactly. As the problem size increases, the computational performance of the MILP model deteriorates significantly, with the average and maximum deviations corresponding to the problem set C reaching 22.96% and 50%, respectively. For the larger problem sets, D, E, F, and G, the MILP model is unable to provide a feasible solution to any of the instances within one hour.

Research on Emission Reduction Decision of Supply Chain Based on Different Carbon Quota Trading Path

WANG Daoping, YIN Yue, ZHU Mengying

2024, 33(5):  35-41.  DOI: 10.12005/orms.2024.0144

Asbtract ( )   PDF (1405KB) ( )  

References | Supplementary Material | Related Articles | Metrics

Environmental sustainability affects people’s lives and business operations. As countries in Europe and the United States have developed environmental protection plans and achieved good results, China opened its carbon emission trading market in 2013, and formulated relevant carbon emission trading policies. The carbon emission trading market enables smaller enterprises to obtain more carbon allowances, and larger enterprises to trade among their subsidiaries, that is, there are two carbon trading paths—external carbon trading path and internal carbon trading path. Under different trading paths, carbon quotas affect the emission reduction decisions of enterprises, and the emission reduction decisions of enterprises and the internal and external carbon trading prices will have an impact on the final profits of enterprises.
Based on this background, the possibility of an internal carbon trading market is considered. By constructing the Stackelberg model under three scenarios in which only external carbon trading market exists in the retailer-led supply chain, manufacturers conduct internal and external carbon trading, and retailers conduct internal carbon trading and external carbon trading, the impact of external carbon trading price on corporate carbon emission reduction and corporate profits in the three scenarios is analyzed respectively. The carbon emission reduction and corporate profits of the three cases are compared and analyzed.Finally, Matlab and Mathematica are used to visually analyze the model to assist the presentation of conclusions. The numerical analysis data were taken from the commonly used data in papers in this field.
The research shows that the internal carbon trading market is beneficial to the carbon emission reduction and profits of supply chain enterprises. When there is only an external carbon trading market, the carbon emission reduction of enterprises decreases with the increase of the external carbon trading price, and the carbon emission reduction effect is affected by the external carbon trading market. When internal and external carbon trading prices coexist, the free transfer of carbon allowances by retailers will reduce the wholesale profits of manufacturers. Setting internal trading prices within a certain range is conducive to reducing the carbon emission reduction of enterprises in the supply chain and improving the overall profits of the supply chain, which will exceed the overall profits of the supply chain when only external carbon trading markets exist. When the carbon emission reduction difficulty of leading supply chain enterprises is less, it is conducive to helping non-leading enterprises to obtain more profits and improve the overall carbon emission reduction level of the supply chain.
This paper makes an in-depth study of the supply chain carbon emission reduction decision-making problem of “retailer-led-manufacturer-followed decision”, and analyzes the effects of internal carbon trading price, external carbon trading price and carbon emission reduction cost on carbon emission reduction and profits of supply chain members. However, the research still needs to be strengthened in the practical application level: in the actual “production-marketing” link of the supply chain, the quantity of products sold by retailers in different sales stages often deviates from the quantity of products ordered. In addition, the internal carbon trading price is also affected by multiple factors such as government policy orientation and consumers’ preference for low-carbon consumption. In view of this, future research should focus on how retailers should make reasonable carbon emission reduction decisions under the influence of multiple factors such as government policies and consumers’ low-carbon preferences, which will provide an important reference for enterprises to develop more realistic carbon emission reduction strategies.

Interval Grey Entropy Decision Model Considering Attribute Association

ZHENG Qiuhong, DING Quanyu, WANG Yingming

2024, 33(5):  42-47.  DOI: 10.12005/orms.2024.0145

Asbtract ( )   PDF (975KB) ( )  

References | Supplementary Material | Related Articles | Metrics

Under the background of the information age, decision-makers often face many problems that are difficult to solve directly in daily life and production activities, and any problem may have multiple attributes to be considered at the same time. Multi-attribute decision-making problems are ubiquitous in real life. Nowadays, research on multi-attribute decision-making has made great progress and plays an important role in many fields such as economy, science and technology, and aviation. Due to the complexity of society and the high uncertainty of the problems faced, in most cases, it is difficult to accurately describe the relevant information with precise numbers. For this reason, how to solve multi-attribute problems under fuzzy or uncertain conditions has gradually attracted attention. Due to the limitations of human cognition, when the obtained decision information is “poor information” or “less samples”, grey numbers are often used to describe it. The interval grey number can represent uncertain numbers and express complex information effectively, which is not contained in precise numbers. Using the interval grey number can not only reflect the behavior of the decision-maker but also match the actual decision-making situation.
The complexity of decision-making problems and ambiguity of human thinking and cognition, often drive decision-making under uncertain contexts. Though the use of the grey system for dealing with the uncertainty in decision-making has yielded successful results, they are not enough because the use of just one single number to elicit preferences may not reflect experts’ opinions properly. Therefore, in such situations, interval grey numbers have been introduced to model experts’ uncertainty on multiple decision-making models. Despite an important number of decision models based on interval grey numbers, most of them do not consider the bounded rationality of human beings, although their need and convenience in many real-world decision problems are useful and in demand. Therefore, this paper aims to present a new grey multi-attribute decision-making method based on the geometric shape of the associated attributes. First of all, determining the distinguishing coefficient subjectively will cause adverse effects on the correlation ranking, so we propose a new method based on the sequence fluctuations. By determining the distinguishing coefficient of different sequences dynamically, we can effectively solve the problem that is subjectively given. Secondly, the formula of area phase separation is given to calculate the adjacent degree, instead of using the distance between points. The essential characteristics of the interval grey number defines a new formula that can better distinguish between the interval grey number and other ones, and provides a new way of constructing the grey relational model. Then, a new adjustment weight formula that reflects the correlation between attributes is proposed, and the balance degree is calculated. Finally, an example is solved to validate the proposed method.
In this paper, an interval grey entropy decision model considering attribute association is proposed which has a positive impact on the development of grey relational decision-making. In spite of this, this paper only considers the correlation between two attributes and does not analyze the complex situation of correlation between multiple indicators. In addition, grey relational decision-making can be extended to dynamic environments. In view of this, a dynamic interval grey number evaluation is one of the possible future research directions.

Scheduling Deteriorating Jobs on a Single Machine with a Common Due Window

YUE Qing, WAN Guohua

2024, 33(5):  48-54.  DOI: 10.12005/orms.2024.0146

Asbtract ( )   PDF (990KB) ( )  

References | Related Articles | Metrics

In operational systems of firms, scheduling significantly influences the performance. At the same time, with the application of just-in-time concept, due-date assignment also plays an important role in operational systems, which requires customer orders should be finished at a assigned time. In brief, through delicate scheduling rule and appropriate due-date assignment, firm’s limited production resource can be utilized more efficiently, and at the same time they can be better matched with varied customer demands. Thus, the decisions of scheduling and due-date assignment are usually decided simultaneously. However, for many practical application scenarios, it is allowable to complete customer orders within a period. Under these cases, decision makers need to determine due windows for customer orders instead of due dates. A due window for a job consists of three parts: the starting time of due window, the finishing time of due window and the due window size. In literature on due-window assignment scheduling problems, decision makers often assign the same due window for all customers to maintain a unified customer service policy. But the size of due window can be fixed directly by decision makers or can be negotiated with customers. In addition, the existing researches always assume jobs to be finished before the starting time of due window or after the finishing time of due window would incur cost due to earliness or tardiness, but jobs to be completed within due window would not result in any cost. Besides the influence of due window assignment method on formulating and solving due-window assignment scheduling problems, other interference factors existing in operational systems also exert an influence. Among these factors, jobs deterioration would change their actual processing times, and further influences the way the due-window assignment scheduling problem is solved. In the field of studies on due-window assignment scheduling problems with deterioration jobs, it is often assumed that the actual processing time of a job is a linear function of its starting processing time and deterioration rate.
This paper studies the due-window assignment scheduling problem with deteriorating jobs in a single machine environment. The common due-window assignment method is applied to assign a due window for all jobs, and the beginning time of due window and the size of due window are decision variable. The actual processing time of a job is a linear function of its starting processing time and deterioration rate, and jobs have various deterioration rates. The objective of the problem is to simultaneously determine the scheduling policy and the common due window for all jobs, so as to minimize the total costs associated with earliness, tardiness and due-window assignment. To deal with the problem that we consider, we first introduce some notations and formally describe the problem with classical scheduling language. On the basis, we analyze the property of optimal due-window assignment and propose the optimal rule to assign due windows for all jobs. Then, we analyze the property of optimal schedule and propose the specific rule to schedule early jobs, tardy jobs and jobs to be completed within due window, respectively. Based on this, we point out the way jobs are scheduled in different sets, which are the set of early jobs, the set of tardy jobs and the set of jobs to be finished within due window. With those conclusions about optimal schedule and due-window assignment, we derive the way the studied problem is solved and propose the solution algorithm formally. For the proposed algorithm, we finallyanalyze the computational complexity by computing the computational time for each step in the algorithm. By careful analysis, we conclude that the proposed optimal algorithm is polynomial.

Configuration Model for Post-disaster Emergency Medical Service Stations Considering Deprivation Cost

CHEN Xin, HU Zhihua, WANG Yaozong

2024, 33(5):  55-61.  DOI: 10.12005/orms.2024.0147

Asbtract ( )   PDF (1431KB) ( )  

References | Supplementary Material | Related Articles | Metrics

China has a vast territory with a wide variety of natural disasters, and is one of the countries in the world that often suffer from natural disasters. The huge impact range of large-scale disasters, the adverse impact on the affected population, and the low probability of disaster occurrence pose new challenges for decision-makers on how to reasonably allocate emergency facilities and effectively allocate emergency rescue materials to ensure people’s lives and protect their safety. The medical rescue for disasters in China mainly adopts the “on-site treatment” mode, and the location and material allocation of emergency medical mobile hospitals directly affect the rescue efficiency. Protecting the life safety and physical health of the affected population, and maintaining social stability are the starting point and foothold of emergency rescue. Therefore, the psychological pain caused by the lack of rescue materials among the affected population during the post disaster emergency rescue process cannot be ignored. This article uses the cost of deprivation to measure the psychological feelings of the people in disaster areas, considering economic costs and deprivation costs as the decision-making objectives of the medical service station configuration model, medical service station configuration, and material allocation model. It models emergency rescue operations after disasters, providing a reference basis for decision-makers in emergency medical service station configuration and material allocation.
In the “on-site treatment” mode in disaster medical rescue, the location of an emergency medical mobile hospital directly affects rescue efficiency. Based on the deprivation cost, a multi-objective two-level medical service station configuration model is constructed to optimize rescue efficiency and psychological trauma equity of the affected people. Relative deprivation cost is used to describe the fairness of relief and absolute deprivation cost is used to describe the pertinence of relief. Taking the Lushan earthquake emergency rescue in Sichuan province as an example, the feasibility of the model and solution method is verified. The experimental results show that a slight increase in an economic cost can alleviate people’s panic caused by the shortage of medical supplies, reduce the difference in psychological trauma of the affected people, and improve the response speed of emergency rescue.
The research results of this article enrich the site selection decision-making problem of emergency medical service stations and provide new decision-making methods for the development of emergency disaster relief work. This article does not consider the issue of horizontal transportation of emergency supplies at the same level of medical service stations. Therefore, the horizontal transportation of supplies at the same level of site selection points is the next research focus of the author.

Two-stage Emergency Case Retrieval Method for Emergencies with the Influence of the Public Opinion Evolution Situation

WANG Zhiying, YU Liuliu, ZHAO Hongli, NIE Huifang

2024, 33(5):  62-69.  DOI: 10.12005/orms.2024.0148

Asbtract ( )   PDF (1213KB) ( )  

References | Related Articles | Metrics

In recent years, emergencies have caused serious loss of life and property in China and the international community, and their characteristics of uncertainty, suddenness and dynamism often force decision-makers to draw on past event-handling experiences to rapidly formulate emergency response plans. In addition, with the development of mobile Internet and smart terminal devices, once an emergency occurs, the related information derived from it usually receives a large number of comments and retweets from the public, and this kind of information interaction promotes the evolution of public opinion, which, in turn, has a counter effect on the process of handling emergencies. Therefore, it is of great significance to propose a more targeted case retrieval method by considering the influence of public opinion evolution situation on the process in the context of decision makers drawing on historical case experiences to solve current case emergency response problems.
However, the existing case retrieval-based emergency response decision-making methods seldom take into account the impact of public opinion information derived from emergencies and its evolution situation on decision-making results, and it is difficult to conduct research from the overall perspective of the event chain. To this end, the problem to be solved is how to quantify and incorporate the effects of the decision maker’s risk perception preference and the evolution situation of public opinion on the case retrieval process according to the decision-making information such as the given case attributes (including those of the event and the public opinion information) and their weights, in order to retrieve the most similar historical cases from an event chain perspective. Based on this, a two-stage emergency case retrieval method in response to emergencies with the influence of the public opinion evolution situation is proposed. The basic idea of this method is: on the one hand, based on the purpose of case retrieval, the historical cases that are most similar to the target case are retrieved as a kind of expectation of the decision maker, and each attribute value of the target case is selected as the “reference point”, and the prospect theory is applied to incorporate the decision maker’s behavioral characteristics, such as the risk perception preference; on the other hand, the formation process of the event chain is considered: when an emergency event triggers the initial public opinion information, its initial situation does not have a substantial impact on the event handling, and the case retrieval process at this time only needs to focus on the emergency event. However, with the continuous evolution of public opinion, once its influence exceeds a certain threshold, it will affect the emergency response process of emergencies or even trigger new crisis events, and the case retrieval process needs to be adjusted according to the current evolution situation of public opinion. In the research process, firstly, the heat value of public opinion information is calculated to determine the influence degree of public opinion evolution situation, and the stage of case retrieval is determined; secondly, the similarity of event and its attributes is calculated by incorporating the decision maker’s risk perception preference, and the first stage of the case retrieval process is constructed; on this basis, the comprehensive similarity of cases is obtained by calculating and assembling the similarity of public opinion information and its attributes, and the second stage of case retrieval process is constructed; finally, the feasibility and validity of the proposed method are verified through case application and method comparison.
The innovation lies in: first, the risk perception preference of decision-makers for attribute values is introduced into the calculation of case similarity; second, considering the uncertainty of the influence of public opinion evolution situation in different stages, the best similar historical case is determined by organically assembling event similarity and public opinion information similarity. The results show that the proposed method can not only reflect the important role of decision makers’ risk perception preference in emergency case retrieval, but also reflect the impact of event-derived public opinion information and its evolution situation on decision-making results. The above research results can not only lay a certain foundation for the application of behavioral decision-making theory in emergency case retrieval, but also provide feasible ideas for the study of this problem from the event chain rather than the single event level.
For future research, two directions will be considered: first, only the case attribute values of exact numbers, interval numbers and fuzzy linguistic variables are considered in this study, and other data types need to be further refined and incorporated; second, the proposed method only considers the secondary event chain composed of the event and its derived public opinion evolution situation, and in the future, secondary events that may be triggered by public opinion can be further introduced into the emergency case retrieval process, so as to propose a retrieval method applicable to the tertiary event chain.

Review of Emergency Supply Network Based on WOS and Scopus Database

GENG Shaoqing, HOU Hanping

2024, 33(5):  70-76.  DOI: 10.12005/orms.2024.0149

Asbtract ( )   PDF (1168KB) ( )  

References | Related Articles | Metrics

Disasters cause substantial economic losses, affect the lives of a large number of people, and severely damage the environment.From the logistics perspective, it is paramount to plan for efficient arrangement of emergency resources to combat the effects of disasters. An efficient emergency supply network should be established during rescue operations to reduce the impact of disasters. Therefore, the emergency supply network is the research focus of this review paper. The emergency supply network is a complex, interdisciplinary, and inter-agency network as it may call for many organizations and the coordination of executives and practitioners, engineers, scientists, information technology experts, and medical personnel from governmental, public, private, and non-profit organizations in unpredictable, time-limited and budget-constrained circumstances. Constructing a reasonable emergency supply network is a critical decision in both national/regional and international response to a disaster that justifies optimizing emergency resources.
This article takes papers on emergency supply networks included in the Web of Science and Scopus databases from 2000 to 2021 as samples to explore the research progress in emergency supply networks at home and abroad. The paper adopts a systematic literature review. After a series of screening operations, a final 188 articles are used for bibliometric, network, and content analyses. The combination of bibliometric, network, and content analysis can make up for the shortcomings of the existing research on the topic details and follow the academic rigor. It uses BibExcel and Gephi software to analyze the characteristics of papers, research clusters, and development trend analysis. The research results show that most articles in this field are published in operations research and operations management journals, and the number of publications generally indicates an upward trend. In addition, core author groups and research institutions in emergency supply network research have gradually emerged, showing that the outstanding performance of an organization may only require the work of one or two scholars.By counting the countries from which the authors come, it is found that the countries/regions facing disaster challenges lack robust rescue systems and comprehensive disaster management plans. As seen from the title and keywords of the paper, it is necessary to develop modes and frameworks and optimize emergency supply networks to cover diverse and variable demands in uncertain environments.
We determine four research clusters using bibliometric and visualization tools and analyze the interrelationships among research topics. Domain research mainly focuses on material distribution networks (Cluster 1), location-routing-inventory networks (Cluster 2), vehicle routing and scheduling networks (Cluster 3), and casualty management networks (Cluster 4). Specifically, material distribution networks dominate early research. Scholars still maintain interest in this cluster to this day. The location-routing-inventory network combines site selection with inventory and transportation issues in Cluster 1 and plays a central role in the early stages. Cluster 3 focuses on specific vehicle dispatch arrangements. After 2010, cluster 4 becomes the focus of research, emphasizing the importance of post-disaster services. By 2021, clusters 1-3 are highly correlated, and attention to cluster 4 continues to increase, indicating the maturation of research in this field and the need to review the relevant literature on this topic. From the perspective of development trends, research on vehicle routing and scheduling in combination with site selection or inventory has been highly correlated. Post-disaster emergency evacuation and assistance services are still the research focus in this field.
Finally, we highlight and discuss the gaps we have found through reviewing these papers to provide researchers with potential future research directions. The four research directions of improving the reliability of emergency supply networks, focusing on the demands of victims, constructing more realistic models, and integrating various relief activities are current and continue to be the research hotspots in the field of the emergency supply network. These directions may provide a robust roadmap for future research in this field.

Evolutionary Game Analysis of Supply Chain Operations Decision under the Background of Low-carbon Economy ——Based on the Perspective of Government-Enterprise-Consumer Synergy

ZHANG Jinquan, WEN Subin, LI Hui, LYU Xin

2024, 33(5):  77-83.  DOI: 10.12005/orms.2024.0150

Asbtract ( )   PDF (1154KB) ( )  

References | Supplementary Material | Related Articles | Metrics

The development of a low-carbon supply chain is an important pathway to achieve the goal of carbon peak and carbon neutrality. How to realize the spontaneous development of low-carbon economy in the secondary supply chain of manufacturers and retailers? Based on various theoretical perspectives, including the signal theory, legitimacy theory, and stakeholder theory, this paper systematically expounds the strategy choice of manufacturers and retailers considering the engagement of the government, enterprises, and consumers. Under the premise that the government and consumers participate in the low-carbon development of enterprises, the development strategies chosen by manufacturers and retailers will inevitably be influenced by the government and consumers, and evolve to a direction that is conducive to their own development. There are two ways for manufacturers to choose strategies: the negative or the positive development of low-carbon economy, and retailers can choose strategies to negatively or positively develop low-carbon economy. The combination of two pairs produces four strategic combinations: low-carbon technology and low-carbon marketing; traditional technology and low-carbon marketing; low-carbon technology and traditional marketing; and traditional technology and traditional marketing. And we innovatively divide the four strategic combinations of manufacturers and retailers into Cash Cow, Star, Question Mark and Dog ones. Then, the method of evolutionary game is used to explore the equilibrium conditions under different strategy combinations, Propositions 2 and 3 are proposed according to the equilibrium conditions, and the parameter value ranges of Propositions 2 and 3 are analyzed in realistic scenarios. Besides, the equilibrium conditions of the Cash Cow combination are explained economically. Finally, a numerical simulation is used to analyze the impact of internal and external factors on the equilibrium profit of manufacturers and retailers under the low-carbon technology and low-carbon marketing strategy, including the sensitivity coefficients of carbon emission reduction and marketing effort, carbon trading price, low-carbon subsidy, and carbon tax ratio.
The results show that only when the sensitivity coefficients of carbon emission reduction and marketing efforts, carbon trading price, low-carbon subsidy and carbon tax are within a certain range, the low-carbon technology and low-carbon marketing strategy can become the stabilization strategy of the system, and the government, enterprises and consumers can effectively collaborate. Meanwhile, the carbon tax ratio has a certain complementarity with the low-carbon subsidy, meaning that an appropriate increase in the carbon tax ratio can reduce the fiscal expenditure. Under the equilibrium condition of the low-carbon technology and low-carbon marketing strategy, the level of manufacturers’ low-carbon effort positively affects the level of retailers’ low-carbon marketing, and retailers can only obtain greater benefits by actively cooperating with manufacturers to develop low-carbon economy. Additionally, the overall profit of the low-carbon supply chain can be achieved by increasing the sensitivity coefficient of carbon emission reduction, the sensitivity coefficient of marketing efforts, and the low-carbon subsidy, and reducing the carbon emission per unit product. When the carbon emissions per unit product are low, the increase in carbon trading prices can also achieve the same goal.
By analyzing the game process of manufacturers and retailers, we can understand the critical value of the resource input level of the government and consumers under the four strategy combinations, e.g. Cash Cow, Star, Question Mark and Dog combinations. It is conducive to the precise allocation of resources by the government and consumers, enhancing the efficiency of social resource allocation and ultimately achieving the goal of carbon emission reduction. A numerical simulation is a simulation analysis in a more ideal situation, but it does not fully reflect the reality, so future research can be combined with real data to make a further empirical analysis.

Research on the Channel Mode Selection of Supply Chain Considering Green Technology R&D Uncertainty

CHEN Nan, CAI Jianfeng, HAN Wenting, MA Yanran

2024, 33(5):  84-90.  DOI: 10.12005/orms.2024.0151

Asbtract ( )   PDF (1303KB) ( )  

References | Supplementary Material | Related Articles | Metrics

With the increasingly serious problems of resource waste and environmental degradation brought about by traditional supply chains, the operation and management model of green and sustainable supply chains has become a hot spot for governments, enterprises, and scholars. To meet the requirements for government policies and regulations, manufacturers have begun to make green R&D and innovation to reduce environmental pollution. However, manufacturers cannot strictly control all processes, and the failure of green technology research and development always exists. At the same time, manufacturers need to deal with consumer concerns about the authenticity of green products and require traceability of products when green technology R&D is successful.
This study considers that the green supply chain operates on traditional online platforms and blockchain platforms, respectively. In each mode of operation, we establish the model considering the success or failure of the manufacturer’s green technology R&D. At the same time, the government subsidies and retailer’s service level, platform service costs, blockchain marginal use costs, fixed costs, and other blockchain-related attributes are further introduced. Through the construction of the manufacturer-led Stackelberg game model and Cartelization cooperation model, the paper investigates the supply chain members’ optimal decision-making under four different situations that operate on traditional online platforms and blockchain platforms, and analyzes the influence of changes in parameters such as the using cost of the blockchain on the optimal decision-making of supply chain members.
Specifically, the manufacturer and retailer make two-stage decentralized decision-making independently under the traditional platform. In the first stage, the manufacturer first determines the manufacturer’s green R&D effort level. If green technology R&D is successful, then the retailer will determine the marketing effort level. However, if R&D fails, the retailer will not conduct green marketing but directly determine the product price. In the second stage, if R&D is successful, the manufacturer will determine the online service level, and on this basis, the retailer determines the sales price of the product. If R&D fails, it will make independent decisions. When the manufacturer and retailer operate through the blockchain platform, the two parties will adopt the cooperation model, which aims to maximize the overall profit of the supply chain. When green technology R&D is successful, they will jointly determine the green R&D level of the manufacturer and the green marketing level of the retailer.
The results of this paper mainly include the following: (1)Whether it is a traditional online platform or a blockchain platform, the optimal decision-making under the successful R&D of green technology will be better than the optimal decision when green technology R&D fails. This is because the green product can obtain government subsidies, further expand market demand and increase product sales prices to obtain higher profits when green technology R&D is successful. (2)The blockchain platform can eliminate the wrong service cost of traditional online platforms and provide better service levels. The cooperation model between members is also better than the traditional decentralized decision-making model. (3)The indirect benefits, marginal usage costs, and fixed costs of the blockchain platform determine whether the supply chain members use blockchain. The indirect benefits brought by the blockchain platform have a relatively large impact. With a decline in the level of benefit, the retailer’s sales prices have risen rapidly, but the profits have fallen sharply. At this time, retailers will tend to adopt traditional online platforms. (4)Regardless of whether green technology R&D is successful or not, the profits of manufacturers under the blockchain platform are higher than those under the traditional online platform. This is because manufacturers and retailers can operate in a cooperative manner through the blockchain platform. In summary, the results provide decision support for the members of the supply chain to adopt green technology R&D and the blockchain platform and provide theoretical and methodological references for the application and practice of blockchain technology in the green supply chain. This study also presents a new direction for further study.

Batch Strategy of Remanufacturing Supply Chain Based on Dual-uncertainty of Market Demand and Number of Remanufacturable Parts

XIA De, ZHANG Yundong, LI Yuting

2024, 33(5):  91-97.  DOI: 10.12005/orms.2024.0152

Asbtract ( )   PDF (1236KB) ( )  

References | Supplementary Material | Related Articles | Metrics

The recycling, remanufacturing and reselling of parts is of great significance for the resource conservation and reduction of environmental burden. For example, the first batch of 14 remanufacturing pilot enterprises announced by the National Development and Reform Commission, such as WEICHAI Power (Weifang) and Sino-Trunk, have started to calculate the economic cost of the whole remanufacturing supply chain of waste engines and other parts and arranged remanufacturing production in combination with demand orders. Meanwhile, in August 2022, the National Development and Reform Commission, the National Bureau of Statistics and the Ministry of Ecology and Environment jointly issued the Implementation Plan on Accelerating the Establishment of a Unified and Standardized Statistical Accounting System for Carbon Emissions, proposing that a unified and standardized statistical accounting system for carbon emissions would be initially established by 2023. Therefore, this study provides decision-making reference for enterprises to formulate remanufacturing production batch strategy and procurement batch strategy under double uncertainty.
In this paper, the remanufacturing supply chain consists of procurement, transportation, storage and manufacturing activities. Considering the uncertainty of the annual demand quantity of the end market and the uncertainty of the quantity of remanufactured parts in the recycling market, and taking each production batch and purchase batch in the remanufacturing process as the decision variables, a baseline model is established for the annual environmental cost, annual economic costs, and annual total costs of the entire remanufacturing supply chain system. The following problems are studied: (1)By solving the model, the existence of the optimal production batch and purchase batch is discussed respectively when the annual environmental cost, annual economic cost and annual total cost are the lowest. (2)The expression of the optimal production batch and purchase batch is calculated, and the optimal production decision of the enterprise is demonstrated by a numerical simulation experiment. (3)Through a sensitivity analysis, the influence of the double uncertainty of market demand and the quantity of remanufactured parts on the optimal production batch and purchase batch is discussed.
The results show that: (1)Under the three situations of the lowest environmental cost, economic cost and total cost, there is a unique optimal production batch and purchase batch and an increase in the proportion of remanufacturable parts can significantly reduce the annual total production cost. (2)When remanufacturable parts or new parts are in two different preparation states of finished products and products in process, differences in corresponding annual carbon emission, annual economic cost and annual total cost significantly affect the production batch and purchase multiple strategies of enterprises. (3)The double uncertainty of remanufacturable parts and consumption demand quantity will have different effects on the optimal solution. When the total storage cost generated by the storage unit of finished products in the finished products warehouse is greater than the total storage cost generated by the storage unit of supporting parts required for the preparation of finished products in the parts warehouse, with an increase in the proportion of remanufacturable parts, the optimal production batch will decrease and the optimal purchase multiple will increase. With an increase in the total market demand, the optimal production batch will increase, and the optimal purchase multiple will decrease. When the total storage cost generated by unit product storage in finished product warehouse is less than or equal to the total storage cost generated by corresponding supporting parts required for unit product preparation in parts warehouse, the purchase batch should always be equal to the remanufacturing production batch. In this case, the optimal production batch will increase with an increase in the proportion of remanufacturable parts or the total market demand. And the optimal purchase volume will be increased simultaneously to achieve the minimum annual total cost.
This study has enriched the relevant research on the remanufacturing supply chain in the waste recycling-manufacturing-consuming market. In the future, combining the classification of the quality characteristics of remanufactured parts, we can try to discuss the remanufactured production decision-making of recycled parts with different quality levels. We gratefully acknowledge the financial support from the National Natural Science Foundation of China (No. 72172112).

Research on Factoring Financing Decision of Supplier’s Accounts Receivable Considering Bank-Enterprise Game

LI Junhao, CHEN Kehong, DONG Ke

2024, 33(5):  98-104.  DOI: 10.12005/orms.2024.0153

Asbtract ( )   PDF (1064KB) ( )  

References | Supplementary Material | Related Articles | Metrics

In contemporary business environments, the phenomenon of cash flow shortages among upstream suppliers due to accounts receivable tie-up is prevalent, particularly in scenarios involving e-commerce giants such as Alibaba and JD.com. This liquidity constraint not only affects the production and operational efficiency of suppliers but also poses stock-out risks for retailers, thereby impacting the stability and development of the entire supply chain. The exacerbation of this issue is attributed to the fact that the assets of most small and medium-sized enterprises are tied up in accounts receivable, limiting their ability to leverage these assets for financing. The motivation of this study is to explore accounts receivable factoring financing as a strategic decision to alleviate supplier funding pressures, considering dynamic the demand uncertainty and bankruptcy risks within the framework of a bank-enterprise game. The core issue addressed in this research is how suppliers optimize their financing decisions through accounts receivable factoring in the supply chain environment where interactions with retailers and banks occur. Specifically, the study investigates the equilibrium decisions of banks, suppliers, and retailers in a three-party Stackelberg game, focusing on the impact of factoring financing on the expected benefits and bankruptcy risks for all parties involved. The research also examines the relationship between the actual production quantity by suppliers and the expected production quantity by banks, and how this relationship is influenced by the initial capital of suppliers. The significance of this study lies in its theoretical contributions to the field of supply chain finance, particularly in the domain of accounts receivable factoring. By constructing a comprehensive model that considers strategic interactions among banks, suppliers, and retailers, the research provides insights into optimal financing strategies that maximize expected returns while managing risks. Practical implications are equally important, as the findings guide small and medium-sized enterprises to effectively utilize their accounts receivable to alleviate financial constraints and enhance operational performance. Furthermore, the study provides a theoretical basis for banks and other financial institutions to formulate credit policies that balance risk management with profitability.
The present study employs mathematical modeling to construct a tripartite Stackelberg game model involving banks, suppliers, and retailers. This model takes into account the stochastic nature of market demand as well as the associated bankruptcy risks of suppliers and retailers. The decision of suppliers to participate in factoring financing is modeled as a response to the trade credit terms offered to retailers and the financing conditions set by banks. Parameters of the model are derived from real-world scenarios, and equilibrium outcomes are obtained through backward induction. The study also considers the impact of initial capital of suppliers on production decisions and the expectations of banks regarding the quantity of production by suppliers.
The research findings indicate that both retailers and suppliers face bankruptcy risks due to demand uncertainty, with suppliers experiencing lower risk compared to retailers. The actual production quantity of suppliers does not always align with the quantity expected by banks, and this disparity is influenced by the initial capital of suppliers. The study demonstrates that banks can influence the production decisions of suppliers and maximize their own revenue by adjusting interest rates or collateral requirements.
The application of this research can be observed in the strategic financing decisions of small and medium-sized enterprises operating within supply chains dominated by large retail giants. By understanding the dynamics of factoring financing, these suppliers can make informed decisions to optimize their cash flow while mitigating risks associated with financial constraints.

Game Model of Long-term Freight Rate Contract between Container Liner Companies and Customers

FU Qiang, HU Sheng, WU Di

2024, 33(5):  105-111.  DOI: 10.12005/orms.2024.0154

Asbtract ( )   PDF (1083KB) ( )  

References | Related Articles | Metrics

The fluctuation of container ocean freight rates is considered crucial to the income of liner shipping companies. Facing increasingly fierce market competition and the changing customer demands, liner shipping companies (carriers) must develop reasonable pricing strategies to attract and retain customers (shippers), ensuring successful cooperation of their container transportation business. This study focuses on the pricing issues of long-term freight rate contracts between the carrier and shipper, aiming to construct an effective game model by deeply analyzing the bargain strategy of both parties under different market conditions. The goal is to find a balance point that maximizes the income of carriers while satisfying the shippers’ needs for their cost control. This paper enriches the application of operations research and management science in theory and provides valuable pricing strategies for carriers in practice, which will have significant theoretical and practical implications.
A game model is built based on the Rubinstein’s alternating offer framework, which simulates the bargain strategy between carriers and shippers in the long-term freight rate contracts. The model specifically considers the bidding policies under the presence of irrational offers and explores the impact of irrational offers on the attitude of both parties in the game under different market situations. Through this model, we are able to gain a deeper understanding of the changes in the shipper’s attitude in freight rate bargains and its impact on the carrier’s bidding strategies under excess or insufficient capacity market conditions.
The results show that in an excess capacity shipping market, shippers usually occupy a dominant position in bargain. In such circumstances, there is an uncertainty in the carrier’s first-stage bidding strategy, while the shipper’s rational bargain directly affects the final outcome of the deal. When the shipper adopts an irrational bargaining strategy, if the carrier fails to recognize this, the offers may decline and the bargain ends,or else the first-stage fails and comes to the second stage. In an insufficient capacity shipping market, the shipper is in a passive position and will accept the offer as long as the carrier’s bid is below their acceptable price.
The empirical analysis further validates the effectiveness of game theory and reveals the final game structure of both parties and factors such as expectation for the future market, discount factors, and bidding strategy. Through the solution of the model, we have obtained the optimal bidding strategies under different shipping market scenarios, providing practical guidance for carriers and shippers in actual bargains. The empirical analysis indicates that by adopting the strategies proposed in this study, carriers can effectively increase contract signing rate, enhance long-term cooperative relationships with shippers, and thereby gain a stable market share in the fiercely competitive market.
Future studies may further focus on more market factors, such as the operational efficiency of liner companies, the bargaining power of shippers, and potential market entrants. In addition, studies can also be extended to other types of transportation such as multimodal transport, and exploring more pricing game methods such as dynamic pricing. These studies will help carriers and shippers better respond to market changes, thereby achieving more scientifically sound and reasonable long-term transportation contracts.

Differential Game Analysis of Government-Enterprise Procurement Strategy under Public Health Emergencies

WANG Yue, LIU Ming, CAO Jie

2024, 33(5):  112-117.  DOI: 10.12005/orms.2024.0155

Asbtract ( )   PDF (1100KB) ( )  

References | Related Articles | Metrics

For the past twenty years, the world has experienced significant public health emergencies such as SARS (2003), H1N1 (2009), MERS (2012), Ebola (2014), Zika (2016), and COVID-19 (2019), which have seriously endangered human life and health safety. To address the explosive growth in the demand for emergency supplies following these outbreaks, governments typically adopt two approaches;the first approach involves pre-stocking some emergency supplies, which incurs inventory costs for the government in the absence of an epidemic; the second approach involves directly participating in the spot market to purchase emergency supplies, which can lead to soaring prices and dramatically increased procurement costs when a public health emergency occurs.
Unlike existing literature, which primarily focuses on pre-event joint procurement or stockpiling by governments and enterprises, this paper focuses on the procurement strategies for emergency supplies by governments and enterprises during public health emergencies. It explores how the government can gather as many emergency supplies as possible and how enterprises can produce them in substantial quantities. Furthermore, by examining the procurement strategies of governments and enterprises under different conditions, this study analyzes the factors influencing their strategic choices, thereby enhancing the specificity and effectiveness of government procurement strategies and regulatory actions.
Based on the content above, we focus on developing and applying differential game theory to analyze procurement strategies between governments and enterprises during public health emergencies. This approach integrates multiple variables such as taxation, market prices, and the number of enterprises to construct a differential game model that analyzes the profits of governments and enterprises in both non-cooperative and collaborative scenarios. The model aims to determine equilibrium strategies that optimize outcomes for both government and enterprises under various conditions. Through this methodological framework, the paper investigates how strategic adjustments in government procurement policies and enterprise production capabilities can effectively meet the urgent demand for emergency supplies, thereby enhancing crisis management and response effectiveness.
The results indicate that after public health emergencies occur, compared to non-cooperative strategies between governments and enterprises, the government’s choice to collaborate with enterprises can enhance its efforts in managing the emergency. The decision of enterprises to opt for a cooperative strategy is influenced by several factors: (1)An increase inemergency supplies’ market prices may prompt enterprises to choose non-cooperative strategies.Therefore, the government should not only raise its procurement prices but also regulate emergency supply market prices at a macro level. (2)An increase in the number of enterprises producing emergency supplies does not necessarily mean an increase in production capacity, because these enterprises may engage in malicious competition to secure higher profits and limited raw materials for emergency supplies. Production capacity can only be improved when the number of enterprises that choose to cooperate with the government increases; otherwise, it might lead to a decrease in production capacity. (3)Raising tax coefficients and government procurement prices can incentivize enterprises to adopt cooperative strategies. Therefore, following the outbreak, the government should implement appropriate fiscal measures to encourage enterprises to choose cooperative strategies.
It is worth noting that this paper does not consider the condition for government subsidies on production costs to enterprises producing emergency supplies, nor does it account for enterprise actions such as donating or selling emergency supplies at low prices due to social responsibility following a public health emergency outbreak. Future research will address these aspects to optimize the existing model further.

Hazmat Multimodal Transport Network Design Based on Hub Risk Hierarchical Toll Setting

ZHANG Guanxiang, SU Guiping, XU Botong, ZHONG Huiling

2024, 33(5):  118-125.  DOI: 10.12005/orms.2024.0156

Asbtract ( )   PDF (1424KB) ( )  

References | Supplementary Material | Related Articles | Metrics

With the development of the economy, the demand for hazardous material transportation increases rapidly. Due to the advantages of better accessibility and economies of scale, multimodal transportation is widely used in the long-distance transportation of hazardous materials.During multimodal transportation, hazardous materials converge, stay and transit at hubs, which greatly increase the hub’s transportation risk. Therefore, in order to control the risk of multimodal transportation of hazardous materials effectively, it is necessary for government to issue hazardous material transportation permits to proper hubs and optimize the transportation network. We find that studies related to hazmat transportation network design (HTND) problem have the highest relevance to the research of this paper. The existing literatures generally control the transportation risk through a variety of policies and strategies, such as transportation banning, toll setting, flow diverting and speed limiting. These studies mainly focus on a single mode of transportation, especially road transportation, with less research on multimodal transportation. Moreover, the characteristics and constraints of hubs are nearly neglected by most of the research.
Based on the constraints on the location number and maximum tolerable risk of hubs, this paper proposes a risk hierarchical toll setting strategy to minimize the risk of hazardous material multimodal transportation network. Firstly, the strategies of hub location and hierarchical toll setting based on the hub risk level are introduced. Furthermore, according to the ALARP criterion, for each hub, there are three risk levels: acceptable, tolerable and unacceptable. Secondly, a bi-level model is formulated for the hazmat multimodal transport network design. In this model, the upper layer is a hub location and hierarchical toll setting problem for minimizing the total network risk and the total transportation cost, which considers the constraints on both the maximum number and the upper bound risk of hubs. Whereas, the lower layer is a transportation routing problem for minimizing the cost of each transportation event. Thirdly, in this paper, we use KKT conditions to convert the lower layer model to a series of equivalent constraints, linearize the nonlinear constraints in the model, and solve the single-layer bi-objective model after model transformation and simplification with a commercial solver Gurobi. Finally, under the context of road and waterway transportation of hazardous materials in the Chinese Pearl River Delta, numerical experiments are performed and discussed. We first conduct a sensitivity analysis of the maximum number of opening hub. We find that, with an increase in the maximum number of openings of hubs, the total network risk, the total transportation cost, and the average hub risk decrease and gradually stabilize. Second, we present a network design solution based on the hub risk hierarchical toll setting strategy and demonstrate that this strategy can reduce the network risk by regulating the flow of hazardous materials in routes. Third, we compare the network optimal solutions based on four strategies: no government regulation, full government regulation, transportation banning, and hierarchical toll setting based on the hub risk. And we identify the advantages of the hubrisk hierarchical toll setting strategy in balancing the hub risk, controlling the total risk, and reducing the transportation cost.
There are some related topics of this paper deserving further study. For example, it is well worth portraying the relationship between the government regulatory cost and the number of hub locations in the hazmat multimodal transportation network. Meanwhile, in addition to the government and carriers, which are two main participants of the hazardous material transportation in the existing studies, the perspective of the public should be added, and the impact of public’s risk controlling demand on hazmat transportation network design need to be considered.

Reliability of Complex Systems with Mutually Dependent Competing Failures

XING Mengxue, LI Yan

2024, 33(5):  126-131.  DOI: 10.12005/orms.2024.0157

Asbtract ( )   PDF (1441KB) ( )  

References | Supplementary Material | Related Articles | Metrics

In engineering applications, systems in operation generally experience multiple shock-failure modes and mutually dependent competing failure process(MDCFP) because of the variability of environments, and thus it is necessary to consider the mutual influence between degradation and external shocks. On one hand, shock arrivals not only increase the degradation amount of the system, but also its degradation rate with time. On the other hand, the ability of the system to resist external shocks also decreases with the gradual degraded performance of the system. In this circumstance, the system is more vulnerable to damage due to the external shocks. Meanwhile, with the development of intelligence, more and more systems have self-healing mechanism after shock-arriving. In the existing literature, some scholars have only studied an external shock impact on the system degradation, but have not investigated a system degradation impact on the ability to resist random shocks. And few literatures have focused on a self-healing system with MDCFP. Based on the above analysis and background, therefore it is necessary to propose a novel reliability model, which is a self-healing system with mutually dependent competing failure process (DMDCFP), to calculate the system reliability more accurately. It has a certain theoretical significance and engineering value to consider this kind of model and its reliability analysis. This model in this paper not only brings some tools for describing real phenomena, but also opens a new way to research the degradation model and shock model, and is a supplement of the reliability modeling theory. What’s more, our achievements can also provide a new idea for the design of highly reliable engineering systems.
Therefore, a reliability analysis method for the self-healing system is developed by MDCFP, including soft failure and hard failure. In this model, the total degradation consists of internal natural degradation caused by wear and corrosion, and damage produced by external random shocks. Shocks increase the system degradation increment and rate, and the system’s ability to resist shocks decreases as the system gradually degrades, which is reflected in the change in the hard failure threshold. Moreover, soft failure will occur when the total deterioration reaches a pre-supposed threshold value and hard failure will occur when a single shock load exceeds the pre-supposed threshold value. So, the research work is exhibited as follows in detail. First, a new degradation model for the system with the self-healing mechanism is established by using the mutually dependent competing failure theory of shock and degradation models, in which the threshold of hard failure decreases as shocks arrive, and the self-healing effect is expressed by a nonnegative monotone decreasing function. Then, the reliability models of sudden and degradation failure under the circumstance with or without self-healing are shown respectively, and system reliability functions are given by utilizing the probability theory and stochastic processes. Furthermore, the analytical expressions in the case without self-healing are also derived and obtained. For convenience of calculations, a flow chart for calculating multiple integrals is provided and the detailed algorithm is shown by using the Monte Carlo simulation to calculate numerical solutions. However, due to the complexity of the model with the self-healing mechanism, the analytical solution of the reliability function cannot be obtained directly, so the simulation solution of the system reliability is estimated by the Monte Carlo simulation method.
Finally, an engineering example of the Micro-Electro-Mechanical System (MEMS) developed at Sandia National Laboratories is used in this paper to illustrate the proposed model and methods. The system is subject to processes of wear and shock, and its evolution process can be deemed as a degradation-threshold-shock model considering both the degradation and shock. For the sake of authenticity, the performance parameters of the MEMS are cited from those previous works extensively studied. Base on the background of the MEMS, the validity and effectiveness of the proposed reliability model are verified, and the sensitivity of the system is also analyzed. From the result, we can see that numerical solutions are consistent with simulation solutions generally when computing system reliability without self-healing. Besides, the system reliability with self-healing is more reliable than that without self-healing, which matches the reality well. By the sensitivity analysis, the system reliability is related to the initial threshold and the threshold reduction of hard failure, where the greater the threshold reduction of hard failure is, the lower the reliability is and the higher the initial threshold of hard failure is, the higher the system reliability is. What’s more, different self-healing functions or changes in parameters in self-healing function also have a great impact on the system reliability. To sum up, all of the factors affecting system failure in this paper are essential and are in accordance with the reality. The method discussed in this paper is also applicable to the reliability evaluation of other products with self-healing mechanism.

Portfolio Selection of Smart Services Based on Multilinear Portfolio Utility Functions

YAN Yong, ZHANG Xinwei, WANG Shijia

2024, 33(5):  132-139.  DOI: 10.12005/orms.2024.0158

Asbtract ( )   PDF (1323KB) ( )  

References | Related Articles | Metrics

As an emerging type of service and value creation method, smart services driven by digital technology have gradually become an innovative development direction for service-oriented manufacturing. Companies utilize the vast amount of data generated by smart, interconnected products throughout their lifecycle, along with various big data analysis technologies, to form smart services driven by big data. These services play a significant role in enhancing customer satisfaction and increasing corporate performance.
When developing smart services, companies face the challenge of selecting a few from a multitude of potential smart services for further development. Solving this problem requires considering not only the decision-maker’s preferences but also the resource constraints of the company and various uncertainties, with the aim of maximizing the decision-maker’s preferences. If the chosen portfolio of smart services is not reasonable, it will inevitably lead to losses for the company.
Current research on smart service selection mainly utilizes multi-attribute decision-making methods to evaluate the weight of different services to support service selection. Portfolio decision analysis refers to the theory, methods, and practices of using mathematical models to help decision-makers select a subset of projects from a set of projects, taking into account preferences, related constraints, and uncertainties. The multilinear portfolio utility function can model richer decision-makers’ preferences in terms of multiple attributes, uncertain project outcomes, project interactions, and risk preference compared to the additive portfolio utility function. It also has the advantage of reducing the number of parameters that need to be identified to a linear level, thereby lowering the difficulty of applying the multilinear portfolio utility function. However, most existing research based on the multilinear portfolio utility function is based on deterministic information. In the context of smart service selection, the decision-makers’ preference information and resource information required for service selection often have uncertainties. How to conduct robustness analysis based on the multilinear portfolio utility function for these uncertainties in smart service selection requires further study.
The paper proposes a new approach for smart service portfolio selection based on the multilinear portfolio utility function. First, in the case of uncertain outcomes of smart services, when the utility evaluation information, decision-makers’ preference information, and resource requirements for smart services are deterministic, an optimization model based on the multilinear portfolio utility function will be constructed to solve for the optimal smart service portfolio. This includes the following steps: (1)obtaining utility evaluation information and resource requirements for smart services, (2)eliciting decision-makers’ preference information and determining the parameters of the multilinear portfolio utility function, (3)considering the computational complexity of the multilinear portfolio utility function, and transforming it into a general linear function, (4)taking into account the interaction relationships of substitutiveness and complementarity between smart services, as well as the interaction of required resources between smart services, and establishing various constraints for the optimization model, and then (5)based on the multilinear portfolio utility function and various constraints, establishing an optimization model. The above model is a mixed-integer linear programming model, which can be solved using a linear programming solver, and the solver used in this study is Gurobi.Furthermore, considering the uncertainties that may exist in decision-makers’ preference information, the utility of smart services, and the required resources, robustness analysis is conducted for the three types of uncertainties. In situations where preference information is uncertain, robustness analysis can be conducted by utilizing the obtained uncertain preference information and the hit-and-run algorithm. For situations where the utility of smart services is inaccurate, when the resources required for smart services are inaccurate, uncertainty sets for the utility of smart services and the resources needed for smart services should be constructed respectively, followed by robustness analysis. After portfolio optimization under the three scenarios, it is possible to obtain all possible portfolios of smart services and their frequencies of occurrence, and the frequency with which each smart service is selected, providing important references for decision-makers. Smart service portfolios and smart services with high occurrence frequencies can be considered as robust choices for decision-makers in subsequent decision-making; smart services with relatively low occurrence frequencies can be further analyzed in subsequent decision-making; smart services with extremely low occurrence frequencies can be considered as alternatives when resources are very abundant.
Finally, the proposed approach is applied to the problem of selecting a smart service portfolio for a heavy truck company. Through case analysis, it can be seen that this approach can quickly determine the optimal smart service portfolio based on known information. Moreover, it can provide multiple reliable portfolios and the selection status of each smart service under the circumstances where preference information is incomplete, and the utility and required resources of smart services are uncertain. Therefore, the proposed approach can effectively support decision-making in the selection of smart service portfolios.

Multiplicative One-switch Utility Functions

ZHANG Jia, XIE Jiehua, ZOU Wei, MA Zhipeng

2024, 33(5):  140-146.  DOI: 10.12005/orms.2024.0159

Asbtract ( )   PDF (1169KB) ( )  

References | Supplementary Material | Related Articles | Metrics

In financial economics and decision science, the utility function is one of the most important theoretical tools to describe the risk attitudes or the satisfaction degrees of individual decision makers, and it is also a basic tool for the decision analysis under risk and uncertainty. The research on the characteristic and properties of utility functions has been an important issue in the field of decision science and risk management. Under the framework of the expected utility theory, for any two lotteries, when the preference of the decision maker does not change as the wealth in these two lotteries increases, the forms of utility functions will be given explicitly. However, in reality, a decision maker could change his/her preference when the wealth in these two lotteries increases. Hence, the concept of one-switch in the utility theory is introduced, that is, a decision maker can change his/her preference for any two lotteries once at most as the wealth in these two lotteries increases. Meanwhile, the forms of one-switch utility functions are identified explicitly.
It is a natural task to consider the notation of multiplicative one-switch corresponding to the characteristic, of which a decision maker can change his/her preference for any two lotteries once at most when the wealth in these two lotteries is multiplied by the same amount. To the best of our knowledge, there is little literature tackling this problem. Our objective is to fill the gap and shed some light on the study of this problem. Under the expected utility framework, the concept of multiplicative one-switch in the utility theory is introduced, that is, a decision maker can change his/her preference for any two lotteries once at most when the wealth in these two lotteries is multiplied by the same amount. The four forms of multiplicative one-switch utility function are obtained explicitly. They are the quadratic function of logarithm, the logarithm times power function, the power plus power function and the logarithm plus power function. Then, a wealth of relationship among these four multiplicative one-switch utility functions and the decision maker’s risk attitudes is given. More specifically, the relations among the four multiplicative one-switch utility functions and the risk aversion, the relative risk aversion coefficient as well as the risk consistency are obtained, which provide practical application backgrounds for these multiplicative one-switch utility functions. Under the framework of the multiplicative one-switch property theory, the risk consistency means that when a decision maker changes his/her preference for any two lotteries due to the wealth in these two lotteries multiplied by the same amount, the preferred lottery should have a higher logarithmic expected value. Furthermore, the concept of multiplicative strong one-switch is further introduced. It means that the preference behavior of the decision maker has the multiplicative one-switch property toward any two compound lotteries. The form of utility function, logarithm plus power, satisfying the characteristic of multiplicative strong one-switch, is also verified.
The research in this paper enriches the contents of one-switch property in the utility theory and extends the existing notation of one-switch. The families of multiplicative one-switch utility functions and the form of multiplicative strong one-switch utility functions are given. The result obtained in this paper provides a theoretical basis for applying the multiplicative one-switch utility functions to the researches on the optimal investment, and provides theoretical tools for analyzing decision-making problems with the multiplicative one-switch property under risk and uncertainty.
The existing research on the one-switch utility property focuses on the univariate utility function. It is an interesting and important problem of extending the multiplicative one-switch notion to the multi-attribute case and identifying the families of multi-attribute multiplicative one-switch utility functions. In the future work, this problem will be further considered.

Application Research

Research on Profit Model Selection and Pricing of Capacity Sharing Platform

ZHAO Daozhi, ZHANG Shan

2024, 33(5):  147-153.  DOI: 10.12005/orms.2024.0160

Asbtract ( )   PDF (1211KB) ( )  

References | Related Articles | Metrics

In recent years, due to the rapid development of technologies such as the Internet of Things, big data, and cloud computing, along with the emergence of idle and excess resources, the “sharing economy” has seen swift growth as a new business model in the internet era. The advent of advanced concepts like Industry 4.0, cloud services, and cloud sharing has led to the rise in the “capacity sharing” model within the sharing economy. Capacity sharing primarily refers to an economic paradigm where internet platforms serve as the base, characterized by the sharing of processing equipment usage rights. It focuses on integrating and configuring diversified manufacturing resources and capabilities around each processing segment of the manufacturing process to maximize production efficiency in the manufacturing sector. The sharing of manufacturing capacity harbors significant opportunities and is set to become the main battleground for the future sharing economy. Our research primarily investigates the profit models and pricing decisions of intermediary-type capacity sharing platforms. Although there are studies on traditional platform pricing and sharing platform pricing, literature that simultaneously considers cross-network externalities and platform matching rates while choosing among platform profit models is still scarce. Therefore, our research constructs a two-stage game model to study and compare the profits obtained by capacity sharing platforms under different objectives within a targeted time frame when adopting transaction service fee models versus registration fee models. We explore the optimal profit model and its conditions, and analyzes the impact of cross-network externalities and platform matching rates on the platform’s maximum profit. The four scenarios considered in this paper are: (1)the platform aims to maximize its own profit by charging service fees only to suppliers; (2)the platform aims to maximize its own profit by charging registration fees to both suppliers and demanders; (3)the platform aims to maximize social welfare by charging service fees only to suppliers; (4)the platform aims to maximize social welfare by charging registration fees to both suppliers and demanders. Besides model construction and computation, this paper also conducts a numerical analysis for verification. The research in this paper draws three main conclusions:
(1)When the platform aims for profit maximization, under certain conditions of product price, cross-network externality coefficient, and matching rates, the platform can obtain greater profits by charging registration fees to both suppliers and demanders. Additionally, there are special conditions under which the platform would earn the same profit if it adopts a registration fee or service fee model.
(2)The platform’s maximum profit is positively correlated with the cross-network externality coefficient and the platform’s matching rate for suppliers, but negatively correlated with the matching rate for demanders. This is because the increased revenue brought by a higher matching rate for demanders is less than the loss caused by the reduction in the number of demanders. Therefore, platforms should focus on the matching rate for suppliers to achieve profit maximization.
(3)Without government subsidies, when the platform chooses a profit model that charges registration fees to both suppliers and demanders, it will need to provide certain subsidies to demanders at equilibrium, resulting in negative profits for the platform. In contrast, choosing a service fee model can yield greater profits.
The limitation of this paper lies in its focus on the choice of profit models for capacity sharing platforms within a certain time frame, without considering the long-term nature of the problem. If the long-term perspective is considered, companies might prefer a service fee model related to order volume to achieve maximum profit, as opposed to fixed registration fees. In studying the maximization of social welfare, this paper does not consider the impact of policy subsidies on platform behavior decisions. Therefore, future research related to the design of pricing mechanisms for capacity sharing platforms could take policy factors into account.

Research on Club Convergence in Throughput Growth of China’s Mainland Container Port System

LI Wanying, YOU Zaijin, SUI Yi

2024, 33(5):  154-160.  DOI: 10.12005/orms.2024.0161

Asbtract ( )   PDF (1191KB) ( )  

References | Supplementary Material | Related Articles | Metrics

Since China’s entry into the World Trade Organization (WTO) in 2001, the development of import and export markets has driven a rapid growth in container throughput in Chinese ports. Due to the fact that container ports serve as catalysts for regional economic development and important infrastructure for ensuring the smooth operation of international industrial and supply chains, sufficient attention and importance should be given to the construction and development of container ports.As far as container throughput is concerned, China’s ports have performed well, but due to the historical reasons for port development and the different natural resource endowments of each port region, the challenge of uneven port development in China always exists. Looking forward to the future, China will continue opening to the outside world and rely on the development of ports to trade with other countries. Thus, whether the container ports’ performance converges or diverges and how the development gap among ports would develop and evolve over timeare questions that bear significant implicationsfor port operationsand management.
Convergence is mostly used in the field of economics, referring to the phenomenon of narrowing the income gap between developing and developed countries. Club convergence is a special kind of convergence phenomenon, which means that economies with similar initial levels would converge under the effect of similar structural characteristics, so that multiple steady states coexist in the form of “intra-club convergence and inter-club divergence”. The natural conditions and resource endowments vary greatly among different regions in China, thus resulting in a certain degree of uneven development among container ports in various regions. In order to comprehend the disparity in development among ports, analyze potential factors and driving mechanisms that contribute to either the reduction or expansion of the port development gap, and investigate the underlying patterns of port development, the concept of convergence within a port system has been proposed. Club convergence in a container port system indicates that ports with similar initial conditions converge to the same steady state under the condition of similar factors affecting port development, while ports with different initial conditions are subject to different influencing factors and thus move towards different development directions and paths, thus converging to different steady states, which leads to the phenomenon of multiple port clubs coexisting in the port system.
On the basis of the concept of club convergence in a port system put forward, this paper aims to explore whether there is a phenomenon of club convergence in China’s mainland container port system in terms of the growth in port container throughput. Based on the container throughput data of 52 container ports from 2001—2020, a nonlinear time-varying factor model is utilized to investigate the unbalanced development in the port system, and during the process of the investigation, the convergence test is performed and the club clustering algorithm is applied to identify the club convergence in the container port system. Moreover, the potential factors that may affect the formation of port clubs are selected from dimensions of both the port initial conditions and port development characteristics based on the concept of club convergence. Further, the driving factors of club convergence and the convergence mechanisms are explored with the help of the Ordered Logit model. The main research results show that: (1)The container throughput growth does not lead to an overall convergence in mainland China, but four different clubs are formed. (2)The scale structure of China’s container port system is relatively concentrated, and the small and medium ports experience a stage of slow growth or even stagnation. (3)The initial conditions of a port, port city scale, foreign trade potential and industrial structure of the port city, and the growth rate of the traffic infrastructure may promote a port to a high-level club, but an economic and trade promotion is the original power for port development.
The identification of club convergence and the change in transitional paths of different port clubs in this paper show that the monopoly position of large ports will be further strengthened, and small and medium-sized ports may fall into the poverty trap, which has a reference value for port owners, managers and operators under both China’s both “one city, one port” mode and current provincial port consolidation mode (i.e.,“one province, one port”). For example, in the stage of “one city, one port”, small and medium-sized ports should focus on how to maintain their position instead of going for investment; in the stage of port integration, it is worth noting that excessive integration will make the monopoly position of large ports further strengthened and the lack of competition may also be detrimental to the development of small and medium-sized ports in each province. These are all worthy of consideration and also illustrate that the results of the club convergence study would have some policy guidance implications for container port development.

Multi-classifier for Car Review Sentiment Classification Based on ER Rule

ZHOU Mi, ZHOU Yajing, HE Yang, FANG Bihe

2024, 33(5):  161-168.  DOI: 10.12005/orms.2024.0162

Asbtract ( )   PDF (1238KB) ( )  

References | Related Articles | Metrics

With the rapid development of the next-generation information technology, more and more users are accustomed to sharing personal experience and opinions through the Internet, such as online reviews of book, movie, product usage experience and so on, which always contain positive and negative sentiment of users. Text sentiment analysis aims to use computer technology to detect and extract diverse sentiments, attitudes, opinions and other perceptual information in text documents, thereby converting qualitative user expressions into quantifiable data to serve decision-making and strategic planning. For users, these product reviews can provide them with sufficient information that will help them make informed purchasing decisions to the greatest extent and minimize the degree of regret after consumption. For manufacturers, consumers’ needs can be acquired timely through the reviews, thus adjusting their marketing strategies in a targeted manner and improving the design and quality of products. Currently, due to the exponential growth in the number of these review texts on the Internet, traditional manual analysis methods can hardly satisfy the rapidly changing market demand. Deep learning-based methods may fall into the dilemma of weak interpretability. Therefore, how to automatically obtain users’ sentiment information from numerous comments via a rational and intelligent way is a challenging issue.
　　For the problem of sentimental dichotomy on car commentary corpus, a text sentiment classification method based on ER rule multi-classifier fusion is proposed in this paper. Firstly, the research explores sentiment feature construction by examining the classification effects of various feature models, including unigram, bigram and unigram+bigram. The CHI Square test is adopted for text feature extraction. This method is particularly effective in managing high-dimensional feature spaces, facilitating more accurate sentiment classification by highlighting the most relevant features for analysis. Secondly, the improved TF-IDF method is proposed to enhance the discrimination of terms relevant to sentiment analysis. It incorporates the CHI Square values to assess the distinctiveness of terms across different document classes, and refines the traditional TF-IDF calculation. This adjustment accounts for the distribution of terms within categories, making the sentiment-related terms more impactful for classification tasks. Thirdly, on the basis of fully considering the weights and reliabilities of different classifiers, the ER rule is introduced to fuse multiple classifiers for text sentiment polarity analysis in order to integrate the advantages of different classifiers. Specifically, the classifier is regarded as evidence, and the weight of classifier is dynamically formed by the Euclidean distance between evidence and the difference in judgments of different categories within the evidence. The weight of a classifier is negative with the difference between the results of that classifier and those of all other classifiers, while it is positive with the discrepancy among the judgments of different categories within the classifier. Meanwhile, the accuracy of classifier is assumed to be reliability of the classifier, in order to produce better classification results.
　　In order to verify the effectiveness and rationality of the proposed method, the automobile review data set crawled from the network is used for verification. The result shows that the multi-classifier fusion method based on ER rule can achieve better results in text sentiment classification than single classification algorithm, ensemble algorithm and deep learning algorithm. In addition, to reduce the influence of contingency and single data set, the results are verified using original data sets of hotel comments published in other fields under the same experimental conditions. The experimental comparison results show that the fusion method based on ER rules achieves the best results in F1 value and Accuracy index, and also performs well in Precision and Recall indexes. So this method can be well generalized and applied to text sentiment classification tasks in different fields. At the same time, ablation experiments are conducted on the proposed improved method in terms of feature models selection and feature weights calculation. The experimental results show the effectiveness of the improved method in text sentiment classification performance. In summary, the ER rule considers both the weight and reliability of each classifier to fuse multiple classifiers, and integrates the advantages of different classifiers. The method can effectively reduce the classification limitations caused by different types and topics of text. The final sentiment classification results are stable and balanced, which has a wider applicability in the practice of sentiment classification.

Heterogenous Environmental Regulation and Regional Green Total Factor Productivity

GUO Xu, FAN Shilong, YUAN Fang

2024, 33(5):  169-174.  DOI: 10.12005/orms.2024.0163

Asbtract ( )   PDF (967KB) ( )  

References | Supplementary Material | Related Articles | Metrics

The traditional extensive development model of “high input and high emission” has worked an economic miracle since China’s reform and opening up, but it has also restricted sustainable economic and social development through ecological destruction, environmental pollution and resource shortage. Environmental protection policies are mainly divided into control command-type environmental regulation and market incentive type environmental regulation. The control command-type environmental regulation has low implementation cost, is mandatory, and produces good management and control results. However, a consequent increase in enterprise emission reduction cost may have a “crowding out effect” on productive investment, and it is difficult to effectively stimulate environmentally protection-oriented technological innovation behavior. Market-incentive environmental regulation promotes enterprises to weigh independently between emission costs and benefits through market mechanisms, and guides enterprises to carry out emission reduction technological innovation. However, such policies depend on government financial expenditure and regional institutional environment, which may lead to differentiated effects among regions. What impact different types of environmental regulation will have on regional productivity and how to measure regional green productivity more accurately in order to understand the nature of environmental regulation are the core issues to be solved in this paper.
Different from previous studies, this paper has improved the traditionally directional distance function, innovatively decomposed green total factor productivity into three dimensions of energy efficiency, output efficiency and emission reduction efficiency, and measured the environmental regulation level from the two aspects of control command type and market incentive type. Based on the inter-provincial panel data of China from 2003 to 2014, the Tobit model is used to test the influence of two kinds of environmental regulations on regional green productivity. The empirical data in this paper are collected from China Environmental Yearbook, China Industrial Economic Statistical Yearbook, China Science and Technology Statistical Yearbook and China Statistical Yearbook, and the relevant data of 30 provinces (autonomous regions and municipalities directly under the Central Government) in China from 2003 to 2014 are collected and aggregated to form a panel data set.
The empirical results from the overall sample show that the control command-type environmental regulation has a significant “nverted U-shaped” effect on green productivity. The appropriate regulation level can promote the improvement of green productivity, and the high regulation intensity will have a “crowding out effect” on productive investment. There is a “U-shaped” relationship between the market incentive environmental regulation and green productivity. When the emission collection standard is too low, enterprises will lack sufficient motivation to innovate and reduce emissions. Only when the emission cost reaches a certain level, can enterprises be forced to produce “innovation compensation effect”. According to the regression results of the interaction terms, there is an obvious correlation effect between the control command type and the market incentive type regulation policies.
Our further discussion shows that since the current policy direction of environmental regulation is to reduce pollution emissions, its effect on green productivity is mainly reflected in the improvement of emission efficiency, and has not been transmitted to industrial output and energy consumption. The phased regression from different periods shows that the effect of environmental regulation is only apparent after the promulgation of a series of policies represented by the 11th Five-Year Plan for National Environmental Protection in 2008. The heterogeneity test from different regions shows that compared with the central and western regions, the leading industrialization development level and market-oriented reform degree in the eastern region are more conducive to the effective play of environmental regulation policy effects.

Cooperative Strategies for Cross-border Environmental Governance Based on the Damage of Multiple Pollutants

LING Xingyuan, MENG Weidong, HUANG Bo

2024, 33(5):  175-181.  DOI: 10.12005/orms.2024.0164

Asbtract ( )   PDF (1092KB) ( )  

References | Supplementary Material | Related Articles | Metrics

Currently, atmospheric environmental issues are becoming increasingly complex, exhibiting regional and global characteristics. Ecological and environmental issues with regional characteristics such as haze and acid rain, as well as global characteristics such as greenhouse effect and climate change, have become key and hot issues in the field of ecological and environmental protection. Especially, currently a large amount of fossil fuels such as oil, coal, and natural gas are used in China’s energy consumption process, and the combustion of these conventional energy sources can produce multiple pollutants, causing differentiated environmental problems. On the one hand, the combustion of fossil fuels emits sulfur dioxide, and suspended particulate matter, etc., leading to increasingly prominent regional environmental problems such as acid rain, PM2.5 pollution, and haze pollution. Statistical data shows that haze pollution in China is mainly concentrated in North and Central China, especially in the Beijing Tianjin Hebei region and its surrounding areas. On the other hand, the combustion of fossil fuels emits chlorofluorocarbons (CFCs), nitrous oxide (N₂O), and other pollutants, which accumulate over a long period of time, making global environmental issues such as greenhouse effect, ozone layer depletion, and climate change increasingly acute.
Faced with the different damages caused by multiple pollutants, local governments should not only focus on regional ecological environment issues, but also pay attention to global ecological environment issues. However, relying solely on single pollutant control is no longer enough to solve the increasingly complex ecological environment problems. The diversification and complexity of atmospheric environmental problems urgently require the means of air pollution control to shift from single pollutant control to comprehensive control of multiple pollutants. Therefore, in order to deepen the battle against pollution and effectively respond to climate change, it is necessary to focus on synergistic efficiency enhancement, and strengthen the coordinated control of multiple pollutants, and regional coordinated governance. So this article focuses on solving the differentiated environmental problems caused by multiple pollutants, exploring pollution control strategies and their influencing factors, which helps to enrich existing ecological environment protection theories, expand the research perspective of regional synergy, and provide theoretical basis for China to accelerate the construction of a beautiful China and achieve the “dual carbon” goal. At the same time, it provides important support and reference for achieving high-quality development of the ecological environment, and provides reference for formulating and optimizing multiple pollutants control strategies.
In view of this, this article considers the realistic background of different damages caused by multiple (non-accumulative and accumulative) pollutants to the natural environment. With the help of the optimal control theory, an optimal control game model for cross-border pollution is constructed to explore environmental governance strategies between two adjacent regions in non-cooperative and cooperative governance situations, including the degree of pollution control efforts, and investigate the changes in cumulative pollutant stocks over time. The optimal solutions under the two governance models are compared and analyzed. The theoretical and simulation analysis shows that each region will consider the damage caused by its non-accumulative pollutant discharge to its adjacent areas under the cooperative game. The degree of pollution control efforts under cooperative governance in each region may be higher than that under non-cooperative governance. The total income of all regions is higher than that of non-cooperative management, and the cooperative surplus is not only affected by the stock of pollutants, but also by the impact of non-accumulative pollutant emissions on adjacent areas, but it has nothing to do with the impact of non-accumulative pollutant emissions on the source region. Finally, through a numerical simulation analysis, the validity of the model is verified, and the sensitivity of relevant parameters in non-cooperative situations is analyzed, which provides a theoretical basis for local governments to carry out joint prevention and control cooperation and pollution control. However, in the research process, this article does not take into account factors such as emission trading and joint execution (JI) mechanisms, and incorporating these factors into scientific research is very meaningful, which is also a direction for further research.

Research on Flexible SERU System Formation Based on Product

REN Yuhong, TANG Jiafu

2024, 33(5):  182-188.  DOI: 10.12005/orms.2024.0165

Asbtract ( )   PDF (1425KB) ( )  

References | Related Articles | Metrics

With the rapid development of information technology and economic globalization, the diversity of consumer preferences and the uncertainty of demand are increasing, making flexibility a key factor for manufacturing companies to maintain a competitive advantage. In order to achieve production flexibility, many companies have transformed their assembly lines into other production systems, and the SERU production method has emerged in this context. The SERU system is derived from the division and transformation of assembly lines, replacing conveyor belts and specialized automated machinery with movable workbenches, simple equipment, and hand tools, allowing for rapid and frequent construction, modification, dismantling, and reconstruction. Therefore, it is more flexible than traditional production methods. The construction of the SERU system is crucial for achieving flexibility and is also a focal point of research on the SERU production method. Currently, academic research on the construction of SERU systems mainly focuses on optimizing the operation process of SERU systems, emphasizing task-oriented SERU formation (TOSF). However, the strategic-level SERU system construction, as an important part of the construction problem, has received little attention. In addition, in terms of performance evaluation, existing literature mostly emphasizes the responsiveness to specific production tasks and focuses on operational evaluation metrics, which does not adequately reflect the adaptability of SERU systems to dynamic production environments. High flexibility, as the most important feature of SERU systems, has been rarely studied in terms of the flexibility performance of SERU systems. Therefore, studying the construction of flexible SERU systems at the strategic decision-making stage has important practical and theoretical significance.
This study focuses on the strategic-level construction of SERU systems, with a particular emphasis on the system’s flexibility performance. From the perspective of flexibility costs, the study utilizes flexibility investment costs (including formation costs and worker training costs) and flexibility loss costs (including opportunity loss costs and capability loss costs) to comprehensively evaluate the system’s flexibility level. A Flexible SERU System Formation Problem (FSFP) model is developed to comprehensively assess the flexibility of the system. The complexity of the problem is analyzed, and nonlinear models are equivalently transformed into linear models for precise solutions to small-scale problems. Through extensive numerical experiments, the performance of the FSFP strategy is compared with the TOSF strategy from the perspectives of demand scenario variations and cost parameter changes, confirming the effectiveness of the FSFP model. The research indicates that the FSFP model, considering the nature of dynamic demand changes, can generate different levels of flexibility based on demand variations, making it more suitable for dynamic demand environments. Furthermore, compared to the TOSF strategy, the FSFP model is more sensitive to changes in cost parameters, and as these parameters increase, the advantages of the FSFP model become more prominent. This study not only enriches the research field related to SERU production but also provides insights into optimizing construction methods to help companies leverage the high flexibility advantages of SERU systems.
As the first step in implementing the SERU production approach, the FSFP model based on product provides a high-quality solution that outperforms the TOSF strategy in situations of demand fluctuations. However, this study does not consider the dynamic SERU system formation that continuously adjusts internal configurations of the SERU system based on changing market demands, nor does it account for the internal structure within Serus. Combining the strategic-level SERU system structure with the capability reorganization at the operational decision-making stage to achieve relative system stability and optimize performance objectives for specific production tasks is a promising area for further research in the future. Additionally, considering dynamic adjustments in flexible SERU system formation is also an important research direction for the future.

Market Entry Strategies for Information Goods under Quality Differentiation

XU Guangye, LIU Hui, ZHOU Kaile

2024, 33(5):  189-196.  DOI: 10.12005/orms.2024.0166

Asbtract ( )   PDF (1228KB) ( )  

References | Supplementary Material | Related Articles | Metrics

As the digital revolution accelerates, information products have become an integral part of consumer demand and market competition. Within this context, quality differentiation among information products has emerged as a pivotal factor shaping consumer preferences and market outcomes. This study delves into the market entry strategies for information products under quality differentiation, aiming to provide a comprehensive understanding of the challenges and opportunities facing firms entering this dynamic market.
The research problem is defined as the identification and analysis of optimal market entry strategies for firms introducing new information products in a competitive environment where quality varies. The theoretical significance of this study lies in its potential to contribute to the understanding of strategic interactions between new entrants and incumbents, while the practical implications extend to informing firms of strategic decisions and improving their market positioning.
To address this problem, we develop a theoretical model that captures the dynamics of market entry under quality differentiation. The model incorporates consumer preferences for different quality levels and firms’ strategic interactions in pricing, promotion, and product positioning. We devise algorithms to solve for equilibrium outcomes, considering both the first-mover advantage and the response of existing competitors. The analytical techniques employed include econometric model, simulation, and comparative case studies.
The theoretical results of our study reveal that firms introducing high-quality information products tend to adopt a differentiated strategy, focusing on niche markets and building brand loyalty. Conversely, firms offering lower-quality products are more likely to compete on price and pursue a broader market segment. These strategies are shaped by consumer perceptions of quality, market conditions, and the competitive landscape. Our numerical results provide further validation, showing that firms successfully implementing quality-based market entry strategies have achieved higher market shares and profitability.
To illustrate the practical application of our findings, we present numerical examples of firms that have successfully navigated the information product market under quality differentiation. These examples demonstrate how firms have leveraged their quality advantages, responded to consumer preferences, and outmaneuvered competitors. The insights gained from these case studies offer practical guidelines for firms entering the market.
However, this study also highlights several areas for further research. Firstly, we recognize the need to explore the impact of consumer heterogeneity and changing market trends on market entry strategies. Secondly, the integration of emerging technologies, such as artificial intelligence and big data, in shaping consumer preferences and market competition remains an untapped area of investigation. Finally, the role of government regulations and policies in facilitating or constraining market entry under quality differentiation merits further scrutiny.
In conclusion, this study offers valuable insights into the market entry strategies for information products under quality differentiation. By combining theoretical model and numerical analysis, we provide a comprehensive framework for understanding the strategic interactions and market outcomes in this dynamic market. The findings of this study have the potential to inform firms’ strategic decisions, improve their market positioning, and ultimately enhance their competitiveness in the information product market.

Strategy Analysis of Incorporating the Third-party Credit Evaluation Mechanism into Rental Platform

LIU Yang, PAN Hong

2024, 33(5):  197-203.  DOI: 10.12005/orms.2024.0167

Asbtract ( )   PDF (1194KB) ( )  

References | Supplementary Material | Related Articles | Metrics

In the traditional rental mode, consumers need to pay the deposit for rental products, which limits the development of the rental platform. With the rapid development of big data technology, third-party credit evaluation organizations represented by Sesame Credit and Tencent Credit have developed. Based on the data resources owned, third-party credit evaluation organizations can evaluate consumers’ credit levels, which will provide support to the credit rental business mode, and also provide new business opportunities to the rental platform. This paper conducts a study on whether the rental platform should incorporate the third-party credit evaluation mechanism. Taking into account several factors, including the proportion of “rich” customers, the proportion of high credit customers and the credit evaluation accuracy, this paper constructs the profit models of the rental platform under the two strategies, incorporating and not incorporating the third-party credit evaluation mechanism. Then, by solving the models, the optimal product rental price, the platform’s profits under the two strategies are obtained. Further, by means of numerical simulation, this paper compares the optimal profits of the rental platform under the two strategies.
The research results show: (1)Under the credit rental model, the rental price is affected by the proportion of “rich” customers, the proportion of high credit customers and the credit evaluation accuracy. Therefore, the price strategy should be adjusted appropriately according to market conditions. (2)Based on the factors, such as the proportion of “rich” customers, the proportion of high credit customers and the credit evaluation accuracy, the rental platform should make strategies for incorporating the third-party credit evaluation mechanism. When the proportion of “rich” customers is high, the platform should not incorporate the third-party credit evaluation mechanism. When the proportion of “rich” customers is low and the proportion of high credit customers is high, the platform should incorporate the third-party credit evaluation mechanism. And, with an increase in the credit evaluation accuracy, the platform will gain more profit from the incorporation of the third-party credit evaluation mechanism.
In addition, this paper further studies the changes in social welfare under the two strategies. The research shows according to the size of social welfare, the government and relevant departments can regulate the incorporation strategy of the third-party credit evaluation mechanism of the rental platform. When the proportion of “rich” customers is low and the proportion of high credit customers is high, the government and relevant departments may adopt the method of providing financial subsidies or reducing taxes to the rental platforms, and encourage rental platforms to incorporate the third-party credit evaluation mechanism. When the proportion of “rich” customers is high, the government and relevant departments may restrict or regulate the incorporation of the third-party credit evaluation mechanism for the rental platforms by strengthening the supervision of the rental platforms or establishing punishment mechanisms.
This study provides a theoretical basis and reference for the decision-making of rental platforms and relevant government departments. But, this paper assumes that the credit evaluation accuracy is an exogenous variable, and further research can consider taking a contractual design approach to promote the third-party credit evaluation agency to continuously improve the evaluation accuracy.

Social Willingness to Pay for Reducing Rare Disaster Risk under Uncertainty

ZENG Huifang, ZHU Huiming, XIONG Peiyin, LI Bin

2024, 33(5):  204-209.  DOI: 10.12005/orms.2024.0168

Asbtract ( )   PDF (1102KB) ( )  

References | Related Articles | Metrics

Disaster policy design needs to weigh the costs and benefits of policy implementation. Social willingness to pay (WTP) is often used to evaluate the benefits of disaster prevention and control policies. This is because the larger the losses caused by disasters, the stronger the willingness of society to pay, and consequently, the greater the perceived benefits of these policies. Disaster probability and scale are two critical variables that characterize disaster loss. In analyzing the benefits of policies aimed at mitigating rare disasters, it is essential to explore how the probability and magnitude of such disasters affect socio-economic dynamics. Nonetheless, there is a significant uncertainty surrounding the consumption losses caused by rare disasters. Previous studies indicate that consumption losses from rare disasters typically follow a fat-tailed distribution. Therefore, this study examines the social willingness to pay to reduce both the probability and the impact size of rare disasters under the fat-tailed uncertainty.
To assess the benefits of rare-disaster policies under the fat-tailed uncertainty, we suppose the economic impact of a rare disaster follows a power-law distribution. However, the rate at which the probability density function tapers off to approach zero in the tail is slow compared to an exponential decay. This fat-tailed uncertainty is characterized by the tail index parameter, which defines the tail behavior of the impact distribution. Consequently, the objective of a rare-disaster policy is to expedite the slimming of the probability density function so it approaches zero in the tail more rapidly. We have developed a straightforward framework to explore how the tail uncertainty affects social welfare and to determine the extent of willingness to pay that society should be prepared to commit in order to mitigate the uncertainty associated with the economic impacts of rare disasters.
The simulation results reveal nuanced dynamics in social responses to disaster risks. As the probability of disasters increases, there is a corresponding rise in the social willingness to pay. Interestingly, while the overall WTP to address disaster risks grows, the WTP to specifically reduce the probability per unit of disaster actually decreases. This implies a declining marginal utility in investments aimed at reducing disaster probabilities. Conversely, an increase in disaster scale not only elevates the general WTP but also boosts the WTP per unit reduction in disaster scale, suggesting heightened sensitivity and concern as disasters grow in magnitude. In scenarios involving large-scale disasters, the society demonstrates a readiness to allocate significantly more resources to curb disaster impacts. Additionally, the study explores the effects of the fat-tailed uncertainty associated with the disaster scale on social WTP. The results indicate that the greater uncertainty in disaster scale leads to increased overall WTP. However, the WTP to reduce the uncertainty per unit of disaster scale diminishes, highlighting a strategic shift in prioritizing broader risk reduction over specific uncertainties as disaster risks escalate.
In this empirical study, consumption data from the World Bank are employed to evaluate the impact of the COVID-19 pandemic on global consumption across various countries and regions. We analyze the social willingness to pay for disaster prevention and control in both developed and developing nations. It is observed that the willingness to pay in developing countries exceeds that in developed countries. This suggests that the epidemic prevention and control policies should differ from developing countries to developed ones. Additionally, the study calculates China’s social willingness to pay for COVID-19 prevention and control using consumption data. Although China shows a higher willingness to pay than developed nations, it displays a lower willingness than other developing countries with similar human development indices. This could be attributed to China’s effective measures in controlling the epidemic’s spread, which have resulted in minimized consumption losses.
Global Risk Report in 2024 suggested that global threats would intensify, such as extreme weather events, adverse outcomes from AI technologies, interstate armed conflicts, and cyber insecurity intensification, so this article’s theoretical framework can be used to assess the benefits of policies designed to mitigate these risks.

Regional Integrity and Corporate Fraud: Evidence from Chinese A-share Companies

LU Chao, ZHAO Ziying

2024, 33(5):  210-217.  DOI: 10.12005/orms.2024.0169

Asbtract ( )   PDF (989KB) ( )  

References | Related Articles | Metrics

In recent years, there has been a surge in fraud incidents involving listed companies. The data released by the China Securities Regulatory Commission (CSRC) reveals that the number of violations by A-share listed companies increased from 116 in 2010 to 1485 in 2020, indicating a growing trend of non-compliance. In October 2020, the Opinions of the State Council on Further Improving the Quality of Listed Companies emphasized “raising the cost of violations of laws and regulations of listed companies and related entities, and strengthening law enforcement efforts to improve the operation standardization of listed companies”. Consequently, addressing corporate violations and enhancing the quality of listed companies has become a key focus for both academia and industry.
While existing research primarily focuses on internal corporate governance factors and external regulatory environment as influencing factors for corporate fraud, little attention has been paid to the impact of informal institutions. As a typical informal institution, integrity is widely recognized as a crucial social capital that determines a country’s economic growth and social progress in addition to physical and human capital. Regional integrity construction provides an environment for people to trust each other, reduces information asymmetry, and has a guiding and constraining function. Under this system based on social integrity values, enterprises are influenced by various internal and external factors which shape their business conduct.
From an informal institutional perspective, the paper empirically examines how regional integrity construction impacts corporate fraud, using A-share listed companies in Shanghai and Shenzhen from 2010 to 2019 as its sample group. By utilizing the China City Commercial Credit Environment Index (CEI) to measure the levels of regional integrity construction, we establish a Probit model,an Ordered-Probit model and an OLS regression model for empirical analysis.Our research findings indicate that regional integrity can significantly inhibit corporate fraud.We utilize the regional education level as an instrumental variable and employ the 2SLS method to address endogeneity. Additionally, we lag the explanatory variable by one period to mitigate the endogeneity issue. Furthermore, we employ methods such as altering the regression model, substituting the explained variable, and replacing the explanatory variable to bolster the robustness of our conclusions.Subsequently, we delve into investigating how regional integrity influences corporate fraud. Our empirical study reveals that the gender of senior executives, institutional investors’ shareholding ratio, audit quality, and media attention have a moderating effect on the negative correlation between regional integrity and corporate violations. Specifically, we find that regional integrity exerts a more significant inhibitory effect on corporate fraud in companies with a lower proportion of female executives, higher proportion of institutional investors, non-Big 4 auditors for hire, and greater media attention. Moreover, we analyze the influence path and find that regional integrity construction impacts corporate fraud through two pathways: alleviating corporate financing constraints and enhancing internal control quality.
The major contributions of this paper are as follows. First, this paper provides a new perspective and empirical evidence for the research on the factors affecting corporate violations. Different from the previous perspectives of internal corporate governance and external regulatory environment, this paper focuses on the positive effect of informal institutions, i.e., regional integrity construction,on restraining corporate fraud. Our research enriches the relevant literature. Second, this paper confirms that regional integrity plays an important role in corporate governance, and promoting regional integrity construction can significantly inhibit corporate violations. Our findings provide evidence from the micro firm level for the research about the economic consequences of informal institutions. Third, this paper deeply examines the influence mechanism and action path of the above relationship, which provides fresh insights into understanding how regional integrity system plays a role in corporate violations at the micro level.
This paper gets several policy implications. First, regulatory authorities should attach importance to the construction of integrity, promote the long-term and high-quality development of integrity construction, cultivate a good social integrity environment, and appropriately use informal systems such as integrity to supplement, support and enhance the formal systems. Second, social organizations should give full play to the role of social organizations as a link between the government and enterprises, cooperate with the implementation of government departments’ policies, cultivate a good business credit environment, and promote the self-discipline of enterprises. Third, enterprises can employ a reasonable number of female executives, introduce institutional investors, hire high-quality auditors and promote media attention to restrain the insider’behaviorand reduce corporate fraud.

Differential Game Models of Non-cooperation of Shareholders and Managers Based on Dismissal Threat

WANG Kaihong, WANG Ziyue, DING Chuan

2024, 33(5):  218-225.  DOI: 10.12005/orms.2024.0170

Asbtract ( )   PDF (1418KB) ( )  

References | Supplementary Material | Related Articles | Metrics

Information asymmetry, which leads to managers to manipulate corporate performance, adopt risky investment strategies, and focus too much on short-term interests, has been a major concern in academia. It also has an impact on the incentive compatibility of the contracts between shareholders and managers of a firm. In order to mitigate the agency conflicts, shareholders will design a series of mechanisms to motivate managers to choose a more suitable strategy for the development of the enterprise. The dismissal of the CEO for his poor performance is an important measure taken by the board. Since it not only results in the loss of dedicated human capital invested in the previous period, but also affects the managers’ perks, professional reputations, and even future employments, managers have strong incentives to avoid dismissal.
Several scholars have examined performance and non-performance factors affecting the dismissal of managers, indirectly suggesting that executive turnover has become a common phenomenon. However, because it is important to maintain policy continuity and consistency in corporate management, and the arrival of the successor may indicate changes in corporate future decisions: either correcting past mistakes or introducing policies reflecting the different views of the new manager.This releases different signals to investors and affects the market environment. Moreover, when managers anticipate their imminent dismissal, they are more likely to have “short-sighted biases” and place more emphases on short-term performances at the expense of projects that create long-term value for the firm, resulting in higher agency costs. Therefore, combined with the firm’s actual situation, analyzing the impact of executive dismissal on other decisions and corporate future development is significant in evaluating its effectiveness.
Based on the current research results, this paper describes the firm’s capital accumulation process from different perspectives, and introduces the dismissal mechanism on the basis of shareholders hiring a professional manager responsible for project investment. Aiming at the problem of non-cooperation between shareholders and the project manager in the presence of dismissal threat, we construct two-stage differential game models in a continuous time. The Nash equilibrium solutions of the game, which is the optimal efforts of the manager and shareholders, are obtained through the HJB equations and the optimal investment decision of the manager is solved by using the backward induction method. Besides, we discuss the influence of the dismissal mechanism on the above results and verify them by means of numerical simulations.
Our contributions are threefold. First, this paper describes the firm’s capital accumulation process from different perspectives. It takes into account both physical capital in the form of plants, equipment and also includes non-physical capital such as knowledge, skills, etc., which plays an important role in the development of modern enterprises. Second, when exploring the incentive role of dismissal for managers, this paper considers the attributes of the enterprise in production, operation and management, theoretically proving that the threat of dismissal will make managers put more efforts in the first period of the two-phase project under certain conditions. Third, considering the heterogeneity of enterprises in terms of the proportion of physical capital, management methods and investment strategies, this study provides reference for shareholders’ reasonable dismissal decisions and managers’ investment strategies.
The results of the study show that: (1)In the case where the enterprise parameter settings satisfy a certain condition, when the proportion of physical capital is greater than a threshold, the manager will choose a higher optimal investment ratio with the increase in initial capital, but the increase in dismissal probability will inhibit the enthusiasm of the manager for investing in the project; on the contrary, when the proportion of firm’s physical capital is less than the threshold, the increase in initial capital will have a negative impact on the optimal investment ratio, but with the rise in dismissal risk, the manager will invest more. (2)The reinvestment that the manager chooses will gradually decrease with the increasing proportion of physical capital in a certain range. (3)There is a positive correlation between the manager’s optimal effort in the first stage and the dismissal probability, which is unrelated to the share of the firm’s physical capital, so the dismissal mechanism can effectively motivate managers to work hard.
The follow-up study will attempt to relax the constraints on the relevant parameters and take into account the interactions between other variables and the probability of dismissal. Moreover, both incorporating the probability of dismissal as an endogenous variable in the model, and exploring the mechanism of dismissal’s effect on managers’ investment strategies as well as optimal efforts need further research.

Management Science

Research on Farmers’ Financing Mode Selection in Contract Farming Supply Chain under Government Intervention

CAI Zigong, YE Fei, LIANG Lunhai, XIE Zefei

2024, 33(5):  226-232.  DOI: 10.12005/orms.2024.0171

Asbtract ( )   PDF (1212KB) ( )  

References | Supplementary Material | Related Articles | Metrics

The issue of “difficulty with financing” for farmers is a significant bottleneck in the process of modernization of agriculture in China, directly related to the sustainable development of agriculture and increase in farmers’ income. To address this challenge, the Chinese government has implemented a variety of policies to facilitate smooth financing for farmers, among which the agricultural loan risk compensation policy is a notable example. This risk compensation policy has effectively lowered the financing threshold for farmers, providing them with initial funding for the cultivation of agricultural products. Consequently, it has been beneficial to enhancing the income of farmers. However, in agricultural supply chain finance, there are two main financing models, trade credit financing and bank credit financing, presenting farmers with the dilemma of choosing a financing model. The formulation of government policies also needs to consider the performance of policies under different financing models. Therefore, this paper focuses on exploring the following questions: (1)What is the impact of different financing models on the output of agricultural products? (2)Under government risk compensation policies, which financing model should farmers choose to optimize their income? (3)How does social welfare change under different financing models, and how can both farmers and the government achieve a win-win situation?
To address the above problem, this paper constructs a government-intervention “company+farmer” type order agriculture supply chain financing model based on the characteristics of government risk compensation and the stochastic output of agricultural products, analyzes and compares the impacts of bank credit financing and trade credit financing on farmers’ production decisions and social welfare.
The managerial implications of this paper include: (1)Under different financing models, the impact of government risk compensation policies on farmers’ expansion of planting scale varies. In trade credit financing, government guarantees can reduce corporate borrowing risks, and encourage enterprises to increase their purchase prices and quantities of agricultural products. However, in the bank credit model, while government guarantees also reduce the borrowing risk for banks and lower loan interest rates, this may lead to a decrease in contract purchase prices. Nonetheless, the lower loan rates reduce the production costs for farmers, facilitating an expansion in planting scale. (2)Banks’ risk aversion leads to higher loan interest rates and purchase prices, increasing the planting costs for farmers, which might suppress the expansion of planting scale. Compared to trade credit, bank credit can better help farmers transfer risk and expand their planting scale when output risk is higher.(3)Governments with ample budgets should prioritize promoting bank credit to increase farmers’ income and social welfare. Under smaller guarantee intensities and lower risks, the government should guide farmers to choose the appropriate financing model based on the size of output risk and provide accurate meteorological information to support decision-making.
Future research plans will delve into the specific impact of the initial capital size on farmers’ production decisions, and analyze how different levels of initial capital affect the determination of agricultural production scale and government decision-making behavior. Furthermore, considering that farmers’ attitudes towards risk significantly influence their production behavior during the decision-making process, future research will also incorporate farmers’ risk aversion into the analytical framework of the model.

Study on Influencing Factors of Joint Management and Maintenance Performance of Rural Irrigation Facilities: An Empirical Analysis of 104 Nature Villages in Heilongjiang Province

CHEN Huaiyu, ZHANG Ziyuan, ZHANG Yulin, RONG Dongsheng, WU Ling

2024, 33(5):  233-239.  DOI: 10.12005/orms.2024.0172

Asbtract ( )   PDF (1030KB) ( )  

References | Related Articles | Metrics

In May 2022, the General Office of the Central Committee of the Communist Party of China and the General Office of the State Council issued the Programme for the Implementation of Rural Construction Actions, stressing the principles of “guiding and motivating the participation of farmers” and “integrating the promotion of construction and care”. How to reach a collective action of public facilities management and care, so as to avoid the “tragedy of the commons” has always been an important issue in the field of public resources governance set. This study focuses on rural irrigation facilities and explores the factors affecting farmers’ collective care behaviour through empirical analysis. This study innovatively applies evolutionary game theory to formulate and empirically test hypotheses on factors affecting the realisation of collective care of irrigation facilities. The results of the empirical research are of great significance in guiding farmers to voluntarily participate in the joint management and care of public resources, giving full play to the main role of farmers, realising the effective governance of public resources, and promoting the action of rural construction.
This paper proposes three hypotheses on the factors affecting the realisation of joint care of agricultural irrigation facilities based on mathematical models and evolutionary game theory. One is that it is more difficult to achieve joint care of irrigation facilities in villages with non-farm employment opportunities compared to villages without non-farm employment opportunities. Second, the probability of joint care becoming an evolutionarily stable strategy will be the greatest when incomes among farmers are equal. Third, the effect of village size on reaching joint care of irrigation facilities is uncertain. In this study, two towns (Hongkeli Township and Dalianhe Township) and two townships (Yinglan Korean Township and Yugong Township) in Yilan County, Heilongjiang Province, are selected as the survey area, and 104 natural village leaders in the four townships are randomly selected as the interview subjects for the questionnaire survey. This study uses the Tobit regression analysis model to test the hypotheses.
The results show that: (1)Joint labour opportunities have a significant positive effect on the probability of reaching joint stewardship. (2)Degree of non-agriculturalisation has a significant negative effect on the probability of reaching joint stewardship. (3)The proximity of agricultural income promotes farmers’ participation in stewardship. (4)The relationship between village size and the probability of achieving joint care of irrigation facilities is inverted “U” shape.
Based on the results of the study, the article suggests the following policy implications: improving the punishment mechanism for “free-riding” in various types of common labour, forming a selective incentive system, reducing the difference in farm income between small-scale farmers and large-scale operation, cultivating an ideological system in small-scale villages, and realizing the visibility of individual contributions in large-scale villages are the key to achieving the common management and care of irrigation facilities.