Table of Content

25 January 2024, Volume 33 Issue 1

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Theory Analysis and Methodology Study

An Auction-based Negotiation Mechanism to Distributed Multi-skilled Multi-project Scheduling Problem

YOU Weibao, XU Zhe, LIU Dongning

2024, 33(1):  1-8.  DOI: 10.12005/orms.2024.0001

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Economic globalization has promoted the development of multi-project distributed management and its management concept, and advanced information technology provides fast communication services between managers through the Internet, making distributed multi-project scheduling optimization possible. The distributed resource-constrained multi-project scheduling problem (DRCMPSP) is a project scheduling problem that studies the integrated optimization of single project scheduling and global resource coordination allocation in a distributed decision-making environment with multiple independent decision-makers and asymmetric information in multi-project scheduling. When the global resources are multi-skilled human resources, the distributed multi-skilled multi-project scheduling problem (DMSMPSP) is formed. As an extension of DRCMPSP, DMSMPSP involves the matching relationship between “activities-skills-resources” and the multi-skilled heterogeneous characteristics of global human resources in the global resource allocation process, which further increases the complexity of scheduling problems and the difficulty of research. Through the research of this paper, the depth and breadth of theoretical research on distributed multi-project scheduling problems are further strengthened. In practice, it provides a decision basis and methodological guidance for project managers to carry out multi-project scheduling in a distributed decision-making environment. Given the characteristics of the research problem, a two-layer model integrating local scheduling optimization and global coordinated decision-making is established based on the multi-agent system. In the local scheduling optimization model, each local decision-maker independently schedules the managed projects with the optimization objective of minimizing the completion time of single project. In the global coordinated decision-making model, the global decision-maker achieves the optimization goal of minimizing the multi-project total delay cost through reasonable allocation of human resources.
In addition, the local scheduling problem can be regarded as the classic resource-constrained project scheduling problem. A genetic algorithm based on forward-backward scheduling improvement is used to solve the local scheduling problem and generate the initial local baseline scheduling plans. The global coordination decision-making level primarily addresses the assignment of global multi-skilled human resources. An auction-based negotiation mechanism that takes into account the multi-skilled heterogeneous characteristics of human resources is designed. At each decision point, the global resource conflicts among multiple projects are coordinated through six stages: preparation stage, bid generation stage, temporary winner determination stage, bid modification stage, final winner determination stage, and local plan adjustment stage. We adapt the MPSPLIB instance sets and conduct experimental research. The research results indicate that the designed auction-based negotiation mechanism combined with the improved genetic algorithm can effectively coordinate the global resource allocation for problems of different sizes. Moreover, compared with the well-performing sequential game negotiation mechanisms in existing literature, the auction-based negotiation mechanisms can achieve lower average project delays, multi-project total durations, and multi-project total delay costs on most problem sets. Compared with the optimal solution obtained by the branch-and-bound algorithm, it indicates that the improved genetic algorithm can obtain high-quality initial local scheduling results. Furthermore, as the problem size or the intensity of resource conflicts increases, both the multi-project total delay cost and the number of auction rounds increase, indicating that the coordination and scheduling of multiple projects become more difficult, and the allocation process of human resources more complex.
In multi-project scheduling practice, the availability of global resources is often uncertain due to various unexpected and uncontrollable conditions, such as equipment failure. The distributed multi-skilled multi-project scheduling problem with uncertain global resource availability can be further investigated in the future. Moreover, the skill level of human resources is a dynamic concept, and the impact of learning or forgetting effects on the skill level of human resources can be further considered in research problems.

MAS-based Multi-mode Distributed Resource-constrained Multi-Project Scheduling

ZHANG Haohua, BAI Sijun

2024, 33(1):  9-15.  DOI: 10.12005/orms.2024.0002

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With fierce competition in the market, multiple projects are gradually decentralized both geographically and organizationally. Enterprises need to ensure that each project is performed and the limited shared resources are appropriately allocated to each project. Therefore, for this problem of multi-project management, the distributed resource-constrained multi-project scheduling problem (DRCMPSP) is proposed. However, the current research on the DRCMPSP mainly considers the case where the resource requirement and completion time are for a single activity mode, ignoring the reality that project activities often have multiple execution modes. Therefore, based on the multi-agent system (MAS), this study considers different execution modes of activities and realizes the systematic coordination of distributed multi-project scheduling using a two-layer algorithm. The DRCMPSP is not only closer to the actual situation of distributed multi-project scheduling but also can effectively reduce makespan tardiness, resource idleness and project interruptions,and thus improve the efficiency of distributed multi-project management in enterprises.
The DRCMPSP consists of multiple single projects, each with a different arrival time. First, the project agent (PA) makes project scheduling decisions independently to minimize the project makespan, essentially the multi-mode resource-constrained project scheduling problem (MRCPSP). Therefore, this paper proposes an improved variable neighborhood search (MVNS) algorithm to solve the local scheduling problem. Second, based on the local scheduling results, each PA submits the global resource requirements and the mode information of the corresponding activities to the coordinate agent (CA). Due to the limited global resources and the self-interested tendency of the agent, in order to minimize the makespan, it will prioritize scheduling the activities according to the mode with the shortest activity duration to obtain more global resources earlier. Therefore, the negotiation mechanism based on mode adjustment is proposed in this paper. CA minimizes project disruption and improves resource utilization by mode adjustment of some activities in the original schedule. Finally, PA makes corresponding adjustments to the original scheduling plan and thus obtains the final distributed multi-project scheduling plan.
To verify the effectiveness of the MVNS algorithm for localized scheduling and the global negotiated scheduling algorithm based on mode adjustment, we test the algorithms based on the MRCPSP set of instances in the PSPLIB library. The results show that the MVNS algorithm is computationally faster and has better scale adaptation, and the computational results are better than most of the algorithms published in PSPLIB. The mode-adjustment-based negotiation mechanism results in an average saving of 38% and 23% in the total cost of deferral (TTC) compared to the two cases of randomly determining a mode and mode-adjustment only. The average project delay (APD) is reduced by an average of 18% for the mode adjustment-based negotiation mechanism compared to the randomized mode case. In contrast, the APD is not reduced on average compared to the mode adjustment-only case. As the size of the arithmetic example increases, the competition for global resources among projects intensifies, and the total project extension cost gradually increases. However, the total project extension cost increase can be effectively reduced by model adjustment and prioritizing project activities with high extension costs.
Adjusting multiple modes of activities also contributes to improving project robustness under unstable resource supply. Future research will establish multi-project robustness measurements and construct an integrated optimization model for global resource allocation and robust scheduling of distributed multi-projects under resource uncertainty. In addition, resources between distributed multi-projects need to be transferred several times, and some corresponding costs are often incurred. Therefore, decentralized multi-project scheduling considering resource transfer cost, multi-project extension cost, and robust multi-objective optimization is the next research direction for this paper.

Research on Licensing Pricing Strategy of Dual-use Defense Patent under the Asymmetric Demand Information

YAN Fei, CHEN Hongzhuan

2024, 33(1):  16-22.  DOI: 10.12005/orms.2024.0003

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The transfer of dual-use defense patents from military to civilian fields has been increasingly regarded as the key path to deep integration of both military and civilian fields. In the context of the rapid development of high-tech technologies and products, it is even harder to predict the demand market. Based on the sensitivity difference of market demand information between military and civilian fields, the asymmetry of information between the two sides has arisen.Only by adopting different license methods for different demand markets can military research institutions distinguish between different types of demand markets, avoid false reporting of demand information by civilian manufacturers, and ensure sustainable and reasonable income. This can also enhance the source of motivation for military research institutions to transfer dual-use defense patents, and promote the virtuous cycle of the entire dual-use defense patent license industry.
Under the background of asymmetric demand information between military defense patent owners and civilian manufacturers, the military research institutes license the declassified defense patents to civilian manufacturers. In the mass market and opportunity market, the military licenses the national defense patents to the civilian manufacturer with three licensing modes: equity participation, equity-royalty and equity-royalty-fixed fee, respectively. Three kinds of licensing pricing strategy information screening game models are constructed, and the influence of these three licensing pricing strategies on the military research institute and civilian manufacturer is compared and analyzed. Besides, we also analyze the impact of each licensing pricing strategy choice on both the military research institutes and civilian manufacturers.
It is found that under the condition of asymmetric demand information, the technology licensing pricing method of equity will only lead to the civilian manufacturer’ misrepresentation about the market demand information, and the military research institute will suffer from failure to obtain effective demand information from the civilian manufacturer; although the equity-royalty strategy in the technology licensing mode will lead to the distortion of production quantity and profit of the civilian manufacturer, it can make the military research institute obtain more market demand information from the civilian manufacturer, and reduce the false information of the civilian manufacturer in order to obtain a larger profit share. At the same time, when the military thinks that the market demand is a mass market, the royalty strategy reduces the probability of opportunity market being excluded, thus reducing the loss caused by information asymmetry; the technology licensing method of equity-royalty is better than that of equity-royalty-fixed fee licensing pricing strategy, because fixed fee licensing strategy increases the probability of opportunity market being excluded, thus increasing the loss caused by information asymmetry.
Firstly, in practice, with the marketization of defense patent licensing, military research institutions may choose to license the defense patents to two or more third-party manufacturers, which may lead to more complex and intense price competition in the market. Therefore, in future research, military research institutions need to make real-time adjustments to the optimal licensing pricing strategy based on the situation of the transferee and market competition. Secondly, with the development of defense patent licensing pricing practices and theories, there will be more models for defense patent licensing pricing, pledge financing, milestone payment, buyout license, and technology auction. Therefore, more licensing pricing models should be explored based on practice.

Three-way Decision Based Grey Possibility Clustering Approach and Its Application

DU Junliang, LIU Sifeng, LIU Yong, LI Zhiyuan, ZHANG Weiliang

2024, 33(1):  23-28.  DOI: 10.12005/orms.2024.0004

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As an effective method to deal with the “small sample, poor information” clustering problem, the possibility function-based grey clustering evaluation approach is one of the important research contents of grey system theory. The possibility function-based grey clustering evaluation approaches mainly includes grey variable-weight clustering evaluation model, grey fixed-weight clustering evaluation model and grey clustering evaluation model based on mixed possibility function. However, the classic grey clustering evaluation model has problems such as low distinguishability of several components of the decision coefficient vector. In addition, its improved models still have problems such as more cumbersome calculations and low distinguishability and error tolerance.
Aiming at the problem of the grey possibility clustering model that it is difficult to determine the grey class ascription of decision objects and excessive clustering, based on the thought and method of three-way decisions, by introducing the concept of three-way grey class, it can describe the uncertain clustering relationship between the decision object and the grey class. The three-way grey class replaces the grey class and strict clustering relationship in grey possibility clustering, a grey possibility clustering method based on three-way decisions is constructed, and Bayesian reasoning in decision-theoretic rough set is used to determine the clustering thresholds. Finally, an example is used to verify the effectiveness and rationality of the proposed method. Compared with the classic possibility function-based grey clustering evaluation approach, The model constructed in this paper can provide more clustering information, and to a certain extent solve the problem of the balanced value of each component of the grey clustering coefficient vector or the problem that the grey clustering coefficient vector has several leading principal components with similar values, making it difficult to determine the ownership of the decision object. Therefore, excessive clustering can be avoided, decision-making risks can be reduced, clustering reliability and fault tolerance can be improved, and decision-makers can be provided with more detailed decision-making references. At the same time, classic grey fixed-weight clustering, grey variable-weight clustering and grey clustering based on mixed possibility functions are special cases of the model constructed in this article, and the proposed model is an extension and generalization of these grey clustering methods.
Product decision-making is the process by which a company determines which product (product combination) will meet the needs of the target market and launch the product in the future based on market sales results and the company’s own specific conditions. Therefore, product decisions are of great significance in business operations. We apply the constructed model to the solution of enterprise product decisions. For certain clustering results, enterprises can make decisions directly; for those uncertain clustering results, enterprises need to obtain more market information about these products or adjust the possibility function to further classify them into certain categories. This can effectively reduce decision-making risks.
Most real-world decision problems are dynamic, in the sense that the final decision is temporarily taken in a time cross-section of some constantly explored processes. In this process, decision-making objects, decision-makers, decision-making methods, evaluation standards, weights, decision-making information systems, etc. may change with changes in the environment, which may ultimately affect the clustering results. In view of this, dynamic grey three-way clustering evaluation is one of the possible future research directions.

Integrated Optimization for Train and Wagons in Railway Terminal with Multiple Marshalling Yards

LI Bing, CHEN Xiaoyue, XUAN Hua

2024, 33(1):  29-35.  DOI: 10.12005/orms.2024.0005

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Railway terminal is generally located at the intersection of multiple railway main lines and branch lines, consisting of marshalling yards, loading and unloading station, linkage lines and some transportation service equipment, which is an important part of railway transportation network. It mainly undertakes the departure and arrival of wagon flow from railway network, pickup and delivery of local wagon flow, and efficiently realizes the goal of transship among train flow, wagon flow, and cargo flow. The wagon flows in the railway terminal mainly include through wagon flow, local wagon flow arriving with inbound train, transship wagon flow arriving with inbound train, and local wagon flow departing with outbound train. They share the technical equipment and resources in railway terminal. Therefore, the reasonable work division of marshalling yards can directly reduce the detour routing and repeated operation of wagon flow in the terminal. And the unreasonable pickup and delivery of local wagon flow will drop. The various technical operations arisen from railway terminal will appear more coordinated. The efficiency of the railway transportation network will be promoted.
Marshalling yards are the hub of railway terminal. In large railway terminal, there are often many railway lines connecting to different railway directions. And the pickup station delivery station of local wagon flow is scattered. The direction of outbound train coupling transship wagon flow is complicated. To meet distribution rule of wagon flow, avoiding generating angular wagon flow to increase the workload of railway terminal capacity, two or more marshalling yards often are arranged into the railway terminal. The vast majority of large railway terminal around the world is equipped with two or more marshalling yards. Although the number of marshalling yards in railway terminal is relatively small in China, there are still more than 20 railway terminals equipped with two or more marshalling yards.
The integrated optimization for train and wagons in railway terminal with multiple marshalling yards is studied. It is framed as a mathematical model that incorporates certain constraints which reflect the railway directions connecting marshalling station, handling stations connecting marshalling station, the capacity of train arrival-breakup-accumulation-makeup-departure, and the capacity of transshipping. It intends to minimize the overall cost of inbound train moving, wagon reorganization and accumulation, and local car shunting. According to the specific structure of the programming model, an integrated approach with two stage combining greedy generating procedure and asynchronous iteration heuristic is described as IA-TS. Firstly, a greedy procedure is proposed to arrange the destination marshalling station of the main wagon group to the train. Then the scheme matching the train and marshalling station is updated with the secondary wagon group which is ranked second in number of wagon groups. And then the matching scheme is verified with the capacity of train arrival, train breakup, wagons accumulation, train makeup, train departure, and wagons transship. So, the feasible matching scheme set can be generated. Secondly the matching scheme is coded using the natural integer sequence. An interactive-inherent updating procedure of filtered matching set is offered to discover the best matching scheme. The performance of the proposed approach is evaluated and compared to other algorithms via the testing of different-sized test cases.
In this study, the multiple marshalling yards is homogeneous in the railway terminal. They are not classified into main and auxiliary marshalling yards. The future research will focus on the railway terminal under heterogeneous operation of the main and auxiliary marshalling yards.

Study on R&D Innovation Models and Strategies of Battery Plants and Auto Companies under Green Credit

LI Jiangxin, LI Jizu, WU Yucheng

2024, 33(1):  36-42.  DOI: 10.12005/orms.2024.0006

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For new energy vehicles, in addition to power battery innovation, whether the motor and electric control can have the best performance will also directly affect the range of pure electric vehicles.In recent years, with the continuous decline in subsidies, the power battery and motor markets have been strongly impacted by foreign forces, and there is also a phenomenon that high-end core components in the electric drive market are in the hands of foreigners. In such a severe environment, automobile companies are adopting differentiated competition strategies to cultivate their core competitiveness while also “huddling together to warm up”, with a view to reducing overall industrial costs and forming an industrial agglomeration effect through upstream and downstream collaborative innovation. In the context of the above, automotive companies will face two R&D models: one is to cooperate with upstream battery manufacturers to improve mileage and revenue, and the other is to focus on itself, improving collaborative control technology and environmental management level that leverage the battery,so as to reduce process costs and increase profits. Different R&D models will have different impacts on the innovation and development of new energy vehicles. Therefore, for automotive companies in fierce competition, the choice of research and development models is very important. At the same time, in the context of automobile enterprises cultivating their core competitiveness, the roles played by both automobile enterprises and battery factories in improving automobile performance are equally important. Therefore, for battery manufacturers and automotive companies that have reached cooperative research and development intentions, who will lead the research and development also affects the investment in research and development.In addition, the gradual improvement of the green financial system has made green credit an essential source of funding for innovation in the new energy vehicle industry chain. However, existing research on new energy vehicle innovation mainly focuses on “subsidized” policies, and the role of green credit in innovation has not been taken seriously.
To sum up, at the key point of accelerating the transformation of the automotive industry, in the face of multiple factors such as the lack of effectiveness of the dual credit policy and the large demand for research and development funds, which research and development model should automobile enterprises adopt based on the use of green credit to effectively promote the development of new energy vehicles has become an urgent issue to be resolved. Based on this, the article, supported by green credit, constructs a game model of single R&D led by a battery factory or car company, as well as independent R&D by both parties. By analyzing the R&D investment and innovation effect in each situation, the paper compares different R&D innovation modes and strategies. Finally, based on a comprehensive analysis of the data obtained from surveys and model numerical analysis, the research results indicate that green credit enhances the R&D innovation effect, and the innovation effect and its added value are the largest when both parties conduct independent research and development, while the innovation effect and its added value are the smallest when the battery factories takes the lead in R&D. Therefore, whether from the perspective of social benefits or innovation effects, the single research and development model led by battery factories is not an ideal strategy, or cannot maximize the promotion effect of green credit. When battery factories or automobile enterprises dominate R&D, green credit can always encourage automobile enterprises to increase investment, and only in the cooperative state, green credit can promote battery enterprises to increase R&D investment. When both parties conduct independent research and development, green credit only increases the R&D investment of vehicle companies, but has no impact on the R&D investment of battery factories. In addition, in a state of non-cooperation, the popularity of green credit reduces the enthusiasm of individuals for increasing R&D investment.
The deficiency of this paper is that, in order to facilitate research, this article discusses R&D models and strategies in a non-competitive environment. In fact, new energy vehicle companies are facing significantly competitive pressures in the market. In a follow-up study,we will further expand the model and discuss the impact of introducing competition on the R&D mode and strategy of automobile enterprise. In addition, this article only considers the financing method of green credit.In future studies, we will consider the supply chain financing mode and compare the differences in the influence of different financing models on R&D cooperation models and strategies of new energy vehicle battery factories and vehicle enterprises, hoping to further expand the existing research.

Data Pricing Decision of Health Data Supply Chain Considering Monetization of the Data Shared by the Public

JIA Junxiu, WANG Chen, WU Tao, CHEN Shaohua

2024, 33(1):  43-50.  DOI: 10.12005/orms.2024.0007

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The contradiction between the huge potential value of health data to improve the public health and the few available health data results in the presence of the platform for health data trading. At the same time, comprehensive health data have become an important management asset of Health Data Trading Platform (HDP). The first problem faced by the platform in the operation process is how to encourage the public to share data. After collecting health data through monetary incentives, HDP will consider how to realize the data value by providing data for data demanders such as insurance companies, pharmaceutical companies, health service organization and so on.In practice, the public pays different attention to privacy, and will share data and control the privacy levels according to the utility. And various types of data demanders need data with different privacy levels for their special purposes. Hence, HDP has to deal with how to determine the monetary return to the public who provide data according to privacy levels, and the data price sold to data demanders based on the order quantity, privacy level and matching degree. Because the pricing of health data is a new research field and most of the data trading platforms have just started, these pricing problems have not yet been systematically and scientifically answered in practical operation and academic research.
Taking the hard questions above into account, this paper first gives relevant literature on data pricing, data value mining and service pricing, and summarizes the previous research’s contribution and the challenging work at the moment which urgently needs resolution. Combined with practical commercial cases, the health service supply chain system is composed of the public, the health data trading platform and the data demanders. Meanwhile,this paper focuses on some important decision variables in the system such as monetary return, platform data selling price including fixed price and variable price and data demand quantity. Considering the data value and the privacy level of health data, the paper applies the principal-agent and incentive mechanism theory to present health data pricing and the monetization of public shared data in a supply chain with a HDP and data demanders. Based on the innovation on the data value function, the data pricing models are built for analyzing fixed and variable prices and the optimal pricing strategies are discussed in detail. We also establish the decision models of monetization of public shared data affected by privacy levels, the platform’s data processing ability, the matching degree of supply and demand data and the value increment coefficient on the decision variables.
The findings are as follows: (1)Health data trading platform can set the two types of optimal price for data according to different privacy levels. (2)The optimal decision-making of the platform’s monetary returns to the public is determined by the privacy cost rate, which is positively correlated with the matching degree of supply and demand data under certain conditions, and negatively correlated with the platform’s data processing ability. Under certain conditions, the data demand will increase with the improvement of the platform’s data processing ability and the matching degree of supply and demand data. (3)Given the data value function, data demanders have the optimal ordering quantities under two privacy cases, which are determined by the platform’s capability of data processing and matching degree. (4)Moreover, we obtain the following observations from corresponding numerical analysis. (a)The public can always get more monetary returns when sharing high privacy data compared with those sharing low privacy data. (b)The fixed price of low privacy data first decreases and then increases with the enhancement of platform data processing capability.
The achievement of this paper lies in its essential definition of the data value in the frame of a data supply chain, which is affected by every member in the system, giving the quantification methods of monetary incentives for the public’s sharing data,and expanding the consideration factors of data value function, so as to provide effective decision-making methods and strategies for the data service supply chain, and guidance for the data trading platform when making price decisions. Hopefully, data market segmentation can be further incorporated into the operations models in future work. In addition, we would like to appreciate the National Natural Science Foundation of China for its substantial support and the local government for related project funds at ministerial and provincial-level.

Recycling Decision of Closed-loop Supply Chain Considering Competition ——Based on Government-driven and Market-driven Deposit Refund System

HUANG Yanting, LIU Yi, ZHENG Benrong

2024, 33(1):  51-56.  DOI: 10.12005/orms.2024.0008

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The deposit refund system is an effective mechanism to stimulate the recycling of waste products. The deposit return system can be divided into market-oriented deposit return systems and government driven deposit return systems. Market driven modes are generated spontaneously by manufacturers based on individual economic goals. The government driven model, on the other hand, arises from social environmental goals and is led by the government. This paper considers a closed-loop supply chain composed of an original manufacturer, a remanufacturer, and a recycler, and aims to study the implications of the deposit refund system on the decision making for pricing and collection of manufacturers and recycler. In this paper, we construct four different deposit modes based on the deposit return system, namely (1)basic mode without deposit return, (2)mode with remanufacturers collecting deposits from consumers, (3)mode with the government collecting deposits from two manufacturers, and (4)mode with the government collecting a deposit from recyclers mode. This article studies a closed-loop supply chain considering manufacturer competition under “market driven” and “government driven” deposit return systems, and explores the impact of deposit return systems under different modes on manufacturer recovery decisions.
This article is based on the deposit refund system, considering manufacturer competition, to study the pricing and recycling decisions of closed-loop supply chain, and explores four different modes of the deposit refund system. By discussing the pricing of products from two manufacturers under different modes, the recycling rate of recyclers, and the repurchase price of waste products, corresponding conclusions are obtained: (1)In the mode where the government charges deposits from manufacturers or recycler, the collection rate increases with the increase of unit deposits, revealing that the government-driven deposit return system can effectively promote the recycling and remanufacturing of waste products. (2)In the mode where the remanufacturer charges deposits from consumers, the profit of remanufacturers increases with the increase of unit deposit, which indicates that the market-driven deposit return system will have a positive effect on the profit of remanufacturer. (3)As far as the original manufacturer is concerned, in the mode where the government charges deposits from the manufacturers, its product price increases compared with the mode without deposits, and in the modes where the remanufacturer charges deposits from the consumers and where the government charges deposits from the recycler, its product price decreases. As for the remanufacturer, in the modes with deposit return system, its product price will decrease compared with the mode without deposits. The conclusions are based on the perspective of the deposit refund system, and have certain reference value for the manufacturer’s selection of recycling strategies and the government’s selection of reasonable incentive policies.
This study provides three management insights: (1)Enterprises spontaneously charging environmental deposits to consumers can increase their profits, showcase product quality to consumers, establish a good brand image, and form a competitive advantage for the enterprise. (2)The government’s collection of deposits from manufacturers or recyclers for social environmental goals can effectively improve the recycling rate of waste products and achieve the green cycle of closed-loop supply chain. (3)When consumers pay environmental deposits to remanufacturers, being able to purchase products at the lowest price is the most beneficial for realizing their own interests. The deposit refund system, as a policy mechanism, can have a positive impact on both the environment and the supply chain, ensuring the recycling of products and providing overall profits for the supply chain.

Evolutionary Game Analysis of Agricultural Non-point Source Pollution Control Behavior

BAO Zhe, ZHOU Xiaoliang, LIANG Kairong

2024, 33(1):  57-63.  DOI: 10.12005/orms.2024.0009

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Resource utilization and livestock waste management are two important aspects of sustainable agriculture in China, especially in the agricultural non-point source (NPS) pollution. In order to analyze the influence mechanism of NPS pollution, this study proposes a two-party evolutionary game model to characterize interactions between local governments and farmers. Firstly, we obtain the optimal strategies of local governments and farmers by a theoretical analysis, and then numerical simulations are applied to assess the impact of various factors on strategy selection. A comprehensive evaluation of government incentives and environmental regulations is carried out to find a balanced approach to environmentally sustainable agriculture. Analytical results reveal that environmental and economic factors significantly affect the local government’s decisions, with economic considerations exerting a more pronounced influence. An increase in the environmental quality index coefficient motivates farmers to actively manage NPS pollution, while an increase in the economic index coefficient disincentivizes farmers to do so. Changes in local government spending have a more substantial impact on local government than farmers, who adapt their strategies in response to government actions, albeit with a time lag. Reducing local government operational costs improves the likelihood of farmers actively engaging in NPS pollution control. If the local government increases penalties to tighten its control over pollution-related activities, it contributes to higher revenue. Imposing a penalty limit constrains the strategy space of both parties, thereby facilitating optimal equilibriums in the game. However, more government subsidy affects regulatory actions, which may prove ineffective if it fails to cover pollution control costs. Obviously, it is always a challenge to reach an optimal strategy. Prudent incentive policies are the main means for guiding both the local government and farmers towards an appropriate equilibrium.
This study offers the following managerial insights: (1)Enhancing local government performance assessment standards by progressively emphasizing environmental quality metrics. Prioritizing the evaluation of rural environmental quality during local government tenures. Considering reducing the weight of economic development indicators in these assessments and reward local governments effectively by balancing environmental and economic factors through optimized agricultural production methods. (2)Establishing an environmental supervision system to control agricultural NPS pollution. This system should engage local environmental public interest organizations and public participation in improving feedback, information sharing, and incentive mechanisms for local government interactions with the public. It will reduce the supervisory costs incurred by local governments. (3)Actively guiding farmers for reducing fertilizer and pesticide usage while raising awareness of agricultural NPS. Promoting environmentally friendly agricultural practices to optimize the rural environment through modified planting and breeding methods, operational techniques, and management models. (4)Employing macroeconomic control measures to institute rational incentive systems and corresponding penalties. Ensuring the regularity and institutionalization of local government oversight and inspection activities while enhancing the efficiency of detecting agricultural NPS pollution. These actions are essential for advancing sustainable development of rural ecological environment.


Research on Temperature Control Input of Three-level Supply Chain of Fresh Agricultural Products Based on Differential Game

LUO Ming, ZHOU Guohua

2024, 33(1):  64-68.  DOI: 10.12005/orms.2024.0010

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Fresh agricultural products have prominent biological characteristics that are perishable over time. At the same time, the loss rate of fresh agricultural products in transportation and circulation is also severe. According to the survey results, statistics show that only two fruits and vegetables due to loss cause severe economic losses, which dramatically affect the fresh produce supply chain members of the profit. With the improvement of people’s living standards, consumers pursue the safety of fresh agricultural products and the high quality of fresh agricultural products, and freshness is an important indicator to measure the quality of fresh agricultural products. Therefore, consumers’ demand for high freshness drives the fresh agricultural products supply chain members to put in more freshness preservation efforts. In recent years, some scholars have begun to pay attention to the relationship between freshness and temperature and time and have carried out related research by combining the characteristics of fresh produce. However, none have considered freshness as a function of the dynamic change of temperature control inputs. In addition, from the perspective of consumers, some scholars have suggested that consumer preference is another crucial factor in determining the demand for fresh produce but have not yet suggested that consumer preference is a function of the dynamic change in freshness.
Therefore, this paper raises the following questions: 1)What is the optimal temperature control input strategy in centralized and decentralized scenarios? 2)How do consumer preferences and discount rates affect the optimal strategy? 3)What kind of contract can be designed to coordinate this supply chain perfectly?
To address the above problem, this paper considers a three-level fresh produce supply chain in which suppliers, retailers, and TPL determine their respective temperature control inputs, where suppliers determine the selling price of fresh produce:1) Give the optimal temperature control input strategies for fresh produce under centralized and decentralized scenarios;2)Compare equilibriums under the two scenarios; And 3)propose a mutual cost-sharing-with-fixed-subsidy contract to coordinate the fresh produce supply chain. The primary study shows that the larger the share of each member of the three-tier fresh produce supply chain in the overall benefits of the supply chain, the more significant the proportion of temperature control input costs that they are willing to bear for each other and vice versa.
We consider the retailer side for temperature control inputs in future studies. In addition, the article does not consider other costs of the three parties in the supply chain, such as the supplier’s management costs, the retailer’s sales costs, and the TPL’s maintenance costs, etc. However, the related costs of the three parties in the supply chain can be considered in future research. Finally, to encourage all parties in the fresh produce supply chain to increase their investment in temperature control, the government will subsidize the supply chain parties and introduce their subsidies into the relevant model. The article does not consider government subsidies, however, in future research, introducing government subsidies for fresh produce’s temperature control input strategy is also of practical significance.

Coordination of VMI Supply Chain with Loss Aversion and Replenishment Tactic

MA Chao, HE Juan, HUANG Fuyou, LIU Jing

2024, 33(1):  69-75.  DOI: 10.12005/orms.2024.0011

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In today’s globalized and highly competitive business environment, supply chain management has become a crucial factor for the success of enterprises. In this complex and dynamic network, supplier-managed inventory (VMI), supplier loss-avoidance behavior, and supplier replenishment strategies have emerged as significant factors influencing supply chain coordination and efficiency. Understanding and optimizing these aspects are paramount for enterprises to maintain a competitive edge in the market.Firstly, VMI, as a modern supply chain management model, has gradually garnered widespread attention from businesses over the past few decades. VMI involves suppliers proactively managing the inventory of their customers, facilitating information sharing and real-time responsiveness. This close collaboration not only aids in reducing inventory levels and increasing inventory turnover but also mitigates uncertainty within the supply chain. However, current research primarily focuses on the implementation and advantages of VMI, with limited exploration of the psychological factors and supplier behavior associated with VMI.Secondly, supplier loss-avoidance behavior is a crucial aspect influencing supply chain decisions. When faced with potential losses, enterprises often adopt measures to avoid them, aiming to minimize the likelihood of loss occurrence. This behavior in the supply chain may manifest risk aversion, sensitivity to losses, and impact on decision-making. However, research on the specific effects of supplier loss-avoidance behavior in supply chain management is still relatively scarce.Lastly, the replenishment strategy of suppliers is a key factor influencing inventory levels and supply chain flexibility. Effective replenishment strategies assist enterprises in better coping with market demand fluctuations, reducing stockouts, and enhancing customer satisfaction. However, within the contextof VMI, the relationship between supplier replenishment strategies and coordination contracts, as well as their impact on supply chain performance, requires further an in-depth investigation. In conclusion, a comprehensive understanding of the relationships among supplier-managed inventory, supplier loss-avoidance behavior, and supplier replenishment strategies is crucial for optimizing supply chain coordination, reducing risks, and enhancing efficiency.
Based on this, we then discuss the supplier’s optimal inventory problem in a two-level VMI supply chain consisting of a single loss-averse supplier and a single risk-neutral retailer. In order to minimize losses from output shortfalls, the supplier chooses a replenishment strategy and employs a mental account separation method to characterize its loss aversion. The retailer, on the other hand, incentivizes the supplier to produce through contractual means to improve the overall operational efficiency of the supply chain, and the main contributions are as follows.
First, supply chain coordination models under risk diversification contract, option contract and subsidy contract are established respectively, and it is found that all three contracts can coordinate the supply chain and achieve Pareto improvement. In other words, both parties in the supply chain can induce suppliers to produce from the perspective of risk sharing by adopting appropriate risk diversification contracts, option contracts and subsidy contracts, so as to improve the overall operational efficiency of the supply chain and achieve long-term good cooperation. Second, the above three types of contracts are compared and analyzed, and it is found that the performance of both parties to the transaction under the risk diversification contract is higher than that under other contracts. In other words, suppliers are more willing to adopt risk diversification contract to coordinate the supply chain.Finally, the impact of the degree of loss aversion on the Pareto improvement region of the above three types of contracts is discussed, which shows that suppliers will not choose option contracts and subsidy contracts once the degree of loss aversion they face is too large.
The model setup in this paper is idealized and only considers the “one-to-one” case, and the next step will be to discuss the VMI supply chain coordination under the replenishment strategy adopted by multiple suppliers. In addition, this paper only considers the VMI supply chain problem based on replenishment and loss aversion behavior, and does not involve the promotional effort behavior of the trading participants, so the next step will also consider the VMI supply chain coordination problem when the loss aversion supplier adopts both sales effort and replenishment strategies.

Analysis Method of Passenger Car Demand Trend Based on Online Reviews

YANG Yazao, LI Quansen, TANG Haodong

2024, 33(1):  76-82.  DOI: 10.12005/orms.2024.0012

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In the Internet environment, online reviews reflect consumers’ most intuitive feelings and needs, which will affect consumers’ cognition of products and their decision-making behavior. Mining the potential value of online reviews has become an effective way to obtain intelligence, which is conducive to passenger car manufacturers and sales companies to analyze market demand in multiple dimensions and improve product design and competition strategies. Transforming massive information containing real needs into structured data to extract valuable intelligence information is an urgent problem to be solved. Based on this, this paper uses the implicit Dirichlet assignment (LDA) topic model to learn and obtain document topics from the passenger car review dataset, and sorts the subject words extracted from the documents. The word frequency-inverse document algorithm (TF-IDF) is used to calculate the sentiment value of the subject words. Finally, a data panel related to sales volume is constructed to transform consumers’ real needs into design problems, which is applied to the development of passenger car products. From the subject characteristics and emotional attitude characteristics, the emotional tendency of different model review topics is estimated, and the panel data model of user preference analysis is constructed corresponding to the passenger car sales volume in 2009-2019.
Based on the LDA model, this paper conducts cluster analysis of the topic characteristics of passenger car consumers’ attention, then uses the TF-IDF algorithm to calculate the weight value and the sentiment tendency analysis method to carry out statistical analysis of the comment data to determine the topic of consumer attention,and finally analyzes the tendency change in consumers’ attention topic characteristics through the measurement method and constructs a consumer attention model based on LDA-TFIDF panel data. With the help of the Octopus web page data collection device, this paper conducts online reviews and sales data collection of passenger car products on the three major automobile consumption websites “Autohome”, “Pacific Automobile Network” and “Sohu Auto Channel” with the highest customer engagement rate in China. The Lagrange multiplier method and dual method are used to clean word segmentation and denoising the data, and the LDA topic model is used to analyze the processed data. The main check is the number of words with the same information in the document, and then the TF-IDF algorithm is used to combine the sentiment tendency analysis to assign the characteristics of the comments.
In order to better explore the impact of consumer sentiment on the market share of passenger cars, this study uses the TFIDF value of the previous year’s topic attention in the model to predict the current market share and uses the generalized least squares method (GLS) for estimation. In order to reduce the degree that the prediction results are affected by extreme values, the paper estimates all variables by focusing on the median value of sales weight, then interprets the coefficient of the linear term as the elasticity of passenger cars with median characteristics to market share, and finally uses the linear term to interact with the year to analyze the change in consumer sentiment. In the study, the market share of passenger cars is taken as the dependent variable, and the user’s emotional inclination value as the independent variable.In order to avoid pseudo-regression phenomenon and ensure the stability of the variables, the ADF test should be used to perform unit root test or cointegration test on the variables before establishing the econometric model. The experiments show that: this method can effectively extract online review intelligence information; through the interaction with the year to analyze the productdemand trend, consumers only pay attention to fuel consumption by 8.64% in the past 11 years; among them, the increase in indicators such as appearance, interior and comfort, which are more closely related to the quality of life, is much higher than that in traditional indicators such as power and space. This shows that fuel economy is becoming less and less of a concern and is no longer a decisive factor for consumers to buy passenger cars.
For the past 11 years, consumers’ sensitivity to the fuel economy of passenger cars has gradually decreased, and high-power and large-space models are more popular, but the trend of more pollution emission is increasing. The increase in residents’ income has upgraded consumption,and the appearance, interior and comfort have a more significant impact on consumers’ purchasing decisions and will replace fuel consumption as the main factor affecting consumers’ purchasing decisions. Therefore, passenger car production and sales enterprises should actively use online review analysis results to track consumer preferences and adjust product development directions in a timely manner to enhance market competitiveness. Government should increase policy efforts to guide energy conservation and emission reduction in order to achieve the goals of “peak carbon dioxide emission” and “carbon neutrality” in the transportation sector.

Reliability and Repair Analysis of Complex Systems under Multi-level Disasters Based on Markov Model

DUI Hongyan, LIU Kaixin, TAO Junyong

2024, 33(1):  83-89.  DOI: 10.12005/orms.2024.0013

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With the rapid development of economy, the scale of complex systems has become larger and larger, and system riskiness has increased. When a disaster causes a component of a complex system to fail, it can cause the entire complex system to be hit hard, resulting in huge economic losses. The blockage of the Suez Canal in March 2021 caused the “Butterfly Effect”. According to the world’s insurance giant, Allianz Insurance Group, the Suez Canal blockage is estimated to cause losses to international trade in the range of $6 billion to $10 billion per week. As disasters continue to occur, people gradually focus on pre-disaster prevention and post-disaster repair of the system, with a view to reducing the damage caused by disasters. Since it is difficult to prevent disaster and the damage caused by disasters is huge, it is necessary to focus on the reliability and post-disaster repair analysis of complex systems. Disasters in different severity cause different economic losses to complex systems, for example, the stronger the earthquake, the greater the harm caused by earthquakes. Considering the impact of the disaster level on the performance of complex systems can help to improve the precision management capabilities of post-disaster repairs.
Based on the above background, the paper studies the repair strategies for complex systems under multi-level disasters. Firstly, disasters can be classified into different levels, and the disaster levels are studied based on the Markov model. Then, the component state is abstracted into multiple discrete states, and the performance analysis is carried out for multi-state complex systems based on the Markov model. Based on the performance changes of the complex system before and after the disaster, system losses are analyzed under multiple levels of disasters. System losses include direct and indirect losses; direct losses refer to the damage caused by the disaster to the infrastructure and are related only to the post-disaster state of the failed component; indirect losses refer to the losses of the system due to the degradation of the system performance. A loss optimization model is established, i.e., the repair set of failed components is determined with the objective of minimizing the total system loss. Because of resource constraints, only one failed line can be repaired at a time, and because different lines have different impacts on system losses, the order in which failed lines are repaired must be determined. Finally, the integrated importance measure of the complex system is established to characterize the degree of impact of repairing the failed line on the total loss of the system. The larger the value of the integrated importance measure, the higher the degree of reduction of the total loss of the system caused by repairing the failed line, and the higher the priority of repairing the failed line, thus the repair order of each component in the repair set is determined.
In the paper, the feasibility of the model is demonstrated using the example of the IEEE 18-node standard distribution system. The IEEE 18-node standard distribution system contains 18 bus nodes and 17 lines. Assuming there are 3 states for the disaster level and 5 states for the line, the state transfer rate matrices for the disaster and the line are given respectively. The maximum repair time is given as T=16days. The set of failed lines is given as F={l1,l3,l7,l8,l10,l12,l13,l14,l17} and the failed states of the lines under different levels of disaster are given. Considering the limitation of repair time, firstly, we find that there are 36 possibilities to meet the repair time limitation of the repair set. Then, we calculate the total loss under the 36 repair sets, and when the repair set is R12, i.e., when emergency repair is carried out on the failedlines in R12, the total loss of the system is the smallest, which is 222,500 yuan. Therefore, failed lines l1, l7, l8, l10, l13, l14 and l17 are selected for emergency repair. The integrated importance measure value of each line in the set of repair lines is calculated based on the integrated importance measure formula. The integrated importance measure of lines l1, l7, l8, l10, l13, l14 and l17 are 0.9, 2.799, 8.72101, 6.1226, 4.7497, 2.3250 and 4.1452, respectively. Thus, the repair priority of the failed line is calculated to be l8,l10,l13,l17,l7,l14, l1 in descending order.
The deficiency of this paper is that only a general classification of the disaster level is made, however, the classification standard of different categories of disasters is not the same in real life, and in the future research, we will further extend the model to conduct a more in-depth study of the disaster level. In addition, this paper only studies the post-disaster repair strategy under the objective of loss minimization model.However, the resilience is also one of the important considerations in real life.In the future, we will introduce the resilience theory to study the post-disaster repair research under the dual objectives of loss and resilience.

Reliability Modeling and Analysis of Coupled Cascading Failure Systems

WANG Qi, JIA Xujie, WENG Yuru, TIAN Meiyu

2024, 33(1):  90-94.  DOI: 10.12005/orms.2024.0014

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The phenomenon that natural disasters or man-made failures cause small perturbations in the system, which in turn trigger chain reaction failures in the system, leading to the rapid spread of the disaster in the system and the collapse of part or the whole system, is called cascade failure. Because cascade failure can carry out one-to-many failure transmission, the failure is exponentially fast propagation, so the propagation speed is fast and the scope is wide. In real life, most of the systems are not isolated, such as communication networks and power grids, which are interdependent and interact with each other, and this coupling relationship between the systems makes the scope of cascade failure wider, resulting in a more complex cascade process, which affects the reliability of the entire system and its normal operation.
Aiming at this problem, taking the power communication coupled system as the research object, this paper considers the interaction between the component failure rate and the state of the coupled system to more accurately assess the dynamic performance of the components. A continuous-time Markov process is utilized to establish a coupled cascade failure system reliability model, which describes a more generalized and universally applicable coupled cascade failure system in which inter-system failures and intra-system failures act at the same time. The subsystems are not only affected by component failures that lead to load redistribution, but also the states of other subsystems coupled to them. In this paper, firstly, a stochastic model is established by using the continuous-time Markov process to describe the coupling relationship of the subsystems with the system state transfer. The occurrence of cascade failure of the coupled system is a series of transfers of the coupled system in the state space, which gives the analytical expression of the transfer rate of the coupled system. Then the paper analyzes the effect of cascade failure that the increase in the component load affects the failure rate of the component and the dependence relationship between the subsystems, establishes a reliability model of the coupled cascade failure system, and proves the reliability model of the coupled cascade failure system. The reliability model of the coupled cascade failure system is established, and the calculation method and analytical expression results of the system reliability are proved. Based on the arithmetic example, the specific process of cascade failure occurring in the coupled system is demonstrated to verify the validity and feasibility of the method, the Laplace transform and inverse transform of the coupled system reliability are performed to find the average time before failure of the coupled system, the uncoupled system in which the line failure rate of the electric power system is not affected by the degree of line loading or the state of the communication system is considered, and the indexes of the coupled system and the independent system are compared and analyzed. The results show that the reliability of the coupled system is less than the reliability of the uncoupled system when the number of lines and the line failure rate are the same, and the mean time before failure of the coupled system is smaller than that of the uncoupled system. The comparative results show that the coupling relationship in the system has a significant effect on the system reliability. Therefore, the dependencies between subsystems significantly increase the instability and the risk of the overall system and accelerate the propagation of cascading failures.
The propagation process of cascade failure is deeply analyzed by using the method of continuous-time Markov process and the idea of recursion, which provides a research method for the cascade study of coupled systems based on load and time and can be extended to different coupling relationships, coupling strengths, and different load distribution modes to further study the cascade failure process of the system as well as the reliability analysis. In further studies, the types of system life distributions and coupling relationships can be expanded, such as multilevel coupling relationships and intersystem life nonlinear dependencies.


Reliability Modeling and Optimal Inspection Policy for Degradation System Considering Employee Fatigue and Vacation

LIN Zhou, GAO Zhikuo, YANG Yumin, ZHANG Fengxia, SHEN Jingyuan

2024, 33(1):  95-101.  DOI: 10.12005/orms.2024.0015

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In practical applications, engineering systems usually degrade as time goes by. Various inspection and maintenance policies have been designed and optimized for the degradation systems to improve their availability and meanwhile reduce the operational costs. However, in most of the existing studies, incorrect inspection results caused by human factors have been less taken into consideration, which may lead to the inaccuracy of the reliability model and then influence the maintenance decision. To address this problem, the fatigue levels of the maintenance workers and their negative influences on inspections are taken into consideration to develop a new maintenance model for degradation systems. Base on the proposed model, the main objective of this paper is to design an optimal inspection and maintenance strategy that minimize the long-run average cost of the system.
More specifically, in this paper we consider a degradation system with three states, which are the good state, defect state and failure state, respectively. Systems in the defect state could still work but with a higher failure rate than those in the good state. When the system enters the failure state, it fails immediately and only a corrective maintenance action could restore it to the state to be as good as a new one. A maintenance worker is arranged to inspect and repair the system periodically during his working time. When the inspection result shows that the system is in the defect state, a preventive maintenance action is executed so as to restore the system to the state to be as good as a new one. Inspection results are assumed to be imperfect in this paper. Two inspection errors are considered: type I errors are that the system is in the good state while the inspection result shows that it is in the defect state, and type II errors are that the system is in the defect state while the inspection result shows that it is in the good state. In the literature, the two types of inspection errors have been investigated by some researchers, but most of them have assumed that the probabilities of the errors are constants. The main contribution of this paper is to model the probabilities of the errors as increasing functions of the working time of the maintenance worker, which implies more inspection errors the worker may make when the continuous working time and the fatigue are accumulated. The fatigue accumulated at work could be mitigated or swept away by vacation. Based on this assumption, a new maintenance model is developed for such systems and a simulation algorithm is proposed to calculate the long-run average cost of the system. The cost includes inspection cost, downtime cost and maintenance cost. After that, an optimization problem is formulated: the main objection is to minimize the long-run average cost by taking the continuous working time W, the vacation time V and the inspection period T as the decision variables.
Based on the proposed model and algorithm, a numerical example for the operation and maintenance of elevators is studied. First, we assume that for the maintenance worker the original working time W=5, the vacation time V=2 and the inspection period T=1, respectively. The optimization results are W^*=6, V^*=1 and T^*=0.4, and the long-run average cost is reduced from 359.3 to 307.6, which shows the efficiency of the proposed model. Furthermore, the sensitivity analysis is made for the cost parameters, including the maintenance cost for type I errors, the downtime cost, and so on. Based on the sensitivity analysis, we investigate the influence of changing the cost parameters on the optimization results. The results show that for systems with a high cost of inspection errors, decision makers should increase employee vacation time and reduce the frequency of inspection; for systems with high preventive maintenance costs, decision makers should appropriately increase employee on-duty time and reduce the inspection frequency; systems with high downtime cost are suggested to increase the work period and the frequency of inspections at the same time.
To sum up, in this paper the influences of the fatigue levels of the maintenance workers on the inspection results are taken into consideration. By modelling the relationship between the fatigue and the inspection correctness, the inspection and maintenance optimization model is developed. The main goal is to minimize the long-run average cost of the system by optimizing the work period, vacation period and the inspection interval of the worker. Future studies could pay more attentions to the multi-component systems, in which the dependence among the components may bring new challenges. Besides, in this paper the maintenance actions are assumed to restore the system to the state to be as good as a new one. More maintenance strategies, such as the imperfect maintenance, could be taken into consideration in the future studies.


Forecasting Grain Yield in China Using Attention-based ADE-Bi-IndRNN Model

WU Binrong, WANG Lin

2024, 33(1):  102-107.  DOI: 10.12005/orms.2024.0016

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To meet the demand for domestic food and ensure its security, agriculture has always been regarded as one of China’s important strategic industries. An accurate prediction of grain production is helpful for adjusting agricultural production policies and controlling the national macro-economy to ensure food security. However, an accurate grain production forecast is highly challenging due to various factors such as natural, social, and technological factors, and agricultural production conditions.
To more accurately predict China’s total grain production, a grain production forecasting model based on the adaptive differential evolution algorithm (ADE) and attention-based Bi-IndRNN (bidirectional independent recurrent neural network) is constructed. This model considers five major influencing factors: grain crop unit yield, agricultural production conditions, technological factors, agricultural insurance, and market and economic factors. Using IndRNN for predicting China’s grain production has the following advantages: it addresses the issues of gradient vanishing and exploding by adjusting the temporal changes in gradient backpropagation; IndRNN performs well when using activation functions such as ReLU, ensuring robust training; multi-layered IndRNN can be effectively stacked, especially with residual connections, increasing the depth of the network; the behavior of each IndRNN neuron in every layer is easily interpretable as each neuron is independent. Therefore, employing IndRNN neural networks in deep learning models may achieve better accuracy and stability in grain production forecast. However, setting the parameters for IndRNN is complex, requiring the selection of hyperparameters such as the number of layers, time steps, unit per hidden layer, batch size, and learning rate. Finding the optimal combination of these five hyperparameters is a highly intricate problem that directly impacts prediction accuracy and stability. Hence, a reliable and efficient algorithm is needed to accomplish this task. ADE with adaptive mutation factors strikes a good balance between global search and local search, possessing strong global search and convergence capabilities. Therefore, this study employs ADE to search for the hyperparameters of IndRNN.
Data on total grain production in China from 1991 to 2019 are collected from the National Bureau of Statistics. Taking into account the analysis of influencing factors on grain production by existing scholars and the fact that different stages of agricultural development in China are influenced by different factors, the following factors are selected as explanatory variables to analyze the main factors affecting China’s total grain production at present. These factors are divided into five categories: grain crop unit yield (grain unit yield per area), agricultural production conditions (the number of agricultural, forestry, animal husbandry, and fishery workers, total sown area of grain crops, rural electricity consumption), technological factors (the irrigated area of cultivated land, total power of agricultural machinery, amount of chemical fertilizer used), agricultural insurance (agricultural insurance premium per capita, average compensation per capita in agricultural insurance), and market and economic factors (grain import volume, agricultural production input index, grain production price index).
The predicted grain production for China from 2020 to 2024 is 667 million tons, 672 million tons, 680 million tons, 699 million tons, and 702 million tons respectively, showing an overall upward trend with an average annual growth rate of 1.15%. Furthermore, through the analysis of attention weights on multiple variables, it is found that the three variables contributing the most to the prediction of China’s total grain production are: grain unit yield per area, total sown area of grain crops, and irrigated area of cultivated land. Additionally, government subsidies for agricultural insurance, grain import volume, grain production price index, and agricultural production input index also contribute to the increase in China’s total grain production, providing suggestions for the development of grain production in China.
Currently, the main focus of China’s efforts to increase grain production and ensure food security lies in three aspects: grain yield per unit area, agricultural land, and irrigation area. To improve grain yield per unit area, it is crucial to enhance agricultural technology and improve planting techniques, as well as provide the guidance for the use of agricultural machinery. In terms of agricultural land, the “bottom-line thinking” on the total sown area of grain crops should be upheld, and strict measures for land protection should be implemented to protect the 1.8 billion mu (approximately 120 million hectares) of arable land, preventing its conversion to non-agricultural use. Increasing the irrigated area of cultivated land requires greater investment in agricultural water conservancy, improving irrigation systems, and focusing on the renovation and construction of irrigation pump stations and supporting facilities.
Individual insurance premiums per capita and average compensation per capita in agricultural insurance have certain effects on predicting total grain production. This indicates that the government’s implementation of agricultural insurance subsidies has alleviated the negative impact of natural disasters on grain production, reduced agricultural development risks, safeguarded the interests of farmers, and enhanced their enthusiasm for crop cultivation, thereby temporarily increasing China’s total grain production. Therefore, China should actively promote policy-oriented agricultural insurance development to provide protection for farmers against natural disasters and ensure food security.
Grain import volume, grain production price index, and agricultural production input index also play a role in predicting China’s total grain production. China has consistently adhered to the work principle of being “mainly self-sufficient in grain with imports and exports as supplementary”. However, due to changes in China’s grain production and consumption structure since the reform and opening up, China’s grain import volume has been increasing, making it more reliant on the international grain market. This has to some extent affected China’s food security and led to the introduction of relevant policies in recent years to stimulate the domestic grain production. Agricultural production input index and grain production price index reflect changes in the cost of grain production and grain prices, directly affecting the enthusiasm for grain production in the following year, thus being closely related to grain production.

Grey Modeling Technology for Measurement and Prediction of the Development Capability of Waste Utilization Industry in China

ZENG Bo, GOU Xiaoyi, GONG Ying

2024, 33(1):  108-114.  DOI: 10.12005/orms.2024.0017

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The rapid development of China’s economy and the increasing people’s material needs have intensified the development and utilization of natural resources, resulting in the production of more products and waste after consumption. The utilization of waste resources is the main way to solve the over-exploitation of primary resources and reduce the environmental harm caused by waste treatment, and it is also an important basis for the realization of the concept of sustainable development. Depicting the development trend of related industries is the premise of defining the scale of waste resource utilization. The environmental pollution caused by direct waste treatment is effectively reduced by the rapid development of China’s Waste Resource Utilization (WRU) industry. Therefore, it is of great value to scientifically predict its development ability to improve China’s environmental quality and promote the deep integration of related industries.
This paper constructs a multivariable grey model DWGM(1,N) with adaptive characteristics, which solves the mechanism defects, parameter defects and structure defects of the traditional GM(1,N) and realizes the complete compatibility with the traditional GM(1,N) and GM(1,1) models. Firstly, based on the PEST model and the diamond model, a four-dimensional index system of industrial development ability of WRU is constructed, which is based on the demand of production resources, economic development level, technological innovation and ontology-related factors. After that, based on the grey relational model, the index is measured and screened, and the correlation threshold is set to 0.9. Then, the filtered indicators are brought into DWGM(1,N) for modeling. In order to verify the performance of the new model, this paper compares the modeling results of the new model with GM(1,N) and GM(1,1) models. The results show that the average relative simulation error of DWGM(1,N) is 0.2683%, which is much better than that of the traditional GM(1,N) and GM(1,1) models (17.8382% and 6.7515% respectively). The performance of DWGM(1,N) model is improved by about 25.2 times and 66.5 times respectively compared with that of GM(1,1) and GM(1,N).
The forecast results show that the development capacity of WRU industry will show a gradual upward trend in the next few years, and it is predicted that the development capacity of this industry will be 1.5 times that of 2013 by 2025, indicating that this industry will have a good development in the future, which is in line with the concept of green development in China in recent years. Therefore, based on the research results, this paper puts forward the following relevant suggestions from the perspectives of technological innovation and economic development level, with a view to promoting the further improvement of the development capacity of this industry. It mainly includes: promoting the technological innovation of waste resource production, and enhancing the promotion of enterprise technological innovation to waste utilization. And the development concepts of innovation, coordination, green, openness and sharing need to be thoroughly implemented, a reasonable scientific orientation set, and the integration and development of the eastern and western regions deepened.

Connection Number Projection Based Triangular Fuzzy Number Combination Forecasting Model and Its Application

TIAN Chengshi, YUAN Hongjun, XIANG Ruibing

2024, 33(1):  115-122.  DOI: 10.12005/orms.2024.0018

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In the prediction process, the combined prediction model constructed by assembling several prediction methods together can obtain more accurate prediction results, which has been widely used in various fields of real life. Since the prediction is also accompanied by many uncertain and fuzzy phenomena, the triangular fuzzy numbers can portray the uncertain information more accurately than the interval numbers, which in turn makes it necessary to carry out the research on the innovative methods of triangular fuzzy number combination prediction.
In order to study the problem of triangular fuzzy number combination prediction, this paper firstly introduces the concept of connection number in set-pair analysis, finds out the transformation relationship between triangular fuzzy number and ternary connection number, and uses the ternary connection number combination prediction to study the triangular fuzzy number combination prediction. It also defines the arithmetic rules of ternary connection number, and cleverly avoids the ambiguity and complexity in the arithmetic of triangular fuzzy number combination prediction. Secondly, the contact number projection is constructed as a new index, and the triangular fuzzy number combination prediction model is established by maximizing the contact number projection with fixed weight coefficients from the sequence of actual values of connection number and the sequence of predicted values of connection number combination. This model is a better triangular fuzzy number prediction model because it is easy to calculate and can improve the prediction accuracy. Then we construct the contact number generalized induced ordered weighted average (CNGIOWA) operator, and study the properties of homogeneity, idempotence and substitution invariance of this operator. Aiming at the insufficiency of fixed weight coefficients in fixed-weight coefficient triangular fuzzy number combination prediction, according to the basic principle that high-precision prediction methods in combination prediction should be given larger weight coefficients, and combining with the criterion of contact number projection maximization, a variable-weight coefficient triangular fuzzy number combination prediction model based on the contact number projection and the CNGIOWA operator is established. The model can significantly improve the prediction accuracy, so it is a superior triangular fuzzy number prediction model.
In the empirical analysis, the least squares fuzzy linear regression method, quadratic polynomial fuzzy time series method and fuzzy time series autoregression method are constructed for the actual value sequence of the triangular fuzzy number, and the steps of constructing the two types of triangular fuzzy number combination prediction models are used to carry out the error evaluation analysis, the superiority analysis and the sensitivity analysis in turn. The results all show that the two types of combined prediction models are superior to both single prediction methods and prediction methods in the existing literature, while the variable weight coefficient triangular fuzzy number combination prediction is more effective in improving the accuracy of prediction than the fixed weight coefficient triangular fuzzy number combination prediction.
In this paper, when discussing the variable weight coefficient triangular fuzzy number combination prediction model, only the parameter sensitivity analysis is made in the limited interval, and only the CNGIOWA operator is constructed. If the parameter takes other values and constructs other connection number information aggregation operators, what will be the impact on the variable weight coefficient triangular fuzzy number combination prediction model? These contents can be studied in depth in the future.

Dynamic Optimal Control of a Monopolist’s Investment in Pollution Abatement and Optimal Design of the Environmental Taxation under Knowledge Accumulation

LI Huiquan, MAO Shiping

2024, 33(1):  123-131.  DOI: 10.12005/orms.2024.0019

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Due to the dual externalities of pollution control technology innovation, external environmental regulation is essential. Environmental regulations influence the attitude of enterprises towards the natural environment through coercion and inducement. By incentivizing enterprises to improve their innovation capabilities, offsetting the costs brought about by environmental protection, and enhancing their profitability in the market, domestic enterprises can gain a competitive advantage in the international market, thereby improving industrial productivity. Numerous studies have shown that under the influence of environmental regulations, enterprises will choose different investment decisions for pollution control, confirming the importance of the selection and design of environmental regulations. However, existing research has overlooked the issue of knowledge accumulation in the investment process of pollution control technology. Knowledge accumulation is caused by “learning by doing” in the investment process of pollution control technology. Knowledge accumulation is a by-product of the investment process, and “learning by doing” is an important way to improve production efficiency and achieve endogenous economic growth.
This article applies the theory and methods of optimal dynamic control, based on the research problem of optimal investment in enterprise pollution control technology and optimal pollution tax rate design by social planners, to establish an optimal control model for monopolistic enterprises that consider knowledge accumulation in producing a single product. Compared with existing research, the problem of knowledge accumulation is considered in the investment process of enterprise pollution control technology. Based on the practical background, enterprises will transfer pollution taxes to consumers, and the consumer demand function is modified with pollution taxes. Then, the optimal investment and output level of enterprise pollution control technology under the conditions of maximizing enterprise profits and social welfare are analyzed, and the optimal pollution tax rate is calculated. Finally, the evolution path of the optimal solution is numerically simulated and analyzed.
Research has shown that: (1)Under the conditions of maximizing enterprise profits and social welfare, there exists a unique saddle point equilibrium in the control system. (2)Under the conditions of decision-making by oligopolistic enterprises and social planners, there exists a unique optimal pollution tax rate when the two control systems are balanced. (3)Under the condition of maximizing social welfare, the pollution control investment and production level of enterprises are higher than those under the condition of maximizing profits. (4)The cumulative pollution under the condition of maximizing social welfare and the pollution emissions per unit product of enterprises (the pollution control technology of enterprises) are lower than the cumulative pollution emissions per unit product of enterprises under the condition of maximizing profits.
The main characteristics of this article are: (1)Assuming that the government levies a pollution discharge tax on the pollution control technology of enterprises. (2)Considering the knowledge accumulation problem in the investment process of pollution control technology. (3)Considering the issue of transferring pollution taxes and fees from enterprises to consumers, the consumer demand function is modified using pollution taxes. This study enriches the research theory on the innovative behavior of pollution control technology in enterprises considering knowledge accumulation, and has practical guidance significance for the innovation management of enterprises with knowledge accumulation characteristics. The steady-state equilibrium analysis of the optimal investment in pollution control technology and the optimal pollution tax rate designed by social planners can help enterprises formulate more scientific market competition strategies. Based on the existence of a unique optimal pollution tax rate when the control system is balanced, effective enterprise investment decisions and optimal pollution tax rates can be formulated, thereby improving social welfare levels.
This article mainly studies the investment problem of pollution control technology. In this article, we assume that the marginal production cost of enterprises is constant. Possible future research directions include: (1)Considering the dynamic optimal control problem of pollution control investment for monopolistic enterprises under the condition of process innovation (reducing marginal production costs). (2)Considering the dynamic optimal control problem of pollution control investment for monopolistic enterprises under the condition of cross period emission trading (emission rights bank).

Application Research

Impact of Catastrophe Swap on the Boundary of Insurability for the Catastrophe Risk ——Taking Super Typhoon Hato as an Example

SHANG Qin, QIN Yida

2024, 33(1):  132-137.  DOI: 10.12005/orms.2024.0020

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Climate change and increasingly extreme weather events, have caused a surge in natural disasters over the past 50 years. The insurance industry must play a more critical role in combating the climate crisis andimproving sustainability. China experiences several large natural disasters every year. How to develop China’s insurance market, expand underwriting coverage and enhance China’s overall insurance underwriting level has become one of China’s primary tasks.
As a new type of catastrophe derivatives, catastrophe swap has more simple transaction process, lower cost and greater flexibility, and can spread risks more effectively than those traditional catastrophe derivatives like catastrophe bond. At present, many international insurance companies are using catastrophe swaps as one of their main instruments of spreading risks. China has not yet carried out such products for the time being, and from successful international experience, it can be foreseen that, there is a great potential market in China for catastrophe swap. Due to the opacity of the transaction data, there are few researches on catastrophe swaps abroad, and even less domestically. Although there has been some progress in the design and pricing of catastrophe swaps, there is limited research on the application of these derivatives, which has caused insurance companies tobe lack of the basis for making the best decisions.
In order to enrich the research on the pricing theory and practical application of catastrophe swap in China, this paper explores the impact of a catastrophe swap, a new catastrophe derivative, on catastrophe risk insurability boundaries. The pricing model of the catastrophe swap under the assumption of continuous payment is given, and the surplus process of insurance companies under the catastrophe swap is obtained. By using Gerber-Shiu function, the ruin probability and insurability boundary functions of insurance companies in the state of participating or not participating in catastrophe swaps are derived. It is indicated that the limit of insurability for insurance companies to bear catastrophe risk is a function of the initial capital, loss settlement ratio, safety load factor, and maximum ruin probability. Based on this, this paper takes the TyphoonHato as a pricing example for catastrophe swaps using typhoon loss data from Guangdong Province in China. This study calculates the fair price difference of catastrophe swap, discusses the impact of catastrophic swaps on the insurability boundary, and further analyzes the relationship between the loss compensation proportion and initial capital, safety load coefficient and bankruptcy probability. The sensitivity analysis shows that the lower the initial capital of an insurance company, the greater the required safety load factor. On the contrary, the higher the initial cost, the lower the safety load factor. The proportion of loss compensation increases with the increase of initial capital and safety load factor. As the maximum bankruptcy probability that insurance companies can bear increases, the compensation ratio also significantly increases. The study confirms that participating in catastrophic swaps can help insurance companies expand the insurability boundary of catastrophic risks to a certain extent and improve underwriting capacity with unchanged initial capital, safety load factor, and maximum bankruptcy probability.
This paper provides some inspiration and reference for the research and application of catastrophic swaps in China. Given the particularity and complexity of catastrophic risk, there is still a lot of valuable research in the field of catastrophic swaps that needs to be further explored. More accurate characterization of catastrophic features and the design of catastrophic swaps that better meet the needs of the Chinese insurance market are the main tasks for further research.

Analysis of the Multifractal Characteristics and Risk Measures of China’s Stock Market Affected by COVID-19

XU Nan, LI Songsong, HUI Xiaofeng, ZHANG Yinglong

2024, 33(1):  138-144.  DOI: 10.12005/orms.2024.0021

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After more than three decades of development, China’s stock market has rapidly grown into a multi-level stock market that includes the main board market, the SME board market and the GEM board market, ranking the second in the world in terms of market size, second only to that in the United States. The stock market, a socio-economic barometer, was the first hit by the sudden outbreak of Newcastle Pneumonia. The U.S. stock market set an all-time record of four meltdowns in nine days in March 2020, and international stock markets also experienced huge shocks as a result.In China, due to the need to deal with the rapid spread of the epidemic, the country adopted a strict quarantine policy against the epidemic. The uncertainty of supply and demand as well as the lack of awareness of the epidemic exacerbated market panic, and this sentiment was quickly transmitted to the stock market, exacerbating abnormal market volatility, making it particularly important to measure the structural characteristics and risk profile of the stock market.
Previous research on stock market risk has been more based on the Efficient Market Hypothesis (EMH) and the empirical analysis using simple linear analysis tools. As research continues to progress, anomalies in the financial markets emerge and EMH is constantly challenged because it cannot give a reasonable explanation. As a nonlinear and complex dynamic system, a good performance of multiple fractal analysis method based on Fractal Market Theory(FMT) in analyzing nonlinear non-Gaussian distribution series will provide a more effective metric tool to better measure the changes in market structural characteristics. Therefore, this study uses fractal analysis tools to analyze the structural characteristics of the stock market affected by the epidemic metric, giving new evidence on market structural characteristics and risk measures from a fractal perspective.
With intraday 5-minute trading data of the main board, SME and GEM indices: SSE, SZSE, CSI300, SME, and ChiNext are selected from the WIND database. Multifractal detrended fluctuation analysis (MF-DFA) is used to explore the impact of this new coronavirus pneumonia outbreak on our multi-level stock market. Moreover, the advantage of this method over other methods is that it can find long-range correlations of non-stationary time series and avoid misjudgment of correlations. The sliding window tool is also used to provide a detailed portrayal of the dynamic changes in market fractal characteristics and risk levels.
The results of the study indicate that, firstly, the multifractal characteristics of China’s multi-level stock market is significantly enhanced during the epidemic, the complexity and risk intensity of the market significantly increase, and the effectiveness of the market severely weakens; in the later period when the epidemic is effectively controlled, the risk intensity is significantly reduced and the market efficiency is gradually restored. Secondly, during the epidemic period, the market fractal intensity and riskiness of ChiNext are lower and more effective than other sector indices, and the overall market performance is better.In addition, the fractal characteristics of each sector representative index have time-varying multifractality throughout the sample period. Finally, the combination of long memory of index return series with non-normal thick-tailed distribution makes China’s stock market exhibit strong multifractal characteristics during the epidemic period. The findings of this paper reflect, to a certain extent, the reliability of market risk intensity indicators based on multiple fractal analysis tools during the epidemic, and the findings are consistent with the market performance. Therefore, exploring the establishment of a capital market risk measurement and crisis warning mechanism based on FMT will provide investors and regulators with some references and suggestions for crisis warning and risk management.

Purchasing Intention Identification Model Based on Deep Learning in E-commerce

GUO Xiaoyu, MA Jing

2024, 33(1):  145-150.  DOI: 10.12005/orms.2024.0022

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With the rapid proliferation and intelligent development of e-commerce platforms, accurate identification of user purchase intention has become a crucial influencing factor in driving users from intent toactual purchases. Therefore, identifying user purchase intention is one of the significant methods to enhance the Purchase Rate (PR) in the realm of e-commerce. Purchase intention identification aims to infer the intended purchase of potential customers or users by analyzing the similarity between user query text and product description text, ultimately increasing the PR. Due to the diversity and colloquial nature of user search queries, identifying user purchase intention becomes increasingly challenging, and even more so in vertical e-commerce where users may not even be aware of the names of the products they need.
In response to the phenomenon of unclear user purchase intention, this paper proposes a novel model aimed at identifying user purchase intention from user queries with unclear purchase intention. This model first employs the Word2Vec (WV) algorithm’s Continuous Bag-of-Words (CBOW) model to train word vectors. Subsequently, these word vectors are fed into a one-dimensional Convolutional Neural Network (CNN), followed by further feature extraction using the Deep Semantic Similarity Model (DSSM). This process calculates semantic similarity using cosine similarity, subsequently transforming semantic similarity into a posterior probability form to construct a loss function. During model training, it narrows the textual representations in a high-dimensional space between user queries and intended products while expanding the representations between user queries and non-intended products.
An empirical analysis is conducted using real search data from the U.S. building materials e-commerce website Home Depot, within the Keras framework. The results indicate that our proposed model achieves an F1-score of 80.6% on the test dataset in a five-class classification problem. To test the performance of the model proposed in this paper in more complex purchase scenarios, six, seven, and eight-class classification tasks are designed. The results also indicate that as the number of categories increases, the values of various evaluation metrics decrease. However, the F1-scores for all three classification tasks remain above 70%, demonstrating competitive performance in multi-class tasks.
Through the empirical research, this paper draws the following conclusions: (1)The proposed model leverages Word2Vec and CNN for text feature extraction and employs the DSSM model to further extract feature representations of user queries and product descriptions in a high-dimensional space. This maximizes the utilization of semantic similarity between user queries and the correct product descriptions while avoiding subjective interference during feature extraction, ultimately enhancing the identification of purchase intention for products. (2)Deep learning models are often too large to be practical in real-world scenarios. In contrast to typical deep learning models, the model proposed in this paper converges at a faster rate. (3)The model’s F1-score is significantly higher than the baseline model, and as the number of categories increases, the model’s evaluation scores still maintain a high level. (4)Real training data often exhibit class imbalance issues. The model proposed in this paper constructs negative examples based on positive data to balance the data quantity across different categories, enabling the model to consider all categories during the training process. The method proposed in this paper can only identify users’ intended products within a small number of product descriptions. How to identify users’ intended products within a massive volume of product descriptions is a further research direction.

Impact of International Technology Spillovers on China’s Green Innovation Performance —Conduction Analysis Considering the Coupling and Coordination of Innovative Ecological Elements

ZHENG Wanteng, ZHAO Hongyan, ZHONG Yingjia

2024, 33(1):  151-157.  DOI: 10.12005/orms.2024.0023

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Faced with many challenges brought about by the deterioration of the ecological environment, building a green innovation system and driving comprehensive green transformation are the long-term strategy for sustainable development of the Chinese economy. However, the current green innovation performance in China relatively lags behind, and the fundamental reason is that China’s independent innovation foundation is weak, the green innovation model is not coordinated with economic development, and the technology blockade in Europe and America has led China into the dilemma of “low-end lock in” green technology. With the steady advancement of the “going out” and “bringing in” strategies, China’s outward foreign direct investment (OFDI), absorption of foreign direct investment (FDI), and import trade have shown a rapid growth trend, undoubtedly opening up the international channel for China’s green technology introduction, forming effective factor flow, knowledge diffusion, and technology spillover. In an open economic environment, besides independent research and development to improve green innovation performance, another important way for China is to absorb and utilize international technology spillovers, which is a more cost-effective path for green innovation transformation. So, can international technology spillovers drive the improvement of China’s green innovation performance? Are there any differences in the driving effects of different channels? In addition, China’s green innovation is a systematic ecological innovation model that balances social, economic, and environmental benefits, striving to achieve coupling and coordination of ecological elements such as innovation communities, innovation resources, and technological environment. Generally speaking, international technological elements diffuse and spill over to innovation entities such as Chinese enterprises, universities, and research and development institutions through channels such as investment and trade, strengthening technology transfer and knowledge exchange, and to some extent accelerating the coupling and coordination of regional innovation ecological elements. Therefore, a question worth exploring is: can international technology spillovers steadily improve China’s green innovation performance by driving the coupling and coordination of innovation ecological factors? Obviously, an in-depth exploration of the above-mentioned issues has important practical significance for China to effectively absorb international technology spillovers and achieve improved green innovation performance.
Based on this, this paper is based on panel data from 30 provinces in China from 2003 to 2018, using dynamic fixed effects model, system GMM model, and mediation effects model to empirically examine the impact and heterogeneity characteristics of international technology spillovers on China’s green innovation performance, as well as the transmission mechanism of coupling and coordination of innovation ecological factors. The research has found that international technology spillovers can significantly improve China’s green innovation performance, but there are channel differences. Among them, OFDI technology spillovers have the strongest driving effect, followed by FDI technology spillovers and import trade technology spillovers.At the same time, there is heterogeneity in regional and independent research and development expenditures in this driving effect. OFDI and FDI technology spillovers have a stronger driving effect on green innovation performance in the central and western regions, while import trade technology spillovers are more efficient in empowering green innovation performance in the eastern region. Moreover, in regions with higher independent research and development expenditures, international technology spillovers have a stronger driving effect.In addition, international technology spillovers can indirectly drive the improvement of China’s green innovation performance by guiding the coupling and coordination of innovation ecological factors.The research conclusion indicates that it is necessary to pay attention to the coupling and coordinated development of innovative ecological elements, layout international technology spillover channels based on regional characteristics, and continuously leverage the long-term mechanism of international technology spillovers driving the improvement of China’s green innovation performance.
Although a lot of work has been done, there is still room for expansion in this paper, which is to explore the moderating effect of institutional factors on the empowerment of China’s green innovation performance by international technology spillovers, in order to provide data reference for the efficient institutional design of absorbing international technology spillovers.

Research on the Impact Mechanism of Government R&D Funding on OFDI’s Reverse Green Innovation

HAN Xianfeng, LIU Juan, LI Boxin

2024, 33(1):  158-164.  DOI: 10.12005/orms.2024.0024

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In the new era, strengthening green innovation has become a key choice to achieve a “win-win” situation between economic growth and environmental protection. Many studies have shown that OFDI is an important channel for a nation to obtain green technology spillover. However, there is still a debate in the academic community about whether OFDI reversely drives green innovation. Theoretically, green innovation has certain public goods attributes. In the case that enterprises bear the cost of green innovation but do not obtain the income of innovation, if there is a lack of sufficient government R&D compensation, it may inhibit the enthusiasm of enterprises for green innovation. When the individual income of green innovation is greater than the social income, the government’s blind R&D subsidies may lead to R&D resources mismatch and is not conducive to the green innovation of the whole society. In the context of the country’s further promotion of “going global” and green development strategy, if we ignore the government R&D funding and only consider the OFDI reverse green innovation, this may lead to misleading conclusions. Therefore, it is undoubtedly of great practical significance to explore how to scientifically implement a reasonable government R&D funding strategy to maximize the spillover dividend of OFDI reverse green innovation.
Based on the provincial panel data, this paper empirically examines the dynamic effect of government R&D funding on OFDI reverse green innovation by using the threshold model. Firstly, on the basis of analyzing the static characteristics of OFDI reverse green innovation, based on the panel threshold technology of Hansen, the “bootstrap method” is used to simulate the likelihood ratio test statistic for 500 times, and the internal relationship between OFDI and domestic green innovation is tested. Secondly, the triple panel threshold model is used to study how government R&D funding affects OFDI reverse green innovation, and the internal mechanism and law of the dynamic regulation of government R&D funding are described. The optimal regulation interval of government R&D funding is identified, and the robustness test is made from different angles. Finally, from the three groups of eastern, central and western regions and the two groups of the Belt and Road and the non-Belt and Road regions, the spatiotemporal heterogeneity characteristics of OFDI reverse green innovation spillover under the regulation of government R&D funding are explored.
Based on the theoretical deduction and empirical analysis, this paper analyzes the dynamic effect of government R&D funding on OFDI reverse green innovation. The study finds that OFDI has a significant driving effect on domestic green innovation, but this effect presents an obvious U-shaped dynamic evolution characteristic of being first negative but then positive; government R&D funding will positively regulate OFDI reverse green innovation spillover, and there is an optimal government R&D funding interval for OFDI reverse green innovation(0.366,0.428], and too high or too low intensity of government R&D funding will cause dividend loss of OFDI reverse green innovation to some extent; the dynamic impact of OFDI on green innovation under the regulation of government R&D funding has significant spatiotemporal heterogeneity, that is, there are distinct characteristics of the alternating evolution of “incentive effect” and “crowding out effect” in the spatiotemporal dimension.
The limitations of this paper may lie in the following aspects: Firstly, it fails to describe the impact of OFDI on industrial green innovation, and the panel data of industrial and high-tech industries can be further used in the future. Secondly, the analysis based on China’s provincial data is slightly rough, and more subdivided prefecture-level data can be used in the future study to explore the potential regional heterogeneity. Thirdly, what factors will help to release OFDI’s green innovation spillover remains to be explored, and the subsequent analysis can be deepened based on other new perspectives.

Incentive Mechanism of Tradable Renewable Portfolio Standards Using Green Certificates between Renewable Energy Power Generators and Sellers

SHANG Bo

2024, 33(1):  165-171.  DOI: 10.12005/orms.2024.0025

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Encouraging electricity sellers to actively fulfill the Renewable Portfolio Standards (RPS) obligation will be the meeting point of balancing the relationship between the quotas system and the electricity market reform. China fully launched the monitoring and assessment mechanism of RPS in 2020, which is one of the most common and successful policies to reduce carbon emissions and promote the utilization of renewable energy technologies. In fact, the “Notice on the Establishment and Improvement of the Renewable Energy Power Consumption Guarantee Mechanism” (i.e., RPS) stipulates that the power grid, electricity selling enterprises and other electric power entities shall assume the responsibility of renewable energy consumption. Hence, according to the policy, in order to achieve the quotas target, power sellers can purchase green certificates or excess consumption of renewable energy from renewable energy generators to fulfill the corresponding quotas obligation. Based on this fact, this paper constructs the decision model of quotas trading cooperation between renewable energy generators and upstream and downstream enterprises of electricity suppliers, deconstructs the external incentive model under the single cooperation path and the double incentive model under the continuous cooperation path. This paper aims to quantitatively study the incentive effect of bilateral transactions from the perspective of power sellers, which has strong theoretical significance and application value for increasing the scale of investment in renewable energy, activating renewable energy market transactions, and deepening the reform of electricity market.
Regarding the incentive mechanism of green certificate trading between renewable energy generators and electricity sellers, this paper mainly considers two different trading cooperation situations, firstly, namely single cooperation path and continuous cooperation path, since the incentive effect is different under different cooperation paths. The single cooperation path is that renewable energy generators consider a single cooperation, and electricity suppliers also consider a single cooperation. And continuous cooperation path is that renewable energy generators and electricity suppliers are considering continuous cooperation. Therefore, the decision-making process of green certificates quotas trading considers the external incentive policy of quotas power sale subsidy under the single cooperation path. And the internal incentive mechanism of cooperation interest coordination is considered based on the subsidy policy of quotas power sale under the path of continuous cooperation, that is, internal and external double incentive mechanism respectively. Because renewable energy power and green certificates can be bundled or separated, the green quotas trading can be also divided into two modes. Hence, the incentive based optimal decision model is analyzed under the two quotas trading modes, i.e., the bundled transaction mode of electronic certificates and the pattern of separation of electronic certificates respectively. Finally, the paper focuses on the change in maximum cooperation revenue of both parties and the influence of incentive parameters on market electricity price and green certificate price, aiming to fundamentally alleviate the contradiction that the RPS system is difficult to adapt to the electricity market reform.
The main results indicate that external subsidies promote the increase in single cooperation income. The bundled trading pattern of power and certificates is an important way for power sellers to achieve the quotas target, and corresponds to an optimal subsidy amount. If government fortifies the subsidy for power quotas sale, the trading pattern of separation of power and certificates can produce a prominent incentive effect. Under the continuous cooperation path, the condition for renewable energy power generators to obtain the maximum benefits is equipped with higher sharing proportion of benefits and medium proportion of quotas cost. However, reducing the cost sharing ratio will still lead to an increase in the price of green certificates. No matter what kind of quotas trading pattern, the internal cooperative interest coordination mechanism can restrain the market electricity price and green certificate price rise.
The research is expected to provide a theoretical basis for Chinese government decision-makers to design an incentive scheme that is compatible with the reform direction of the RPS system and the electricity sales side, find a quotas trading model that can improve the consumption of renewable energy market, and at the same time, and relieve the contradiction between reducing the quotas cost and increasing the economic benefit of electricity sales entities.


Cascading Failure Analysis of Engineering Project Portfolio Risk Diffusion Model

LI Qian, WEI Jielin, LIU Fengtao

2024, 33(1):  172-178.  DOI: 10.12005/orms.2024.0026

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The parallel operation of multiple projects is the norm for more and more engineering construction enterprises. The combination management of multiple projects focuses on coordinating the interactive relationships between projects, which can avoid vicious competition for resources between projects, improve resource utilization efficiency, and generate collaborative effects for enterprises. However, the interaction between projects increases the difficulty with risk management, leading to risk diffusion issues. When risk diffusion occurs, the failure phenomenon can spread between projects through interactive relationships, leading to cascading project failures and nonlinear losses. To solve the risk diffusion problem in project portfolios, this study aims to construct a risk diffusion model that conforms to the characteristics of engineering project portfolios, as a research framework for risk diffusion problems.
We first consider the project interaction relationship and the logical relationship of risk factors. Based on the mutual influence and interaction effect between these two different risk diffusion relationships (project interaction relationship and risk factor logical relationship), we construct a two-layer dependent network model for engineering project portfolio risk diffusion, and analyze the complex heterogeneous relationship of engineering project portfolio risk diffusion. Secondly, the cascading failure theory is used to propose risk diffusion rules and describe the logical path of the risk diffusion process. Finally, through the numerical simulation of the risk diffusion process of the project portfolio, the characteristics of risk diffusion are analyzed to verify the effectiveness and applicability of the model.
The simulation results find that: In the engineering project portfolio, the bi-layer dependent network model can reflect the diffusion characteristics of risks in the dual-relationship carrier of the projects and the risk factors; compared with the single-layer network risk diffusion model, the model shows a certain difference in the risk diffusion effect, the risk diffusion shows a more severe cascading failure process in the engineering project portfolio, and the project portfolio has weaker risk diffusion resistance. In practice, enterprise managers can develop preventive measures based on the risk diffusion characteristics of the model, such as: (1)Protecting important nodes in advance. The important nodes of a network generally refer to nodes with high degree, and the failure of these nodes will quickly affect most nodes in the network, reducing network robustness. Therefore, these project or risk factor nodes need to be closely monitored and managed for resource tilt. (2)Optimizing the risk network structure. Within the network layer, the robustness of the project meta network is improved by establishing redundant interaction relationships, adjusting the interaction relationships of a single project, and avoiding one project completely relying on another project. Between network layers, it is necessary to focus on controlling risk element nodes and project element nodes with many dependent edges, and weaken the interdependence between network layers in advance to suppress risk diffusion across network layers. (3)Backing up interaction relationships. For project interactions, project managers can design backup plans in advance from structural or functional aspects to prevent negative impacts on their own projects after the failure of interacting projects. However, the backup strategy requires an increase in cost and time, and in practical applications, it is necessary to consider the balance between the expenditure and return of this strategy. This model has practical significance for designing a more robust project interaction network for enterprises and improving their ability to cope with risk diffusion.
The risk diffusion model serves as a prerequisite for the risk diffusion problem in engineering project portfolios, and in future work, based on this risk diffusion model, network robustness analysis for risk diffusion will be further improved and applied to study network system stability, security, and other issues. This can provide management suggestions for enterprises to prevent and respond to risk diffusion, and provide new ideas and inspiration for further research on the stability of risk diffusion networks.

An Improved Sequential Auction with Complementarity for Pricing the Construction Land Quota

MENG Weidong, LIU Jingyu, HUANG Bo, LI Yuyu

2024, 33(1):  179-183.  DOI: 10.12005/orms.2024.0027

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With the deepening of industrialization and urbanization, the demand for urban construction land continues to rise. However, under the influence of development strategies such as food security, central government strictly controls the number of local construction land quotas every year, and the construction land resources required for economic development in various regions are seriously short. At the same time, a large number of rural labor has flooded into the city, resulting in idle or inefficient use of homestead.
In order to solve this problem, local governments such as Chengdu and Chongqing introduced the market mechanism into the quota transaction of construction land, but the index transaction price formulated by the cost plus pricing method failed to reflect its market value, and could not encourage farmers to actively withdraw from idle residential land and realize the reactivation of rural stock land. For the past years, scholars have focused on the production process, significance and transaction process of land use index for construction. Although scholars emphasize the defects of the land use index pricing transaction process, they have given policy suggestions from a qualitative point of view. However, they have failed to put forward a targeted pricing model. Therefore, it is imperative for governments to design an operable land use index pricing mechanism to alleviate the contradiction between the high demand for urban land and the low utilization rate of rural land.
Based on this, considering the complementarity of construction land index and construction land characteristics and construction land index system in practice, this paper improves a classic sealed complementary sequential auction model, and uses the improved model to study the pricing mechanism of construction land index.
The first part summarizes the complementarity between the construction land index and the characteristics of the construction land index, and the main characteristics of the construction land index transaction practice, and constructs the construction land index and the construction land quotation model. The quotation of construction land index is affected by the depreciation coefficient of land price and the number of bidders. The price of construction land is affected by the number of bidders, and the price of the loser in the bidding stage of land index is also affected by the depreciation coefficient of land price.
The second part discusses the related factors that affect land use index and land quotation. The index price of construction land has a negative relationship with the total number of bidders, and a positive relationship with the depreciation coefficient of land price. In the land auction stage, the total number of bidders increases and the land price increases. The land price of the loser in the bidding stage and the depreciation coefficient of land price show a reverse trend.
The third part discusses the farmers’land compensation income. Farmers’land compensation income and land price depreciation coefficient have a positive development trend, but have a reverse development trend with the total number of bidders.
The fourth part carries out a numerical analysis according to the relevant data of the construction land index trading practice. This verifies the relevant conclusions of the above theoretical research.
Finally, the conclusion is drawn that local governments can raise the requirements for qualification examination of participants, or increase the collection of penalty fees for idle construction land, in order to improve the transaction price of land use indicators, stimulate the enthusiasm of farmers to withdraw to the greatest extent, solve the problem with low the efficiency of rural land use, improve the current situation of China’s construction land spatial allocation, and make an intensive, efficient and sustainable use of land resources.
Although the sequential auction model of complementary products in this paper provides a new way to reveal the intrinsic value of land use indicators and solve the current land supply and demand problem, further exploration is needed: for example, there are still differences in the price discount rules between Chongqing and Chengdu, and which rules can better encourage farmers to voluntarily recultivate idle residential land is still to be discussed.

Debt Financing Model of Growth Enterprise with the Consideration of Degree of Centralization

LU Xiangyuan, WU Zhiqiao

2024, 33(1):  184-190.  DOI: 10.12005/orms.2024.0028

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Capital constraint plays a key role when it comes to managing firms’operations, particularly for enterprises with certain growth potential in the demand market. Evidence shows that many capital-constrained growth enterprises went bankrupt due to the incorrect choice of financing, for example, Luckin Coffee, which rapidly expanded in the consume market. Most of its retail divisions faced financial constraints and financing options when expanding a potential market, which greatly affected the development of Luckin Coffee. However, there are also enterprises such as Alibaba Group, Xiaomi Corporation, which have succeeded through adopting reasonable financing strategies in the process of market expansion. For example, “ele.me” in Alibaba Group quickly expanded the potential demand of food delivery service in catering market through financing. With the same business that expands the market through financing, why do some enterprises succeed while others fail? In addition, with the rapid development of platform economy and business ecosystem, and the success of businesses such as Alibaba and Xiaomi, there has been a certain degree of centralization between upstream and downstream enterprises in the supply chain. The operation structure of supply chain has gradually become more flexible, presenting an operational structure between complete decentralized and complete centralized, which affects the decision-making of enterprises in the supply chain. Therefore, to discover the reasons for such practices and give managerial insights into financing strategies in the context of market expansion, this paper investigates the financing issues faced by capital-constrained growth enterprise, particularly those of their financing equilibrium between bank credit financing and trade credit financing with the consideration of degree of centralization.
With respect to a two-echelon supply chain comprised of a core supplier and a capital-constrained retailer with a certain level growth potential, this paper analyzes the impact of retailer’s growth potential and supply chain’s degree of centralization on the optimal decision-making and debt financing equilibrium between bank credit financing and trade credit financing. By a Stackelberg game model, the study points out that: when the degree of centralization is low, the supplier’s (retailer’s) decision decreases (increases) with the degree of centralization. When the degree of centralization is high, the retailer’s decision decreases with the degree of centralization. In addition, thesupplier’s decisions under bank credit financing are discrete and affected by risk-free interest rate and the degree of centralization of supply chain. Under trade credit financing, the optimal wholesale price is unit retail price. Furthermore, the optimal financing strategy is always trade credit financing in our research framework.
To the best of our knowledge, our research is the first to study capital-constrained retailer’s financing equilibrium under both bank credit financing and trade credit financing from the perspective of market growth potential. By using the classic Stackelberg game model, our research also gives several relevant managerial implications for the capital-constrained enterprises’ financing options in the context of growth market demand. First, market growth potential is a key factor that should be considered when the downstream enterprises finance from internal upstream core enterprises or external banks. Second, capital-constrained enterprises should choose financing through TCF from internal core enterprises in the context of market expansion.
There are some possible extensions to our current model. First, because of the lack of full information about the capital-constrained retailer, moral hazard arises, as retailers might divert credit to other projects (especially in bank credit financing). Second, the retailer sometimes has more precise knowledge about demand conditions. It is worthwhile analyzing the financing equilibrium in channels with such asymmetric information. Finally, our analysis points out the optimal financing strategies. However, this has not been studied empirically. Therefore, collecting industry evidence to demonstrate our theoretical findings comes as a future research priority.

Research on Customer Credit Risk of Small Loan Companies Based on Mixed SMOTE and RF Model

YAN Qing, XU Haiyan

2024, 33(1):  191-197.  DOI: 10.12005/orms.2024.0029

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The microfinance industry plays a crucial role in providing financial services to individuals who often lack access to traditional banking systems. However, the inherent risk associated with small-scale lending, particularly the challenge of accurately assessing the creditworthiness of individuals, poses a threat to the stability and growth of microloan institutions. The persistent challenge of individual credit risk in microloans continues to hinder the healthy and sustainable development of the microfinance industry. Specifically, the accurate identification of high default-risk clients remains a significant issue for microfinance companies when conducting credit risk assessments. This research holds theoretical significance by proposing a hybrid model that combines SMOTE and RF algorithms to address the challenges posed by high-dimensional and imbalanced datasets in the microloan context. The practical significance lies in its potential to enhance the accuracy of credit risk assessments, providing microfinance companies with more robust tools for making informed lending decisions.
To enhance the accuracy of credit risk assessments, this research leverages real-world data from Jiangsu-based J Microfinance Company. To tackle the challenges presented by microloan business data, the study employs a hybrid approach. The Random Forest (RF) model is initially constructed, followed by the development and evaluation of the SMOTE-RF and Borderline-SMOTE-RF models. These models integrate oversampling techniques with the powerful predictive capabilities of the Random Forest algorithm, aiming to improve the accuracy of credit risk assessments. Support Vector Machine (SVM) is selected for comparative experiments to benchmark the performance of the proposed models.
The empirical testing reveals that the Borderline-SMOTE-RF algorithm outperforms the other models, demonstrating superior classification performance in personal credit risk assessment for microloans. The hybrid approach effectively addresses the challenges of high dimensionality and data imbalance, providing a robust solution for microfinance companies. Furthermore, based on the importance scores derived from the models, six key indicators influencing personal credit risk are identified. These indicators can serve as a reference for some microfinance companies with less mature credit risk management practices. Microfinance companies are encouraged to strengthen the collection and utilization of these crucial pieces of information. The study emphasizes the significance of these indicators in enhancing the precision of credit risk assessments for small-scale loans.
While the Borderline-SMOTE-RF algorithm emerges as the optimal solution for personal credit risk assessment in microloans, further research can explore the impact of manually synthesized virtual samples on indicator importance. However, the introduction of oversampling techniques, particularly the incorporation of artificially synthesized samples, may introduce a certain degree of bias to the ranking of indicators during the crucial selection process. Future research should thus focus on the uniformity of classification performance and indicator importance scores in the context of hybrid algorithms. Analyzing the impact of oversampling on the consistency of indicator rankings will be paramount for ensuring the reliability of the selected key indicators.
In conclusion, this research proposes a hybrid algorithm to effectively address the challenge of low accuracy in identifying high default-risk clients in personal credit risk assessment within the microloan industry. For high-dimensional and imbalanced credit data, the hybrid Borderline-SMOTE-RF algorithm can efficiently identify minority class clients with high default risk, ensuring the cash flow of microfinance companies. Simultaneously, the research scores indicator importance and selects six crucial credit indicators, providing more scientifically informed decision support for the lending operations of microfinance companies.

Research on Financing Efficiency and Delisting of NEEQ Enterprises Based on DEA-Logit Model

YANG Songling, LI Fucai, LIU Tingli

2024, 33(1):  198-204.  DOI: 10.12005/orms.2024.0030

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Since the operation of the NEEQ in January 2013, many small and medium-sized enterprises have competed to list on it. By the end of 2020, the total number of listed enterprises has reached 13,392. However, since 2016, the number of enterprises delisted from the NEEQ has been increasing.According to the statistics of the Choice database, by the end of 2020, 4940 enterprises had been delisted from the NEEQ, with a delisting rate of 36.89%, which is far higher than that on the NASDAQ in the United States and the Shanghai and Shenzhen exchange markets in China.Excessive delisting rates can disrupt the balance of the capital market and harm the interests of investors. The phenomenon of the delisting of companies on the NEEQ is serious, which has attracted widespread attention.Deep study of the reasons for the delisting of enterprises of the NEEQ has an important practical significance for promoting the improvement of the multi-level capital market system and ensuring the high-quality development of the capital market.
In order to explore the reasons for the delisting of a large number of NEEQ companies,this article starts from the financing efficiency, using a total of 7473 annual samples from the Wind and Choice databases, and based on panel data of NEEQ companies for the past three consecutive years during and after listing. This article first uses the DEA model to measure the financing efficiency of the research sample, and conducts a comparative analysis from a vertical and horizontal perspective. Then, based on the Logit model, we empirically test the relationship between the financing efficiency and delisting behavior of enterprises listed on the NEEQ.
The study finds that the overall financing efficiency level of the delisted enterprises of the NEEQ is not high, and there is a significant downward trend during the listing period, which is significantly lower than that of non-delisted companies. Therefore, the decline in financing efficiency is an important factor affecting the delisting of NEEQ companies. In other words,during the listing period, the financing scale and financing cost of delisted enterprises have increased, but their corresponding financing output has not increased correspondingly, resulting in the reduction of financing efficiency. Therefore, from the perspective of cost and income, when the increase in financing cost brought by listing does not bring the expected increase in financing output to the enterprise, or at this time the listing behavior does not bring marginal contributions to the enterprise, then the enterprise is prone to delisting.
The above research conclusions have practical and enlightenment significance. First, for enterprises, they should take innovation and future sustainable development as the ultimate goal rather than listing. Moreover, due to the cost effect of listing behavior, enterprises need to choose whether to go public according to their own actual situation.Listed enterprises should also reduce financing costs, improve financing output, further improve financing efficiency, and increase listing returns through innovation driven. Second, for investors, it is necessary to establish a mechanism for identifying the financing output of enterprises, prioritize the allocation of funds to enterprises with higher financing output, improve market liquidity to play the role of the NEEQ in providing financial services for small and medium-sized enterprises. Third, for the emerging capital markets of other countries, it is necessary to strictly control the entry into the market and screen enterprises with high growth and high capital utilization efficiency to enter the capital market,so that the financial function of the capital market can better serve the real economy of the country.

Time-consistent Optimal Strategy for Multiperiod Mean-variance Portfolio Selection with Real Constraints

ZHANG Peng, LI Jingxin, CUI Shulin, ZENG Yongquan

2024, 33(1):  205-211.  DOI: 10.12005/orms.2024.0031

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Investment can help people resist inflation and achieve the maintenance and appreciation of wealth. How to effectively allocate financial assets is a central issue. The mean-variance portfolio model proposed by Markowitz in 1952 settled the foundation for the development of portfolio theory. The model estimates the return and risk by probability theory, achieving the optimal allocation of assets by trading off returns and risks, with the objective function of maximizing returns under established risk constraints or minimizing risks under return constraints. With the extensive research of scholars, the research content and application of static portfolio theory are gradually enriched and improved.
The static portfolio model assumes that investors hold the initial investment portfolio until the end of the investment period without any adjustments. However, factors such as investors’ preferences, and policies change over time, make it difficult to describe the dynamics of the factors by static portfolio models. In actual financial transactions, investment is a continuous process, and investors will adjust their portfolio positions based on the investment environment. Therefore, the multi-period portfolio theory has gradually attracted the attention of scholars.
Scholars have conducted extensive research on multi-stage portfolio optimization, but most of them provide optimal pre-commitment strategies. The pre-commitment strategy is that when investors formulate a strategy during the initial period, they must ensure that the pre-formulated strategy is executed throughout the subsequent investment period. Each period of the strategy depends on the initial state of the investment. However, in multi-period investments, investors need to adjust their strategies based on existing information. It is obvious that the pre-commitment strategy is not the optimal time-consistent strategy. The time-consistent strategy is that investors can adjust their investment strategies with changes in market information. That is to say, the time-consistent strategy satisfies the separability and has no aftereffect in the sense of dynamic programming. There have been transaction costs and cardinal constraints in multi-period portfolios. Therefore, realistic constraints should be considered when studying portfolio optimization. This makes it difficult to solve the optimal time-consistent strategy.
This paper uses mean and variance to measure the return and risk of portfolios, respectively. Considering transaction costs, borrowing and lending constraints, and threshold constraints, we propose a multi-period mean-variance portfolio model. Since the variance is not separable in the sense of the dynamic programming principle, this model is an optimization control problem with time inconsistency. Because of transaction costs, the multi-period portfolio selection is a dynamic optimization problem with path dependence. To seek a time-consistent strategy, we convert the model into a dynamic programming problem approximately by using a nested conditional expectation mapping and design a novel discrete iteration method to obtain the optimal portfolio time-consistent strategy. In order to keep the effectiveness of the novel discrete iteration method, we first prove its linearity and convergence. Then, an example is given to illustrate the behavior of the proposed model and the designed algorithm. This article randomly selects 20 stocks from the Shanghai Stock Exchange for investment, with sample data from April 1, 2006 to March 30, 2017. The moving average method is used to estimate the returns for the next five periods. In the experiment, this article analyzes the impact of weight parameter changes on the terminal wealth of investment portfolios. It can be concluded that the terminal wealth and weight parameters change in the opposite direction.
Considering realistic constraints, a multi-period mean-variance investment portfolio model is proposed and its optimal time-consistent investment strategy is solved by a discrete iteration method. On the one hand, it enriches the research of modern financial decision-making theory and provides a new idea for exploring dynamic risk control technologies. On the other hand, it helps investors adjust their investment strategies in the dynamic investment and enhances the ability of individual and institutional investors to adapt to the investment environment. This is better for investors making scientific and rational decision strategies. However, due to the existing management costs in the financial market, investors cannot invest in too many assets, and there is a minimum purchase volume for each stock. Therefore, it can be extended by considering cardinal constraints and minimum trading volume.

Evolution and Simulation of Green Credit, Government Regulation and Enterprise Green Technology Innovation

CHEN Haihan, LYU Yiqun

2024, 33(1):  212-218.  DOI: 10.12005/orms.2024.0032

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Green finance represents a novel growth point and serves as a fresh impetus for the advancement of the green economy. It plays an important role in enhancing the ecological environment and promoting high-quality economic development. Currently, the focal point of green finance development in China centers on green credit. Both theoretical and empirical evidence indicates that green credit can effectively facilitate the transition to a green economy and achieve green growth by promoting cleaner production methods, enhancing the efficiency of green technology innovation, and optimizing energy consumption patterns. Nevertheless, as the green credit policy gains traction, challenges such as difficulties in verifying the authenticity of information pertaining to enterprise green innovation, concealment of environmental risks, and the absence of external oversight and guidance mechanisms are becoming increasingly salient. These issues significantly impact the willingness of banking and financial institutions to engage in green credit, thereby impeding the progress of the green financial system. To address these concerns, this paper formulates an evolutionary game model aimed at elucidating the dynamic interplay among enterprises, banking financial institutions, and the government within the context of green credit policy implementation. It further scrutinizes the dynamic influences of the initial participation intentions of decision-makers, government regulations, and the rent-seeking costs incurred by enterprises on the evolutionary system. This examination seeks to provide a certain degree of theoretical foundation and strategic recommendations for advancing the development of green credit.
From the perspective of bounded rationality, this paper analyzes the strategy selections of local governments, heavily polluting enterprises, and banking financial institutions. It proposes relevant basic assumptions grounded in evolutionary game theory. Subsequently, using Wolfram Mathematica 13.0, the revenue payoff matrix and evolutionary stable points are derived. The evolutionary stable strategy of the three-party game system is further delved into across four scenarios. Ultimately, based on the actual situation and initial parameter constraints, and in alignment with model parameter settings related to government regulation, banking financial institution investment, and enterprise green technology innovation, Matlab 2018a is utilized to simulate the dynamic evolution process of strategy choices among heavily polluting enterprises, banking financial institutions, and local governments under varying initial conditions. The influence of each subject’s initial willingness, local government incentives, punishments, subsidies, heavily polluting enterprises’ innovation feedback coefficients, and rent-seeking costs on the three-party game system is analyzed based on the simulation results.
The results show that: A)An enhancement in the local government’s initial regulatory willingness can motivate heavily polluting enterprises to embrace green technology innovation and encourage banking financial institutions to implement green credit practices. Notably, the rate of strategic evolution among heavily polluting enterprises is swifter than that of banking financial institutions. As the initial willingness of both heavily polluting enterprises and banking financial institutions rises, the evolutionary pace of local government strategies decelerates. B)Government incentives and penalties serve as substantial drivers for heavy pollution enterprises to adopt green technology innovation. However, subsidies play a comparatively minor role in nudging banking financial institutions towards green credit strategies. C)The innovation feedback coefficient of heavy pollution enterprises does not alter the regulatory strategies of local governments. Conversely, an escalation in rent-seeking costs favors enterprises in choosing green technology innovation strategies.


Management Science

Exclusive Dealings Behavior of Digital Platform and the Regulatory Strategy

SUN Yong, YANG Ruijia, ZHANG Yafeng

2024, 33(1):  219-225.  DOI: 10.12005/orms.2024.0033

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The success of the digital platform development model depends on the joint efforts of all stakeholders, and its antitrust issue is attracting more and more attention from various countries. In the context of the increasing prominence of network effects and concentration effects in the digital economy, some digital platforms, after developing into super platforms, restrict the development of other competitors by implementing exclusive dealings measures, destroying the market environment of fair competition. In order to deeply analyze the mechanisms and measures of antitrust regulation for digital platforms, this paper takes the exclusive dealings behavior regulation of digital platforms as the realistic scenario, and constructs an evolutionary game model of antitrust regulation among the government, digital platforms and merchants. By using Matlab numerical simulation, the paper studies the influence of related parameters on the strategies of the three parties. The main conclusions are as follows:
First, the ideal equilibrium solution of the evolutionary game shows the role of the government in ensuring the orderly development of the relevant market of digital platforms. By solving the evolutionary game model of the government, digital platforms and merchants, eight special equilibrium points can be obtained. Among them, the strategy sets E3(0,1,0),E4(0,1,1),E5(1,0,0),E7(1,1,0) and E8(1,1,1) are asymptotically stable points under certain conditions, but according to the realistic scenario of the exclusive dealings behavior and antitrust regulation of digital platforms, E5(1,0,0) is the ideal equilibrium point. This indicates that when the government adopts effective antitrust regulation measures, digital platforms will tend not to implement monopoly behavior, and enterprises will tend to cooperate.
Second, the intensity of antitrust supervision and enforcement by the government is an important factor affecting the development of the relevant market of digital platforms. The antitrust intensity of the government has a negative impact on the strategy choice of digital platforms to implement the exclusive dealings behavior, but beyond a certain intensity threshold, it will cause the strategy choice probability of digital platforms and the government to enter a fluctuating and unbalanced state. At the same time, the monopoly position has a positive impact on the strategy choice of digital platforms to implement monopoly behavior. The higher the monopoly position of digital platforms, the stronger the willingness of the government to choose antitrust regulation strategy, and the stronger the willingness of merchants to choose non-cooperation strategy.
Third, different antitrust policy tools may bring different realistic effects. Both antitrust punishment and monopoly damage compensation intensity can reduce the willingness of digital platforms to implement the exclusive dealings behavior. When the government’s antitrust punishment for digital platforms is large or the monopoly damage compensation intensity faced by digital platforms is larger, the system is more likely to form benign steady-state equilibrium, at which time digital platforms tend not to implement monopoly behavior, and merchants also tend to choose cooperation. However, the government’s antitrust punishment for digital platforms has a stronger effect, and merchants are constrained by the monopoly position of digital platforms, and the damage compensation intensity has a weaker constraint on the monopoly behavior of digital platforms.
Overall, this paper fully considers the antitrust intensity of the government and the monopoly position of platform enterprises, and analyzes the mechanisms and measures of antitrust regulation for digital platforms. It makes up for the shortcomings of existing research that mostly starts from a single subject perspective, and lacks systematic and complex research on the monopoly problem of platform economy and the analysis of government’s role. The research results can provide guidance for the policy formulation of regulating the exclusive dealings behavior of digital platforms, and offer theoretical support for maintaining the fair competition and orderly development of the relevant market of digital platforms.

The Way to Ease the Financing Constraints of Firm’s Innovation: Government or Market?

LI Mingshan, DONG Yan, SUN Xiaohua

2024, 33(1):  226-232.  DOI: 10.12005/orms.2024.0034

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Technological innovation is an inherent requirement for transforming economic development mode and achieving sustainable growth, and stimulating micro enterprises’ R&D motivation is a basic premise and important guarantee for the smooth implementation of innovation-driven strategy. Generally speaking, the funds required by enterprises for technological innovation come from both internal and external sources of financing. Since R&D activities require a large amount of initial investment, when internal sources of financing can hardly guarantee the supply of R&D funds, they need to rely on external sources of financing to make up for the shortage of funds. However, R&D activities are characterized by high risks and long cycles, and in the case of information asymmetry, external fund providers are unwilling to take risks, causing the problem of financing constraints for R&D activities. In 2019, China’s social research and experimental development (R&D) spending was as high as 2,214.36 billion yuan, accounting for 2.23% of GNP, but the R&D intensity of large and medium-sized enterprises was only 1.32%, which was a large gap with industrial enterprises in developed countries. As the main body of technological innovation, the lack of capital has become the main obstacle that restricts the improvement of enterprises’ technological innovation capacity, and how to alleviate the financing constraints of enterprises’ R&D activities has become one of the core problems that need to be solved for innovation-driven development. In order to promote enterprises’ R&D investment to reach a socially better level, government departments will formulate incentive policies to supplement enterprises’ R&D funds in order to avoid “market failure”, which is an important supplement to market financing in view of the positive externalities of technological innovation behavior. So, does external financing alleviate the financing constraint of Chinese enterprises’ R&D activities? How effective are the market and governmental approaches in alleviating the financing constraint in practice? This paper will discuss the above questions in detail.
To address the above issues, this paper discusses the intrinsic mechanisms and channels through which financial markets and government public policies alleviate firms’ innovation financing constraints at the theoretical and empirical levels, respectively, using micro data of Chinese manufacturing listed firms from 2010-2019 as the sample. The marginal contributions of this paper are as follows: First, this paper examines the ways to alleviate innovation financing constraints using the modified Euler equation, which not only confirms the existence of financing constraints but also tests the impact mechanisms of different channels, making up for the existing literature’s neglect of the intrinsic mechanism of action. Second, in response to the reality that most enterprises have poor financing channels, the differential implementation effects of different market and government financing channels are empirically tested. And third, the differential effects of alleviating enterprises’ innovation financing constraints under different circumstances are compared and analyzed in three dimensional groupings of ownership nature, marketization level and industry technology intensity to ensure the robustness and reliability of the research findings.
The results of this study show that: There is a general financing constraint in the innovation activities of listed manufacturing companies, equity financing in the market can alleviate the problem with capital shortage, but bank loans and bond financing do not play a corresponding role, or direct financial subsidies in the governmental route do not solve the shortage of funds, and the effect of tax incentives in the form of ex post refund is very significant. From the perspective of different ownership systems, state-owned enterprises have more adequate financing channels, while private enterprises still face more serious financial discrimination, and only tax incentives can play an incentive effect. Regional grouping shows that enterprises in advanced market-oriented regions have richer financing channels, while enterprises in lagging regions have difficulty with obtaining the funds needed for R&D investment through market channels, but public policies do not play the function of “sending charcoal in snow”. The empirical results of different technology-intensive industries show that high-tech enterprises are supported by both the financial market and government, while low-tech enterprises face serious financing difficulties with their R&D investments.

Research on the Impact of State-owned Non-controlling Shareholders on the Value Creation of Private Enterprises

SHEN Jiakun, LI Bingyan, ZHANG Jun

2024, 33(1):  233-239.  DOI: 10.12005/orms.2024.0035

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Based on the theory of creating shared value, private enterprises should integrate social responsibility into their development strategies under the supervision and incentives of state-owned non-controlling shareholders, and obtain economic benefits in the process of achieving social goals. In terms of enhancing the economic value of enterprises, private enterprises pursue the maximization of shareholder value, which refers to the maximum sum of value that enterprises can obtain by participating in market exchange. Due to policy bias and government credit guarantees, state-owned capital often receives preferential economic resource allocation in its investment and financing business. According to the theory of resource dependence, when state-owned capital is shared with private enterprises, it will bring this resource allocation advantage to private enterprises. Therefore, state-owned non-controlling shareholders are conducive to private enterprises in alleviating financing constraints, increasing innovation investment, and enhancing market competitiveness, etc., thus achieving the improvement of economic value. In terms of enhancing the social value of enterprises, private enterprises are responsible for stakeholders, society, and the environment in their business operations, and pursue the maximization of social value in economy, society, and environment. State-owned capital belongs to all the people. According to the stakeholder theory, the main business goal of state-owned non-controlling shareholders is to ensure economic benefits while pursuing the maximization of corporate social value. According to the theory of signal transmission, private enterprises actively undertake social responsibility under the supervision of state-owned non-controlling shareholders’ equity and their appointed directors. The good information conveyed can maintain the social relationship between enterprises and stakeholders, and is conducive to alleviating the information asymmetry problem in the capital market, reducing agency costs, obtaining resources needed for development, and ultimately promoting the long-term development and long-term value improvement of private enterprises.
Based on the dual connotations of economic value and social value of enterprises, this study introduces the state-owned non-controlling shareholders to enhance the economic value creation effectiveness, social value creation efforts, and increase resource tunneling costs of the ultimate controller of private enterprises, and introduces the optimal utility function of the ultimate controller to explore the theoretical impact mechanism of state-owned non-controlling shareholders on the behavior of the ultimate controller of private enterprises, expanding the scope of application of the LLSV analysis paradigm in the field of corporate governance, and providing reference for the formulation of policies to effectively play the positive role of state-owned equity. In the study, theoretical derivation and empirical testing are conducted to obtain the impact mechanism of the separation of the two rights of the ultimate controller and the checks and balances of the state-owned non-controlling shareholders on the behavior of the ultimate controller. The results show that: the appointment of state-owned shareholders can enhance the role of the ultimate controller in enhancing the economic value and social value of the target company, and inhibit the ultimate controller’s tunneling behavior towards the target company; state-owned shareholding only has a promoting effect on the ultimate controller’s efforts to enhance the social value of the company, and has no significant impact on the ultimate controller’s efforts to enhance the economic value and resource tunneling of the target company; the appointment of state-owned shareholders can enhance the economic value of the target company and reduce the tunneling of resources from the target company; state-owned shareholding cannot have a direct impact on the economic value and resource tunneling of the target company; the state-owned shareholders need to strengthen their role in enhancing the social value of the enterprise through shareholding and appointment.