Table of Content

25 May 2023, Volume 32 Issue 5

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Theory Analysis and Methodology Study

Research on Permutation Flow-shop Scheduling Problems with Biogeography-based Optimization Based on Linkage Learning

ZHAO Heng, LIU Yingyan, FU Lipeng

2023, 32(5):  1-8.  DOI: 10.12005/orms.2023.0141

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Permutation flow-shop scheduling problem (PFSP) is different kind of the combinatorial problems and is categorized as NP-Hard problem, which is a very typical production planning problem in the field of shop scheduling. The goal of solving PFSP is devoted to finding out an optimal permutation so that makespan is minimal. To find the solution of PFSP is a common challenge in which an algorithm may be trapped in the local optima of the objective function when the complexity is high, and there are several local optima in solution space. In response to the above issues, this study proposes a biogeography-based optimization based on linkage learning (LLBBO) for solving PFSP with the objective of minimizing the makespan. The main contribution of this study is to propose a novel algorithm that greatly improves the effectiveness and efficiency when compared with previous researches in solving benchmark PFSPs. Moreover, the proposed algorithm is expected to be applied to solving other combinatorial optimization problems.
The algorithm takes biogeography-based optimization as architecture, using opposition-based learning mechanism to generate initial groups. The initial groups are divided into dominant group and disadvantage group according to population fitness, based on which the immigration and emigration rates are calculated and the habitat with high HSI has a higher immigration rate. After that, the information entropy is used to calculate the entropy value of each position of the solution sequence. By describing the degree of clutter of the workpiece on each machine, the key machine with a low degree of clutter can be effectively found out as the location covered by the linkage blocks. The linkage blocks are constructed based on the key locations calculated using information entropy. Linkage blocks are linkage structure units that mine frequently occurring segments at the same location or arrangement in the solution sequence based on the statistical information of the solution sequence of a population. These combination of linkage segments can improve the fitness of the solution sequence. The probability matrix model is used to statistically analyze the solution sequence information and generate an initial linkage fragment containing the key location, which is then combined according to mining rules. Next, we use linkage blocks to simulate the migration operation of BBO algorithms to update the population. Specifically, we select a specified proportion of linkage blocks from the database in a random manner, replace them into a sequence based on the location information of the linkage blocks, and arrange the repetitive jobs in the order that optimizes the objective function value of the solution sequence. In order to further improve the searching ability of the algorithm, a NEH reorganization strategy is proposed to perform the segmentation and reorganization on the solution sequence. Then, the proposed LLBBO uses a random mutation operation to enhance search performance and provide more search targets.
To verify the effectiveness of the proposed scheme, computational simulations are conducted on Reeves's and Taillard's benchmark instances. First, the proposed LLBBO is applied to solve Reeves's instances, and the experimental results are compared with classical algorithms such as BBEDA, HDBBO, and VP-QEA. The results show that the BRE of all instances solved by the LLBBO are the best among the compared algorithms, and the average value of ARE is superior to other compared algorithms except HDBBO. Second, we apply the proposed LLBBO to solve the Taillard's benchmark instances and compare the results with ACGA, BBEDA, LMBBEA, and the convergence graphs of the proposed algorithm on some test instances are given. Compared with other algorithms, the proposed algorithm can achieve better average error rates and minimum error rates on most cases.
This study applies the concept of information entropy to the selection of key locations of linkage blocks. The expansion of information entropy in constructing linkage blocks and the applicability of further development algorithms to other types of combinatorial optimization problems can be studied in the future. Future works can also be extended in applying this method to several other combinatorial problems.

Improved Backtracking Search Algorithm for Multi-objective Flexible Job Shop Scheduling Problem

PEI Xiaobing, DAI Yutong

2023, 32(5):  9-15.  DOI: 10.12005/orms.2023.0142

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Nowadays,it is very important for enterprises to arrange limited resources and optimize specific performance. In addition, the production cycle has become one of the main factors of enterprise competition when the manufacturing cost varies little. With the development of lean production and just-in-time production, the production cycle of products is constantly shortened, and many researchers and practitioners focus on the design of a reasonable scheduling plan for all kinds of scheduling problems to improve market competitiveness. In recent decades, scheduling problem has been considered as a key problem affecting production efficiency.
An expansion of the traditional job-shop scheduling difficulty in flexible manufacturing systems, the flexible job-shop scheduling problem is presented. Compared with many scheduling problems in the real world, the multi-objective flexible job-shop scheduling problem (MO-FJSP) usually involves the simultaneous optimization of multi-conflicting objectives to some extent. Therefore, MO-FJSP may be closer to the actual production environment, should be given enough attention, and continue to be conducted in-depth research. At the same time, with the advent of green manufacturing, reducing energy consumption of manufacturing enterprises is indispensable to sustainable development, and it is also an urgent problem to be solved by every manufacturing enterprise. It is necessary to develop energy-oriented methods to minimize energy consumption. So, the issue of energy target in scheduling is of great importance, and it has been the focus of attention in this area. For this reason, this thesis aims at building the MO-FJSP mathematic model and offering a solving way.
Backtracking Search Algorithm (BSA) is a dual-population Algorithm, which uses the old population to guide the evolution of the new population. The traditional backtracking search algorithm has the disadvantages of weak discretization, premature convergence and limited local search ability. This paper applies the BSA to MO-FJSP, and proposes an Improved Backtracking Search Algorithm (IBSA) with Pareto sorting. Then the mutation operation is dynamically controlled by changing the individual search amplitude factor to widen the search direction. In the iterative process, the population will not be placed in the local optimal state. Because the backtracking search algorithm uses the old population to provide the direction for the search process, which results in the weakening of its late-stage search ability and the limitation of local search ability, a new crossover operator is proposed by combining individual guidance with random number disturbance, which improves the ability of late-stage optimization and prevents premature convergence. Lastly, a receiver criterion is proposed to further enhance the convergence ability of the algorithm.
In order to verify the solvability of the algorithm, Kacem and Brandimarte series benchmark examples are used to simulate the algorithm, and the results of different multi-objective optimization algorithms are quantified. Compared with other methods, the proposed method is superior to that of other methods in diversity and optimal value. Finally, in view of this question, the proposed future research has three directions, which may carry on the further research: (1)Considering that there are more and more constraints in the actual manufacturing system, and the common problems such as machine tool failure, tool degradation and worker flexibility are increasing, IBSA can be applied to FJSP considering the actual constraints in the future, to improve the accuracy of the model. (2)Adding local search operator BSA is a global search algorithm, which has the disadvantage of weak local search ability. Local search plays an important role in balancing the search behavior of multi-objective evolutionary algorithms, so improving the efficiency of neighborhood search will directly affect the overall performance of hybrid algorithms. This paper focuses on the application of backtracking search algorithm in multi-objective combinatorial optimization problems, and in the future, we can further explore and improve the performance of BSA by integrating effective local search operators or using compound neighborhood structure, so as to find a way to circumvent local optimum. (3)In order to compare with other algorithms conveniently, the performance of the algorithm is simulated and verified by two classical benchmark examples in FJSP. In the future, the analysis should be combined with the enterprise case, in order to better contact with the industry practice.

Approximate Dynamic Programming for the Split Delivery Vehicle Routing Problem with Stochastic Demands

SHI Jianli, XIE Lirong

2023, 32(5):  16-22.  DOI: 10.12005/orms.2023.0143

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Split delivery vehicle routing problem with stochastic demands is used to describe some realistic problems like garbage collection or cash collection. In real case, stochastic demands appear gradually over time as information flows. Routing decisions have to be made in an ever-changing process of stochastic information, and when and how the stochastic information appears determines the entire decision making process. Several literatures have focused on split delivery vehicle routing problem with stochastic demands, but have used a prior optimization framework to solve the problem, which cannot incorporate the dynamic nature of stochastic demands and cannot obtain solutions with high level of accuracy. To overcome the problems, an approximate dynamic optimization framework is adapted in this paper.
A bi-level Markov decision process model allowing split delivery is formulated. The upper level model solves the division of the customer set, and decides the customer subset serviced by each vehicle. If some customer is split, the left part of the customer is treated as a new customer with stochastic demand and will be assigned to the customer set of other vehicles. When each upper level decision is made, the state is transformed into the lower level model. The lower level model decides the service sequence of the customer. When some vehicle finishes servicing a customer, the state is transformed into upper level model. The number of stages in the models is not an accurate number for allowing split demands. But based on the assumption that each demand could only be split no more than once, the number of the stages will be finite. So the model in this paper is a dynamic model with finite number of states which is not known before. Incorporating the split demand and multi-vehicle feature, we use another vehicle to serve customers as a recourse to deal with route failure when the residual capacity of a vehicle cannot meet another customer's demand. In order to solve the problem efficiently, a global recourse policies based on the dynamic decomposition and the partial reoptimization based on the approximate dynamic programming are adapted. The efficiency of the model and algorithm proposed in this paper is verified by computational results of two instance sets. One instance set is modified from the classical VRP instance set, Solomon Set, and the other set is generated by a random process which is the most commonly used method to generate instances for VRP with stochastic demands.
The test showed several conclusions: 1)The ratio between the number of the cars in the optimal solution and the expected lowest number of the cars is a little more than 1.2 times, and the number is close to the number of cars in the optimal solution derived from the heuristic. 2)The dynamic decomposition is better than the static decomposition on the expected demand delivery and the expected travel cost, even though it is more time-consuming and more customers split. 3)The optimal solution is, on the average, 2.6 percent and 1.9 percent better than the initial solution obtained by the fixed route policy, respectively on the expected demand delivery and the expected travel cost, and also, the number of split customers is, on the average, 2.5 higher than the initial solution.
Further research will take place in three areas. Firstly, models with more realistic stochastic demand distributions should be developed. As more and more information technologies and big data appear, many advanced information collecting and data processing techniques will be used to collect detailed data and to accurately describe the uncertain characteristics of stochastic demands. Second, better methods for decomposition and reoptimization should be developed. Third, new methods such as robust optimization algorithms should be used to solve split delivery vehicle routing problem with stochastic demands.


Optimization of Supervision Mechanism for Pollutant Abatement under Limited Supervision Ability and Heterogeneous Risk Preference

WANG Xiaonan, GUO Peng, GUO Ning

2023, 32(5):  23-28.  DOI: 10.12005/orms.2023.0144

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In recent years, with the continuous advancement of industrialization, the environmental problems caused by pollutant emissions have attracted much attention from society. It is difficult for regulators to supervise all potential polluting enterprises in a region at the same time, which leads to environmental degradation. Risk preferences may differ between enterprises. Heterogeneous risk preferences can lead to differences in behavior among enterprises. Regulators can determine the type of risk preferences of enterprises by observing their historical decisions, and strengthen supervision of risk-seeking firms that often adopt defection strategies.
In order to provide supervisory strategy support to supervisory authorities, the optimization of pollutant abatement supervision mechanism is studied based on assumptions of heterogeneous risk preferences of enterprises and limited supervision ability of regulators. Aiming at the issue of regional enterprises' pollutant abatement and supervision, a game model on the pollutant abatement alliance network is established combined with theories of complex network and evolutionary game. In this game model, regulators set two pollutant abatement targets: A minimum abatement target and a higher abatement target. The minimum abatement target is the minimum requirement to safeguard the region from environmental degradation. If enterprises fail to meet the minimum target, the supervised enterprises adopting the defection strategy are excepted from the shutdown penalty. If enterprises meet the minimum target, the supervised enterprises adopting the defection strategy are excepted from the shutdown penalty. Fines are imposed by supervised enterprises adopting defection strategies if they accomplish higher targets. Based on bounded rationality theory, we consider that enterprises have risk preferences, including risk seeking and risk avoidance. The distribution of enterprises' risk preferences obeys a uniform distribution. Usually, with limited supervision ability, regulators can only randomly select some enterprises for supervision, and we call this approach a random supervision mechanism. An improved supervision mechanism is proposed considering the enterprises' heterogeneous risk preferences and the constraint of supervisors' limited supervision ability, namely the intelligent supervision mechanism. Under the smart regulation mechanism, the regulator determines the preferences of enterprises by setting up a whitelist and observing historical decisions. Enterprises that are long-term cooperators are whitelisted, and enterprises in the whitelist are not supervised. The regulator randomly selects the supervised enterprises from the ones outside the whitelist. The intelligent supervision mechanism contains two main variables. First, the whitelist length is the maximum number of enterprises that can be accommodated within the whitelist. Second, the observation period is the number of consecutive cooperation strategies adopted by an enterprise that is greater than the observation period requirement to enter the whitelist.
Using numerical simulation, compared with the random supervision mechanism, the effectiveness of the intelligent supervision mechanism is verified. The research results show that different punishment strategies affect the decision-making of enterprises. For example, if faced with the risk of being shut down, enterprises show a stronger willingness to cooperate. In the case of limited supervision ability, the supervision ability and the effect of pollutant abatement are positively correlated. Compared with the random supervision mechanism, the intelligent supervision mechanism can promote enterprises to adopt cooperation strategies and achieve a better effect. The increase in the length of the whitelist can improve the overall cooperation level. If the length of the whitelist is small, an appropriate extension of the inspection period can improve the cooperation level. In contrast, if the length of the whitelist is large, an appropriate shortening of the inspection period can improve the cooperation level.

Financing Cost Minimization Project Scheduling Optimization Considering Time Buffer's Costs and Utilities with Random Activity Duration

NING Minjing, ZHENG Xiaoqiang, YU Xiaozhong, HE Zhengwen

2023, 32(5):  29-35.  DOI: 10.12005/orms.2023.0145

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It has become a common practice to promote the implementation of mega projects through debt financing. However, the high financing costs brought by massive financing have pushed up the total cost of contractors and severely squeezed their profit margins. Contractors urgently need to optimize project cash flow, improve fund allocation efficiency, and reduce financing costs. Project scheduling can change the timing and magnitude of cash flow occurrence, thereby affecting cash flow gaps. Contractors can make financing decisions that are beneficial to themselves based on the financing rates of each period. In addition, contractors usually spend a certain amount of prior cost to add time buffer for activities to ensure the stability of the schedule, which can reduce the cost of changes and adjustments after the event. Obviously, adding a time buffer will further change the project funding gap and contractor financing arrangements.In summary, this study investigates the financing cost minimization project scheduling problem considering buffer costs and utility under random activity durations, which has strong practical value.
The research in the field of cash flow project scheduling mostly focuses on the maximization of net present value and cost minimization, and rarely considers the financing costs of project contractors. Although financing cost minimization scheduling can be nominally classified under the research branch of cost minimization project scheduling, the latter does not consider changes in cash inflows and the further financing changes. Therefore, this study has unique research value.
Firstly, we define the research problem and construct an optimization model. The objective function of the model is to minimize the financing costs of contractors during project implementation, taking into account the cost and utility of inserting time buffers for activities. It should be noted that there is no actual adjustment cost, since we only focus on generating the schedule in advance and does not involve the actual execution. Here, a virtual value is used to replace the actual adjustment cost. Because the post adjustment cost will decrease with the increase of time buffer insertion, the virtual adjustment cost of an activity can bedefined as the minus function of the activity time buffer. By optimizing the time buffer decision variables, the cost structure and cash flow of contractors can be optimized, ultimately reducing their financing costs.
Secondly, a hybrid algorithm VNTS is designed to solve the model. VNTS adopts the TS overall search framework. When a better solution cannot be found in the current neighborhood, the neighborhood transformation function of VNS is used to skip to the next neighborhood to improve search efficiency. It can be concluded that adding time buffers to activities with greater impact and higher risk coefficients can effectively improve the objective function value. Based on this, we propose improvement measures for neighboring point generation: for the time buffer list, evaluate the risk impact level of all activities, and sort the activities according to the value from highest to lowest. Select the activity with the highest value among the unselected activities and randomly change its value to another value on the neighborhood interval.
Then, we test the VNTS hybrid algorithm and improvement measures for neighboring point generation. The example library is obtained by the ProGen based on different parameter settings. The results show that the solution quality of VNTS is superior to the two independent algorithms TS and VNS, respectively, and the difference continues to expand with the increase of problem size.In terms of time efficiency, VNTS is significantly inferior to VNS and TS in all problem scales. After applying neighbor generation improvement measures in VNTS, the solution quality of the algorithm has significantly improved. This indicates that the search direction is determined based on the level of risk impact, which makes the search path more optimal, for taking into account both the variability level of activity duration and its impact on the overall project.
Finally, a case study is used to illustrate the research content of this article. Simulation analysis is conducted on three types of benchmark schedules. It is found that the actual total financing costs of the shortest benchmark schedule fluctuate the most severely. The fluctuation in the actual total financing costs of the benchmark schedule for minimizing financing costs considering buffering costs and their utility is much smaller than that without buffering costs and their utility. The results of the sensitivity analysis show that the contractor's financing cost first declines and then rises with the relaxation of the deadline, first declines and then rises with the expansion of the robustness threshold, increases with the increase of the financing rate, decreases with the increase of the payment proportion and payment times, increases with the expansion of marginal cost, and decreases with the increase of attenuation coefficient. The research results of this article can provide quantitative decision support for contractors to control financing costs under random activity durations.


A Dynamic Weighted A^* Algorithm for the Pre-marshalling Problem

DING Yi, YANG Xuze

2023, 32(5):  36-41.  DOI: 10.12005/orms.2023.0146

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The increasing container throughput and the trend of larger size of container ships make container terminals face great challenges, forcing them to optimize the operation of their logistics system, constantly improve the efficiency of terminal operations and reduce operational costs. During the extraction of target containers, the wrong stacking of containers caused by cranes and ship delays will cause frequent marshalling operations in the storage yard, and further lead to port delays. Therefore, the study of container pre-marshalling plays an important role in improving the efficiency of terminal operation. The goal of the pre-marshalling operation is to eliminate all the wrong stacking containers in the bay as far as possible and reduce the total number of marshalling container by determining the optimal sequence that meets the constraints of the operation rules. Therefore, according to the requirements of practical application scenarios of container terminals, a dynamic weighted A^* algorithm is proposed and developed to solve an optimization model of thepre-marshalling problem considering the double-space rule. This process aims to identify the best sequence of container movements. The designed dynamic weighted A^* algorithm can effectively improve the ability to jump out of the local optimal solution in the optimization process. Based on the pre-marshalling operation of container loading process,the bay layout is abstracted into the state node of the A^* algorithm. The total cost function is calculated by the number of mis-overlay and the depth of iteration. The constructed dynamic weighting heuristic function controls the relocation according to the specific iteration situation. The dynamic weighting factor is introduced to control the direction of iteration, the lower bound weighted factor is introduced to dynamically adjust the lower bound of branches in each iteration, and a novel search branch rule is proposed to select the effective branch to search. Among them, the tie rule provides effective search direction for iteration; The branch rule ensures the efficiency of the algorithm by deleting the repetitive branch nodes and the branch nodes with high expected iterations. In order to ensure the orderly operation in the bay, the double-space rule stipulates that the container cannot be placed in the bayside position at the end of the iteration, and the first left rule is given priority to ensure the safe operation of the storage yard. The experiment is carried out through the example of Shanghai port and existing literature. The effectiveness and stability of the dynamic weighted A^* algorithm are verified by examples of the bay layout, operation process and mis-overlay container as the main data features. The results show that the design of dynamic weighted heuristic function could effectively improve the ability to jump out of optimal local solution. This method can effectively reduce the total number of relocations and improve the efficiency of pre-marshalling operations. When the number of stacks is higher than the height, the dynamic weighted A^* algorithm can improve the average performance by 20.3 %. The dynamic weighted A^* algorithm has better stability, which ensures that a large number of optimal solutions can be obtained in scenarios with different bay sizes. The proposed first branch rule and first left rule can select the optimal iterative branch for searching. The performance of the algorithm using the first branch rule is improved by 12.1%, while the performance of the algorithm without the first left rule is reduced by 3.7%. The performance of the dynamic weighted A^* algorithm considering the double-space rule is not significantly reduced, and a large number of optimal solutions can be obtained under the premise of guaranteeing the performance of the algorithm. The research results can be applied to pre-marshalling problem of container terminals with different operation technology and different bay layout. Furthermore, it can provide decision support for yard operation optimization.


Random Fuzzy Mean-variance Portfolio Selection Based on Pivoting Algorithm

ZHANG Peng, LI Linxin, LI Jingxin, ZENG Yongquan

2023, 32(5):  42-48.  DOI: 10.12005/orms.2023.0147

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Modern portfolio theory has been inspired by Markowitz's pioneering work, which firstly used variance to measure the risk of portfolio and originally initiated the mean variance method. The mean-variance model has been widely used and extended in the research of portfolio optimization. Based on the modern portfolio theory, most mean-variance models were proposed, which assumed that security returns are random variables and the mean of asset returns to measure returns is used.
However, in the increasingly dynamic and complex financial market, historical data is insufficient to accurately estimate the probability distribution of asset returns. The historical data of asset returns alone cannot correctly reflect its future performance, and the probability distribution under this assumption is partially effective. Relying solely on historical data on an asset's return we cannot accurately predict its future performance, so the probability distribution under this assumption is partially valid. In the realistic investment process, there are a large number of non-probability factors. Fuzzy set theory is widely applied in risk management to solve the uncertainty problem. Many scholars have studied the problem of portfolio optimization with asset returns as fuzzy numbers. Actually, the investors may encounter the uncertainty of both randomness and fuzziness simultaneously when handling the practical portfolio selection problem. Asset returns reflect not only the probability distribution of asset returns which may be partially known, but also vague information estimated by experts on the basis historical data and empirical knowledge. Taking the mixed uncertainty of asset returns into account, random fuzzy variables are considered to describe the random fuzzy phenomenon.
Considering the asset return as a trapezoidal fuzzy number, we first define the variance of random fuzzy variable and then employ it as risk measure. In the investment process, investors may face many realistic and objective constraints. In this paper, a new mean variance random fuzzy portfolio selection model with the transaction costs, borrowing constraints and threshold constraints is proposed. Based on the random fuzzy theories, the model is transformed into a convex quadratic programming problem with linear equalities and linear inequalities constraints. The KKT conditions for the proposed model can be obtained. To find the optimal solution, we present a novel improved pivoting algorithm which solves the linear part while maintaining the complementarity conditions in the computational process. The remarkable feature of the algorithm is that many variables are deleted from the KKT conditions, and it is extremely easy to implement.
A numerical example for synthetic data from China securities market is presented to illustrate the validity of the method and algorithm. Assume that aninvestor randomly chooses 20 stocks from Shanghai Stock Exchange for his investment. We collect historical data of them from April 2006 to June 2018 and set every three months as a period to handle the historical data so that the trapezoidal possibility distributions of the return rates of assets can be obtained. We analyze the variation trend of variance when the target return takes different values, and apply the rolling-window method to compare the out-of-sample investment performance of the random fuzzy mean-variance portfolio model and the equally weighted proportion.
In the in-sample analysis it can be seen that the investment proportion of risk-free asset will increase when the minimum target value of the total future profit increases. The out-of-sample analysis shows that the Sharpe ratio of the random fuzzy mean-variance model is higher than the equally weighted portfolio. Compared with the previous research, the random fuzzy portfolio model proposed in this paper is more consistent with the realistic portfolio. The improved pivoting algorithm can solve the optimal investment problem quickly and effectively, which has strong operability and practical value. In the further study, we will research the multi-period portfolio optimization problem under the random fuzzy environment.

A Multi-server Priority Queueing System with Customer Balking, Interjections and Reneging

CHEN Yanting, YANG Na

2023, 32(5):  49-55.  DOI: 10.12005/orms.2023.0148

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Queueing phenomena is very common in our daily life. For various reasons, the customers waiting in the queue may have different kinds of behaviors. We mainly focus on three different customer behaviors here. When an arriving customer finds the queue in the system is too long, this customer may decide not to join the queue anymore, which is called “balking”. For example, during rush hours, the arriving customer who finds a long queue at the bus stop may choose to take the next bus. When an arriving customer finds that the queue is too long, this customer may consider cutting in the queue to reduce the waiting time, which is called “interjection”. For instance, during peak time, the customer in the canteen may try to find a friend in the front of the queue to cut in the queue to obtain the food sooner. For the customers already waiting in the queue, after waiting for some time, some customers may lose patience and leave the queue before receiving any service, which is called “reneging”. For instance, in the inventory system where perishable goods are stored, while waiting for sale in the inventory system, if the goods decay then they have to exit the queue for the original sales plan. The initial investigation of the customer behaviors in the queue considered the impact of only single customer behavior mentioned above on the performance of the queueing systems. Recently, the influence of the combination of two customer behaviors mentioned beforehand on the performance of the queueing systems has been investigated. However, the theoretical exploration of the comprehensive influence of customer balking, interjections and reneging on the performance of the queueing system, possibly with priority customers, is still absent. Meanwhile, the combination of these three behaviors may occur in a real priority queueing system. For example, in the hospital, compared with ordinary patients, the emergency patients have priority, when an ordinary patient finds the hospital is too crowded, this patient may choose to visit another hospital, which is “balking”. Moreover, the ordinary patient may try to cut in the queue to receive the treatment in a shorter time, which is “interjection”. Meanwhile, after waiting too long in a waiting room in the hospital, an ordinary patient may decide to leave the hospital, which is “reneging”. Therefore, from both theoretical and practical aspects, we call for the investigation of multi-server priority queueing systems with customer balking, interjections and reneging.
Firstly, by considering the aforementioned three customer behaviors which are customer balking, interjections and reneging, we develop a multi-server queueing system with three-stage input rates and service rates which depend on the system state. Moreover, we determine the threshold levels of the input and service rates that are based on the revenue & cost structure function. In particular, when there is no priority in the system, i.e., there are only ordinary customers in the system, we have obtained the explicit expressions for the stationary distribution of the system with customer balking, interjections and reneging and the corresponding performance measures. Secondly, we investigate the priority queueing system with customer balking, interjections and reneging. We construct a quasi-birth and death process to model the problem, obtain the stationary distribution of the system via the matrix analytical method, and compute the corresponding performance measures. Finally, we conduct numerical analysis to illustrate that the impact of customer balking, interjections and reneging on the system performance is profound. In the future, we may extend our work to systems with more than two types of customers, where more types of customers have priority over other types. Moreover, apart from preemptive queueing systems, we could also investigate non-reemptive queueing systems in our future work.

Social Network Group Decision-making Method Based on Cloud Model and PageRank Algorithm

SONG Ke, GONG Zaiwu

2023, 32(5):  56-61.  DOI: 10.12005/orms.2023.0149

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In recent years, with the rapid development of information, communication and technology, the arrival of the era of big data has brought a large number of complex and growing data, and the relationship between decision-makers has become increasingly close. In this context, decision makers interact based on social network relationships, and social network group decision-making will produce more information than traditional group decision-making. Factors such as incomplete information, randomness, and limited rationality of decision makers will lead to deviation between group decision-making results and the actual situation. Therefore, in view of the multi-attribute group decision-making problem in which the decision preference information in social network group decision-making is a qualitative concept of linguistic information, and there is a degree of trust between decision makers, this paper proposes a social network group decision-making method based on cloud model and PageRank algorithm, and develops a social network cloud clustering algorithm for the existing cloud clustering algorithm. The specific research contents are as follows: First, the uncertain expression of decision-maker preference information is studied. The golden section generation method based on cloud model can fully express the uncertainty of decision by transforming the language preference information given by decision makers. Then, the construction method of social network topology is studied. The whole social network is regarded as a topological graph, the decision-maker is regarded as a node, and the relationship between the decision-makers is regarded as an edge. The PageRank algorithm is used to calculate the trust degree of each decision-maker in the whole social network topological graph, and the trust degree is converted into the weight of the decision-maker for decision information aggregation. Secondly, the cloud clustering algorithm is studied. Cloud clustering is a clustering method based on cloud model. Most of the existing cloud clustering algorithms only consider the similarity of the initial preference information of decision makers. Due to the complexity of large group decision making in social network, it is obviously unreasonable to consider only the utility of a single factor, which is not suitable for the clustering of large group decision members in the context of social network. This paper improves the existing cloud clustering algorithm to apply to social network group decision-making. Finally, the effectiveness and rationality of this method are verified by an example of emergency decision-making and comparative analysis. The main steps of the method proposed in this paper are as follows: First, we translate language information into cloud model. Secondly, PageRank algorithm is used to calculate the weight of each decision maker based on the social network topology. Then, the social network cloud clustering algorithm is used to divide decision makers into several sub-clusters and calculate the weight of sub-clusters. Finally, the cloud model is integrated into the scheme cloud, and the random simulation technology is used to obtain the score of each scheme cloud, and the preference ranking of the schemes is given. To sum up, the contributions of this paper can be summarized into two points:(1)This paper quantifies the social relationship based on the directed relationship of individual decision-makers in the social network, then uses PageRank link analysis algorithm to simulate the social network of decision-makers to determine the individual's trust degree, and converts the calculated trust degree into weight, which is more in line with the objective reality, and enriches the research on the interaction of decision-makers in the social network. (2)This paper considers the complexity of decision groups in the context of social networks, integrates the utility of decision makers' trust and the utility of decision information, applies cloud clustering algorithm to social network group decision-making, and expands the universality of cloud clustering algorithm. Of course, the method proposed in this paper is not limited to the field of social network group decision-making, but also can be applied to a variety of decision-making scenarios, such as large group decision-making, emergency group decision-making, multi-attribute decision-making, and so on. At the same time, it can also provide some guidance for decision makers when making decisions, provide more scientific and reasonable basis for decision makers, and improve the quality and efficiency of decision-making, so as to improve the government, the overall operation level of enterprises and social organizations. In the follow-up research, finding a reasonable threshold of cloud clustering algorithm and the construction method of social network are also worthy of further research.

The Formation of Cooperative Governance Alliance of Air Pollution Control among Inter-provincial Multi-city: Based on the Evolutionary Game

WANG Yueran, GAO Xianyi

2023, 32(5):  62-70.  DOI: 10.12005/orms.2023.0150

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Air pollution is one of the most serious problems that endanger human survival, and air pollution prevention in our country at present also faces many difficulties. China's air pollution control mode is based on administrative division as the boundary of territorial governance. There is a contradiction between non-territorial and territorial governance of air pollution, so we need to carry out collaborative and cooperative governance, break the inter-provincial barriers of territorial governance, and form the central government as the core of the inter-provincial and multi-city air pollution cooperation governance alliance, in order to alleviate and solve the air pollution problem to the greatest extent.
In this paper, the evolutionary game method is adopted to analyze the stable strategies of air pollution control for multiple urban subjects in a certain inter-provincial region under four situations, namely, the spontaneous formation of cooperative governance alliance under unconstrained territorial governance, constrained territorial governance, and constrained territorial governance, and the cooperative governance alliance breaking territorial governance with the central government as the core. The city government with bounded rationality is taken as the decision-making subject, and an evolutionary game model of air pollution control with multiple game subjects is established. Each variable is represented by letters. Through the assumption of probability and derivation, using the income, cost and damage equalized under urban governance and non-governance as well as the rewards that the central government will give to each city, the expected income and average income under urban governance and non-governance of air pollution are calculated. According to the actual situation of different situations, the relationship between variables is determined, and the replication dynamic equation of urban governance of air pollution is constructed. We need to find the evolutionarily stable equilibrium point, which makes it reach the same probability of the city choosing any strategy in the region. Finally, under the different conditions of the four situations, the evolutionary stability strategy of all cities in the region is determined.
The results show that the high cost of controlling air pollution and the high income of not controlling air pollution make local governments choose to ignore the damage caused by air pollution. At the same time, there exists the phenomenon of “free riding”, which eventually leads to more and more serious air pollution in the region. In the case of constrained territorial governance, although provincial governments punish ungoverned cities, the punishment is not strong enough, and local governments often choose to bear the punishment in order to pursue economic development. At the same time, the punishment intensity of provincial governments is different. Cities in provinces with light punishment value choose to “free ride”, which leads to low governance efficiency. In the case of cooperative governance, alliance forms spontaneously under constrained territorial governance, and cities with high penalty value will absorb cities from provinces with low penalty value into the air pollution control alliance. Cities should enhance cooperation and promote innovation. However, it cannot solve the fundamental problem caused by the different penalty values in different provinces. With the central government as the core to form a cooperative governance alliance, the inter-provincial barriers can be broken, the restraint means of punishment and reward can be used, and the cost of air pollution governance can be reduced to promote air pollution governance. Therefore, the cooperative governance alliance with the central government as the core is the relatively optimal air pollution control scheme.Finally, in order to better study the specific situation of the evolutionary game of air pollution control under various circumstances, numerical simulation is carried out. The evolutionary game is simulated by assigning values to parameters, and sensitivity analysis is carried out in four cases by adjusting the size of parameters. The results of numerical simulation are analyzed and compared, and then the optimal scheme of air pollution control is worked out.
Therefore, this paper provides a certain reference for the design of air pollution control mechanism. In the case of territorial governance, the difference of fines between provinces is the root cause of “free riding” behavior and “high investment and low return”. It is relatively more scientific to break the territorial restriction, unify the punishment amount, and form the air pollution cooperation control alliance with the central government as the core. Under the leadership of the central government, relevant policies on air pollution control can be formulated more efficiently and mutual learning can be carried out, which can further reduce the cost of air pollution control.

Game Analysis in Crowdfunding Supply Chain of Agri-food Considering Capital Time Value

CHEN Jun, WANG Nan

2023, 32(5):  71-77.  DOI: 10.12005/orms.2023.0151

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Agricultural crowdfunding is a new business model introduced from the United States, and its development history in China is short. Crowdfunding funds can generate capital premium over time in production and operation activities, forming the time value of funds. Based on the difficult and expensive financing of agriculture in China, small and medium-sized production enterprises (farmers) are facing a big problem of capital turnover. Crowdfunding agriculture has developed rapidly in recent years because it can lock in demand in advance and ease the financial pressure of producers. However, a kind of organic green agricultural project has a long cycle and high risks. When the return on investment cannot meet expectations, the crowdfunding platform is prone to moral hazard. For example, using the remaining money detained before product delivery for secondary investment appreciation not only occupies the margin paid by the producer for free, but also encroaches on the time value of the funds that should belong to the producer. Farmers' investment awareness is weak, and the time value of funds is easily overlooked, but the crowdfunding platform can form an order of magnitude fund pool. In the existing research, the time value of crowdfunding advance payment is rarely considered in the existingresearch.
Facing the new profit point, whether the manufacturer and the crowdfunding platform consider the time value of funds, the time value of funds is introduced into the decision-making model, and then decision models of agri-food supply chain are established under three conditions: The manufacturer unilaterally considers the time value of funds, the crowdfunding platform unilaterally considers the time value of funds, and the manufacturer and the crowdfunding platform both consider the time value of funds. Using the master-slave game model method, the optimal crowdfunding price, service commission and marketing effort level under the three situations are solved, and the profit comparison is made, focusing on the analysis of the production cycle on the manufacturer and the optimal choice of crowdfunding platform. Referring to the Ankang Hanshui project of organic agricultural product crowdfunding, Matlab14 is used to calculate the key factors and carry out sensitivity analysis. The sensitivity analysis of key parameters such as margin payment ratio, production period and return on investment is further discussed.
The results show that the crowdfunding service commission is the highest when both parties consider the time value of funds, and the lowest otherwise. The level of crowdfunding marketing efforts is the highest when the crowdfunding platform unilaterally considers the time value of funds, and the lowest when the manufacturer unilaterally considers it. The price of crowdfunding depends on the production period and return on investment. In three cases, with the extension of production period, the marketing effort level of crowdfunding platform is increasing when considering the time value of funds unilaterally, which can improve the profits of both parties at the same time. However, regardless of the high or low return on investment, the crowdfunding platform pays the highest level of marketing efforts when considering the time value of funds unilaterally. When the return on investment is low, both parties consider that the time value of funds is actually more beneficial to producers, but the profit of crowdfunding platform will increase rapidly with the increase of return on investment. Therefore, if the crowdfunding platform does not consider the time value of funds, producers should not consider it either; If the manufacturer considers the time value of money, the crowdfunding platform should also consider it.
Considering the time value of funds in the process of crowdfunding in the supply chain of agricultural products will make the capital of enterprises appreciate, but the corresponding financial risks also arise. Therefore, it is the future development direction to construct the correlation function between investment risk coefficient and time value of funds and then study the optimization decision of agricultural product crowdfunding supply chain. Secondly, in the example analysis, the margin payment ratio has different degrees of influence on producers and crowdfunding platforms, so a reasonable payment ratio is expected to improve the Pareto of the supply chain.

Sequential Asymmetric Nash Bargaining on Oil-extraction Strategies

FENG Zhongwei, LI Xiaoting, TAN Chunqiao

2023, 32(5):  78-84.  DOI: 10.12005/orms.2023.0152

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Oil plays an inestimable role in safeguarding national economic and social development as well as national defense security. According to the International Energy Agency, Organization of the Petroleum Exporting Countries (OPEC) supplied 41% of the world's oil in 2020. Therefore, the developing countries play a pivotal role in oil supply. However, due to the backward technology of developing countries, they often delegate the task of oil extraction to enterprises in other countries. It is worth noting that the oil extraction companies have caused severely environmental pollution problems by extracting oil from the ground. On the other hand, the environmental groups oppose oil extraction in order to promote local environmental protection, which is the opposite of the motives of the oil extracting companies. Therefore, the oil extraction companies and the environmental organizations from the developed countries have a significant influence on the oil extraction policies of the government in the developing countries. In other words, it is of importantly practical significance to explore the three oil extraction strategies. The extant literature on oil extraction argues that the government of the developing countries should cooperate with the oil extraction companies from the developed countries to exploit domestic oil, but should not get involved in the influences of the oil extraction companies and the environmental organizations on the oil extraction policies of governments in the developing countries. Although Grossman and Helpman proposed a co-agent model of lobbying based on dual motives of government to maximize social welfare and lobby group payment, studying the influence of companies or environmental groups on government decisions, there is no subordination relationship between foreign companies or environmental protection organizations and the government of developing countries, which causes the oil extraction companies or the environmental organizations from the developed countries to have certain bargaining power in negotiations with the governments of the developing countries. Therefore, this paper uses asymmetry Nash bargaining game to investigate the government's oil extraction decisions in the developing countries.
Notably, the oil extraction companies and the environmental groups negotiate with governments (who may be affected by the payoffs). The government,which only seeks to maximize social welfare, is likely to get more investment, while the government, which is not interested in social welfare, is more likely to be persuaded by direct monetary payments. For this reason, government will assess the fees paid by lobby groups (including the oil extraction companies and the environmental groups). The environmental groups would rather prefer small reductions in the oil extraction than large reductions in trilateral negotiations, since the latter are more expensive. On the other hand, the government can not only set the oil extraction policies, but also decide whether to invite both an oil extraction company and an environmental group to perform the trilateral negotiation, or to invite one of them to perform the bilateral negotiation. If the tripartite negotiations break down, the government can choose one of both parties to negotiate bilaterally. If the bilateral negotiation fails, the government will negotiate bilaterally with the other one. When the negotiation breaks down, the government will choose a social welfare maximization strategy. In view of this, this paper constructs a sequential asymmetric Nash bargaining game model with a government in a developing country, an environmental organization and an oil extraction company from the developed countries, where the government faces pressure or bribery an environmental organization and an oil extraction company and the environmental organization tends to reduce production in a small range in trilateral negotiations. The government selects the negotiation order by sequential optimization method, and the negotiation parties predict the negotiation order by backward induction method. The sequential asymmetric Nash bargaining solution effectively solves the problem that “no externality is allowed and unilateral actions are ineffective”.
The results of this paper are mainly divided into the following three aspects: First, in the tripartite bargaining game a lobby has a stronger impact on government decision-making if the cost of raising funds for a lobby is low or if the government pays more attention to this lobby's payments; Second, the lobby (who would still have impact after a breakdown of the tripartite negotiation) pays lower payments in the equilibrium for the trilateral bargaining game than that in the equilibrium for the bilateral bargaining game; Third, it is shown that the environmental protection organization may benefit from its behavior of maintaining the tripartite negotiation if there is no conflict of interest between this organization and the government.
Our analysis provides some interesting managerial insights: If the profit of the oil extraction companyis high, the ability of the environmental organization to pay the government is limited, the government has nothing to do but cooperate with the oil extraction company. Therefore, strengthening bilateral cooperation is not conducive to environmental protection. In this case, the tripartite negotiation can at least reduce oil extraction to a certain extent, which is conducive to reducing environmental pollution. Future research could further consider how emergencies, such as COVID-19 or a change of government, affect collaboration between lobbies with conflicting interests.

Study on the Strategy of Delayed Payment and Coordination in the Two-echelon Supply Chain with A Financial-constrained Retailer

XU Xianhao, ZENG Kuan, DENG Huizhi, PENG Hongxia

2023, 32(5):  85-91.  DOI: 10.12005/orms.2023.0153

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Delayed payment belongs to supply chain finance and is a financing tool widely-used in retailing, wherein suppliers grant credits for a specified time, and retailers should fulfill the repayment within the limited period. Delayed payments can increase sales for suppliers, resulted from the increased orderings amount by capital-constrained retailers, but they bring non-payment risk, due to the transferred demand uncertainty. For the sake of risk aversion, suppliers usually set interest rates for delayed payments.
In this paper, we consider that a retailer purchases products from a supplier, but faces capital-constrained problem. The supplier would announce the interest rate for delayed payments, and then the retailer decides her ordering amount. After the demand is realized, the retailer repays the debt if her revenue is sufficient, otherwise she declares bankruptcy after repaying her entire realized revenue. In the process, there are three questions: 1)How will the supplier determine the interest rate contingent on the retailer's financial constraints? 2)When will the retailer prefer delayed payments for purchase, and how about her ordering decision based on the interest rate? 3)Does delayed payment achieve the supply chain coordination, if not, how to coordinate the supply chain with contracts?
Our study considers a two-echelon supply chain consisting of a supplier and a financial-constrained retailer under demand uncertainty and constructs a Stackelberg game model to analyze the supplier's optimal interest rate for delayed payment and the retailer's optimal strategy on ordering and payment. In addition, we investigate the supply chain coordination under delayed payment, and two contracts, including buy-back contract and revenue-sharing contract, are employed to coordinate the supply chain. Specifically, we adopt backward inductionto solve the problem and consider two scenarios wherein the retailer purchases with her own capital or the delayed payment. First, we derive the retailer's optimal order quantity, conditional on a given interest rate, and figure out her payment decision by comparing her profits in the two scenarios. Then, contingent on the ordering and payment decisions, we analyze the interest rate and find out the optimal interest rate with the retailer's initial capital level.
The results can be described as: 1) The retailer purchases with delayed payment only when the interest rate is lower than a specific threshold, otherwise the retailer orders with all herinitial capital; 2) Under the delayed payment option, the retailer's optimal ordering decreases with the interest rate, meanwhile the supplier's optimal interest rate will not increase with the retailer's initial capital level; 3) Delayed payments can only partially coordinate the supply chain, and the buy-back contract cannot help to achieve the supply chain coordination under the delayed payment; 4) There exists a Pareto zone wherein the revenue-sharing contract can fully coordinate the supply chain under the delayed payment.
To verify the results, we conduct numerical experiments and show that the order quantity always decreases with the interest rate, and exceeds the optimum ordering amount without capital constraints as the rate is lower than a certain threshold. On the one hand, the profit in a decentralized supply chain always decreases with the interest rate, but the supplier could gain more profits as the interest rate increases until the rate reaches a certain level, and the profit decreases since then. On the other hand, the profit in a decentralized supply chain is always lower than that in a centralized supply chain under the buy-back contract, regardless of the buy-back price, indicating that the buy-back contract cannot achieve the supply chain coordination. Under the revenue-sharing contract, the retailer gains more profits but the supplier earns less as the retailer takes up a larger revenue share, but both of them benefit from the contract when the share stays medium, referred to as the Pareto zone.
Finally, we can provide some insights: 1)Delayed payment can improve the whole supply chain efficiency and benefit all supply chain members, but it goes along with the demand uncertainty transfer, thus suppliers should limit the non-payment risks and set the interest rates based on the retailer's financial status, demand uncertainty and product price. 2)In spite of the efficiency improvement, delayed payment can only partially coordinate the supply chain but cannot reach the optimum, and suppliers can fully coordinate the supply chain under the revenue-sharing contract, instead of the buy-back contract.


Winner Determination for A Logistics Service Procurement Auction under Disruption Risks

QIAN Xiaohu, HUANG Min, YIN Mingqiang, CAI Xinyue

2023, 32(5):  92-97.  DOI: 10.12005/orms.2023.0154

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With the development of the digital economy, logistics becomes an important part of people's everyday life. In recent days, disruption risks frequently happened, and would cause dire consequences for a logistics system. Due to the advantage of reverse auctions in reducing procurement and transaction costs, applying reverse auctions to logistics service procurement activities becomes a new trend. Yet, integrating disruption risks into the logistics services procurement auctions has not been sufficiently investigated. In this regard, this paper focuses on a winner determination problem of a logistics service procurement auction under disruption risks, aiming to improve the safety of a logistics system. A hybrid strategy that includes the fortification, reservation, and temporary outsourcing policies is proposed to mitigate disruption risks. Then, a two-stage stochastic winner determination model that integrates the hybrid strategy is constructed. Since the number of decision variables and constraints exponentially increases at the number of disruption scenarios, an approximation algorithm that integrates a reduced scenario method with a dual decomposition and Lagrangian relaxation method is designed.
The small- and medium-scale numerical instances will be generated randomly, while the large-scale numerical instances would be generated by an established tool called Combinatorial Auction Test Suite. Simulation experiments are conducted to compute the above numerical instances by using the proposed approximation algorithm and the CPLEX solver, respectively. We find that for small-scale problems, the proposed method can obtain optimal solutions as the CPLEX solver, but the computing time is much less. For medium- and large-scale problems, the proposed method can obtain near-optimal solutions, since the gap between the lower and upper bounds is very small, while the CPLEX solver cannot give a feasible solution in more than 90 hours when the number of disruption scenarios is sufficiently large. The simulation results verify the effectiveness and applicability of the proposed model and method. Sensitivity analysis is also conducted to provide managerial insights for the auctioneer. First, the proposed hybrid strategy outperforms other known strategies in mitigating disruption risks, since the auctioneer will pay a higher total cost if other known strategies would be employed. To achieve a lower total cost, the auctioneer shall make a trade-off between the fortification, reservation, and temporary outsourcing policies carefully. Second, a sufficient fortification budget can benefit the auctioneer. If the fortification budget is sufficient, then the temporary outsourcing policy becomes less important, while the fortification policy becomes more important, especially when the unit temporary outsourcing cost is relatively high. If the fortification budget is insufficient, then the auctioneer can only resort to the more expensive temporary outsourcing policy, and the total cost will increase. Third, when the auctioneer faces higher disruption risks or clients' demands, the fortification strategy is more significant, which can not only mitigate disruptions, but also expand bidders' capacities. If the unit temporary outsourcing cost is low and the disruption probability is high, then unsatisfied demands can be fulfilled by other suppliers out of the reverse auction with a relatively low total cost. If the unit temporary outsourcing cost is high and the disruption probability is high, then the total cost will increase by using other expensive suppliers out of the reverse auction to fulfill the unsatisfied demands, indicating the importance of the fortification strategy. If the disruption probability is low, then the fortification strategy would be more important only if the original capacity of the bidder that needs to be fortified is relatively low.
This paper not only extends the research field of reverse auction, but also provides methodological and technical support for logistics services procurement auctions.

Dynamic Evolution of the Relationship among Economic Policy Uncertainty, Exchange Rate and International Capital Flows in China

JIANG Yuanying, CHEN Binxia, ZHOU Donghai

2023, 32(5):  98-105.  DOI: 10.12005/orms.2023.0155

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At present, the international economic and financial landscape is undergoing profound adjustment, the external economic environment is becoming more complex, and the domestic economic environment is becoming more dependent on economic policies. The situation of internal and external problems has led to a gradual increase in the frequency and intensity of economic policies formulated and implemented by countries. Increased economic policy uncertainty can exacerbate macroeconomic operational risks, such as domestic consumption and investment, which can lead to exchange rate volatility. In addition, increased economic policy uncertainty prevents international investors from accurately assessing the risks arising from the policy adjustment process, which has a significant impact on international capital flows. In turn, changes in exchange rates and international capital flows can have far-reaching effects on economic policy uncertainty. The objective of this paper is to examine the dynamic evolution of the relationship among short-term international capital flows, exchange rates, and economic policy uncertainty using a variety of analytical approaches.
This paper makes important contributions to both the academic and practitioner communities. First, this paper provides a research framework for the VAR family model. Specifically, in order to avoid unwarranted model setting, this paper first conducts a nonlinearity test based on the BDS and RESET methods. The results show a significant nonlinear relationship among the exchange rate, international capital flows and China's economic policy uncertainty. Furthermore, this paper compares three types of vector autoregressive (VAR) models with linear and nonlinear structures based on Bayesian model comparison criteria. This paper improves the lack of basis in the selection of econometric models in the relevant empirical literature, and also validates the nonlinearity test results. Second, this paper captures the time-varying characteristics, stochastic volatility and spillover effects among short-term international capital flows, exchange rate and Chinese economic policy uncertainty through various dynamic analysis methods, including stochastic volatility analysis and three-dimensional time-varying impulse response analysis. This paper rationalizes the dynamic interactions and shock effects among short-term international capital flows, exchange rates and Chinese economic policy uncertainty, which helps regulators to formulate and adjust various policies and effectively promote a series of measures such as financial reforms.
Our empirical results are as follows. The TVP-SV-VAR model has the largest log marginal likelihood, suggesting that the evolutionary history between economic policy uncertainty, exchange rates, and international capital flows cannot ignore time-varying features and the effects of stochastic volatility.There is a significant immediate transmission effect of exchange rate change shocks on international capital flows, but the transmission of international capital flows to the exchange rate is relatively weak. RMB depreciation significantly increases China's economic policy uncertainty, while increased economic policy uncertainty in turn causes RMB appreciation in the short run. In addition, the impact of increased Chinese economic policy uncertainty on international capital inflows is more pronounced, and after 2012, the shock effect of Chinese economic policy uncertainty on both the exchange rate and international capital flows is significantly stronger.
Since this paper only considers the case of China, there is still room for further exploration of the relationship between short-term international capital flows, exchange rates, and economic policy uncertainty. In the future, we can consider expanding the sample size to different countries for further research. This paper is supported by the National Natural Science Foundation of China(71963008; 71601048)and Guangxi Natural Science Foundation Joint Incubation Program(2018GXNSFAA294131). We would like to express our deep gratitude to them.

Differential Pricing of Closed-loop Supply Chain Considering Fairness Concerns and Two-way CSR Investment

YAO Fengmin, XING Yan, YAN Yingluo, SUN Jiayi

2023, 32(5):  106-112.  DOI: 10.12005/orms.2023.0156

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With the rapid development of social economy, the problems of environmental pollution and resource shortage are increasingly prominent. In order to improve the recycling rate of resources and achieve sustainable development, China issued the “Implementation Plan for the Extended Producer Responsibility System” in 2016, which required manufacturers to recycle and reuse waste products, and also promoted the development of remanufacturing industry. However, many original manufacturers are unwilling to engage in remanufacturing activities for the sake of brand protection or maintaining core competitiveness, so they authorize remanufacturing to professional remanufacturers. In reality, on the one hand, more and more enterprises pay more attention to corporate social responsibility (CSR) while pursuing profits in order to achieve sustainable development. On the other hand, while pursuing economic benefits and fulfilling CSR, enterprises will also pay attention to the fairness of profit distribution in supply chain channels, that is, enterprises will pay attention to not only the gains and losses of their own interests, but also the profit gap with other members.
Therefore, this paper raises the following research questions: Firstly, how does the two-way CSR investment of original manufacturer and remanufacturer affect the differential pricing, recycling decision and performance of closed-loop supply chain? Secondly, how does the fairness concerns behavior of remanufacturer affect the CSR investment level, patent fees, and the differential pricing strategy of new products and remanufactured products of closed-loop supply chain members? Thirdly, what are the differences in the impact of remanufacturer's fairness concerns about information symmetry on differential pricing strategies and performance in closed-loop supply chains?
In order to solve the above problems, based on the closed-loop supply chain with remanufacturing authorization, this paper constructs the differential pricing decision models of closed-loop supply chain with fairness neutrality, fairness concerns & information symmetry and asymmetry. Through the comparative analysis of the equilibrium results of the three models, the following conclusions are drawn: The CSR investment level, patent fees and new product pricing of the original manufacturer are negatively related to the fairness concerns level. When the fairness concerns information is asymmetric, the fairness concerns behavior of remanufacturer has no impact on the decision of original manufacturer, while its own reverse CSR investment level and waste recycling price are negatively related to the fairness concerns level. Moreover, whether the information of fairness concerns is symmetrical or not, the fairness concerns behavior of remanufacturer will promote the price reduction of remanufactured products and the reduction of sales of new products. The fairness concerns behavior of remanufacturer will harm the interests of original manufacturer and whole closed-loop supply chain, and only when the fairness concerns information is symmetrical, the fairness concerns behavior of remanufacturer will benefit itself. The research in this paper provides a theoretical reference for pricing decisions in closed-loop supply chains considering fairness concerns and CSR behavior.
The future research can explore the impact of the two-way CSR investment competition behavior of original manufacturer or remanufacturer and the fairness concerns behavior of different members on the pricing decision and operation of closed-loop supply chain.

Retailers' Internal Equity Financing Decisions from the Perspective of Supply Chains

LIN Qiang, LI Chuying, LUO Xinggang, LIN Xiaogang

2023, 32(5):  113-119.  DOI: 10.12005/orms.2023.0157

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Small and medium-sized enterprises (SMEs) create value for economic development, but face significant obstacles due to funding shortages in their operations. SMEs have difficulty obtaining financing loans directly from commercial banks due to their lower credit ratings, poor financial compliance, and inadequate cash flow. As a result, they face challenges of expensive and difficult financing. However, equity financing is one of the important ways to help SMEs solve financing difficulties. Nevertheless, due to the relatively low regulatory requirements and incomplete financial disclosures of SMEs, fraudulent behavior can occur in order to increase valuation levels. For independent third-party investment institutions, it is difficult for them to correctly assess the valuation of SMEs due to information asymmetry and industry investment barriers, leading to financing failures. In contrast, as a member enterprise in the supply chain, the supplier is familiar with the overall industry situation. Moreover, the retailer and supplier have close business transactions, and the supplier is more likely to know the production and operation situation of SMEs. If the supplier, as the core enterprise, provides internal equity investment to the retailer with funding constraints, it can effectively avoid a series of problems caused by information asymmetry in the traditional private equity financing market. In addition, whether the investor holds the decision-making power of the enterprise after the internal equity financing in the supply chain is also a problem that needs further consideration and discussion. Generally, investors can influence the enterprise's decision-making process through adding “earn-out clauses” during the financing process. The content of the earn-out clauses can be financial indicators such as net profit margin, or non-financial indicators such as market share and user growth. In this paper, the supplier as an investor can determine the level of effort for the retailer to expand the market, which meets the non-financial performance agreement. In summary, the research questions in this paper include: 1)The impact of internal equity financing in the supply chain on the optimal operational decision-making and profits of the retailer and the supplier, namely, whether the retailer will choose financing and in which mode the financing will be reached; 2)After internal equity financing in the supply chain, what is the impact of the retailer's or supplier's control over the decision-making power of market development on the profits of both sides?
The present study investigates a two-tier supply chain system consisting of one supplier and one retailer. The retailer is a financially constrained SME, while the supplier is a financially rich core enterprise. Based on this, a supplier-led Stackelberg game model is established. Under three scenarios of no equity financing, retailer control (R mode), and supplier control (S mode), the optimal decision-making behavior and profit situation of both parties are discussed and analyzed. Furthermore, by comparing and analyzing different financing scenarios, and the following conclusions are drawn:
i)Compared with no internal equity financing in the supply chain, using the R mode of financing will increase the supplier's profit, but the retailer's profit may not necessarily increase, depending on the company's growth potential. If the retailer has high growth potential, using internal equity financing in the supply chain under the R mode can indeed increase its profit. However, if the retailer has low growth potential and blindly pursues financing and market expansion, it may actually decrease the retailer's profit.
ii)Compared with no internal equity financing in the supply chain, using the S mode of financing will also increase the supplier's profit, but the retailer's profit will decrease. At the same time, under the S mode, the supplier will reduce the wholesale price of the product and expand the market by setting a higher effort level. Therefore, if the retailer is in a highly competitive industry, the retailer can consider sacrificing short-term profits, accepting the supplier's investment, and helping the company gain a competitive advantage.
iii)Through a comprehensive comparative analysis of the profits of the retailer and supplier under the three scenarios of no equity financing, R mode, and S mode, we find that: Firstly, both parties can obtain higher profits by controlling the decision-making power to expand the market. However, even if the retailer has decision-making power, its profit will still be lower than the pre-financing level when the growth potential is low. Secondly, in order to expand sales channels and obtain higher profits, suppliers are typically motivated to provide equity financing to downstream retailers who face limited capital. Thirdly, the key factor affecting whether the retailer will carry out internal equity financing in the supply chain and which financing mode the two parties will use for cooperation is the retailer's growth potential. This means that if the growth potential is low, the retailer should reject the financing. If the growth potential is moderate or high, the retailer and investor will reach a financing agreement under the R mode.

Reliability and Resilience Analysis of Power Distribution Network Based on Importance Measures

DUI Hongyan, ZHENG Xiaoqian, CHEN Liwei

2023, 32(5):  120-125.  DOI: 10.12005/orms.2023.0158

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Due to the increased frequency of extreme weather, the incident rate of widespread power outages in the distribution network has also gradually increased, leading to serious economic losses. Disruption events are inevitable and difficult to predict. After power distribution network suffers from disruptive events, a key step to quickly improving power distribution network resilience with limited resources is to identify the critical nodes that have a high impact on power distribution network resilience improvement.The identification of critical nodes in the power distribution network can help managers to allocate resources scientifically after hazards and quickly restore the power supply capacity of the power distribution network.Due to the unpredictability of disruption events, in addition to the study of the resistance of the power distribution network, the study of how to quickly recover the performance of the power distribution network after natural hazards has also attracted attention.How to combine the importance with the resilience model, calculate the importance of different nodes in the power distribution network after the occurrence of disruption events, and identify the key nodes that have a large impact on the resilience of the power distribution network is still an open research problem. In this paper, the reliability and recovery model of the power distribution network after multi-node failure is investigated.Based on the concept of maximum power supply capacity, combined with the resilience index, the optimal resilience model of the power distribution network is proposed so that the power supply capacity of the distribution system is maximized after disconnection due to partial node failure, when stable operation can be maintained and other nodes are not overloaded.Disruption events may lead to multiple node failures in the power distribution network. When multiple node failures occur, the maintenance strategy focuses on determining the order of maintenance of the failed nodes in order to maximize the recovery capability of the power distribution network, i.e., to the best of its ability within a certain time period.In this paper, we focus on the impact of restoring a single node on the remaining resilience of the distribution network, establish the resilience importance index of the nodes, and perform importance analysis on the faulty nodes, so as to determine the optimal maintenance order of the faulty nodes in a power distribution network with multiple node failures.Finally, the IEEE14 busnode standard test system is used to verify the practicality of the proposed model. The IEEE14 busnode standard test system contains 14 bus nodes and 20 branches, where node S1, node S2, node S3, node S6 and node S8 are generation nodes of the distribution system; node T7 and node T11 are distribution nodes of the distribution system; node D4, node D5, node D9, node D10, node D12, node D13 and node D14 are users in the power distribution system.When the power distribution network is subject to a disruption event, it goes through a resisting phase and then gradually adapts to the impact of the disruption event to reach the stabilization phase, at which nodes S2, S6, T11, T7, D4, D10 and D14 fail. The optimal emergency maintenance strategy needs to be given during the stabilization phase to enable the power distribution network to quickly restore power supply capacity and reduce economic losses. The resilience of the power distribution network is restored to its maximum value when the faulty nodes S6, T7, D4, D10 and D14 are maintained urgently relative to the resilience of the power distribution network after the other faulty node groups are maintained. Therefore, with limited resources and unit emergency maintenance time, only one faulty node can be maintained.Based on the resilience importance, at time 1, the emergency maintenance of the faulty node S6 has the greatest impact on the resilience of the power distribution network; at time 2, the faulty node T7 should be maintained urgently; at time 3, the faulty node D4 should be maintained; finally, the order of maintenance of nodes D10 and D14 has the same impact on the resilience of the network.

Multiple-level Projection of Indexes and the Analysis of Causes on A Fuzzy Evaluation Conclusion

MA Zhanxin, SI Qin

2023, 32(5):  126-131.  DOI: 10.12005/orms.2023.0159

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In the process of comprehensive evaluation, decision-makers not only need to obtain the evaluation results of an evaluated object, but also need to know the reasons leading to such results and the feasible optimization paths. The data envelopment analysis (DEA) method and fuzzy comprehensive evaluation (FCE) method are two very important but mutual independent evaluation methods. If the correlation between the two methods can be found, it not only can enhance the advantages of the two methods each other, but also may provide new perspectives and tools for the solution of the above problems. In addition, it will be a very difficult task to find the improved information of synthesized fuzzy indicators by applying DEA method.
To address these issues, this study first constructs a fuzzy multi-level possible sets based on the existing or obtained FCE information, and provides the definition of fuzzy multi-level projection and its measurement method. Then, these methods are applied to analyze the cause of FCE results, and find the improved strategies of multi-level fuzzy objects. Finally, the proposed method is illustrated with an empirical study of the tourist satisfaction of 14 tourism provinces and regions in China.
Based on the results of fuzzy evaluation, this paper further analyzes the existing problems and improvement direction of the tourist satisfaction of 14 tourism provinces in China. The results of the empirical analysis show that the decision-makers can obtain evaluation information and correlation relationships at different levels within the fuzzy object by applying the method in this paper. These results not only reveal the improvement direction and feasible scale of the evaluation object, but also find a feasible way to integrated DEA and FCE.
Due to the lack of previous work on applying DEA ideas to improve FCE methods, this study is only a preliminary attempt, and still needs to be further improved and deepened. For example, the fuzzy operator selected in this paper is the weighted average operator, which is the most commonly used. However, the FCE method has other operators such as the main factor-determining type or the main factor-emphasizing type. Furthermore, more methods can also be used to construct the possible set of evaluation results. Further research in these areas will play a positive role in improving the application ability of multi-level FCE method and enriching FCE techniques.

Research on the Minimal Hitting Set Problem with Redundancy

JING Caixia, CAI Weimin, ZHANG Lei, LI Zuozhi, TIAN Hongzhen

2023, 32(5):  132-137.  DOI: 10.12005/orms.2023.0160

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In the production of oil and gas on offshore platforms, there may be leaks in their production and transportation devices. Once leaked, it not only pollutes the marine ecological environment, but also causes explosions when the concentration reaches a certain value, posing a threat to the safety of personnel and equipment. In order to closely monitor the leakage and concentration of gas, combustible gas detectors are usually installed on the platform. The detection range of each combustible gas detector is limited and the detectors are expensive, so it is necessary to optimize the layout of the detector to minimize economic costs while ensuring safety. In the past, the layout of detectors was mostly determined based on empirical criteria in actual production, lacking scientific and systematic methods. This article establishes a mathematical model and designs a heuristic for the optimization of the layout of combustible gas detectors on offshore oil extraction platforms, in order to provide a more scientific and systematic layout plan.
Based on the mechanism and characteristics of the layout optimization problem of combustible gas detector, a mathematical programming theoretical model is first established. Then, based on production practice, the theoretical model is dimensionally reduced and simplified: (1)Due to the layout height of detectors is decided by the density of the gas to be leaked, the layout height can be determined in advance, thus the model can be simplified from three-dimensional to two-dimensional. (2)Lower limit of concentration and upper limit of response time are considered for deciding whether the monitoring system will alarm, so the modeling can just consider the state at a certain time, thereby omitting the consideration of time dimension. (3)By digitizing graphics using grids, continuous optimization problems can be transformed into discrete optimization problems, simplifying the problem while also improving operability. Through the above processing, an approximate application model is obtained, which can be regarded as a variant of the minimal hitting set problem, that is, the minimal hitting set problem with redundancy. The computational complexity of minimal hitting set problems with redundancy greater than or equal to 2 are proved to be NP complete by using the method of polynomial reduction. Combined with the application background of layout optimization of combustible gas detectors, a heuristic is designed to solve the minimal hitting set problem, whose redundancy is 2. The heuristic prioritizes the situation where detectors have to be layout, and then takes a series of measures to maximize the number of leakage points that each detector may cover, that is, to achieve the sharing of leakage points with detectors as much as possible, aiming to minimize the number of required detectors while ensuring redundancy.
In the simulation experiment, a CHARM 3D simulation model is established based on the Navisworks 3D model of a certain oil extraction platform structure in the South China Sea, and diffusion simulation is conducted,assuming that the gas to be leaked is methane, gas diffusion simulation is conducted in 6 wind directions, and the heuristic is implemented using C language programming. The simulation results show that this heuristic can detect any potential leak point in any wind direction by at least two detectors without significantly increasing or reducing the number of detectors installed, thereby greatly improving the security of the platform.
Our next step is to conduct systematic research on the minimum hitting set problem considering redundancy and design a complete algorithm.

Application Research

Transnational Dynamic Spillover Effects of Political Risk: An Empirical Study Based on the Spillover Index Model

ZHOU Meijing, CHEN Jinyu, HUANG Jianbai

2023, 32(5):  138-144.  DOI: 10.12005/orms.2023.0161

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The continuous advancement of globalization has made the political and economic links between countries in the world increasingly close. Major risk events in one country are easily transmitted to other countries through direct or indirect channels. The global financial crisis in 2008, and the subsequent European debt crisis triggered turmoil in financial markets and political situations all over the world, giving a vivid example of the global linkage of political risks. In recent years, affected by the UK's “Brexit”, the China-US economic and trade friction, the COVID-19 epidemic and other events, the global political landscape has become more complicated, and geopolitical tensions have escalated and affected an increasingly wide range of areas. So how strongly does political risk spread across countries? Namely, to what extent can political risk in one country affect other countries? Answering this question is helpful to prevent or alleviate external political risks, as well as ensure the stable development of society and economy all over the world. The impact of political risk on macro- and micro-economic activity, as well as the cross-country spillover effects of variables related to political risk, has been extensively tested in previous research. However, no studies have directly verified the spillover effects of political risks among countries, and obtained the empirical results of transnational transmission of political risks. In this context, by using the spillover index method proposed by Diebold and Yilmaz in 2012 and 2014, the spillover relationship of political risks in 22 major global economies during July 1984 to August 2020 is calculated.
The research contributions of this paper mainly lie in the following three aspects. First, from the static and dynamic perspectives, this paper quantitatively calculates the overall and directional spillover indexes of political risks among 22 global economies, comprehensively captures the time-varying characteristics and correlation structure of spillover effects, so as to reveal the transnational transmission path of political risks. Second, this paper makes a detailed exploration of the peak period of the political risk spillover index, and analyzes the impact of major global economic and political events on the transnational transmission of political risk. Thirdly, taking China as the key object of investigation, this study makes an in-depth analysis of the outflow and inflow level of political risks between China and other 21 countries, and defines the spillover characteristics of political risks between China and major economies in the world, so as to provide policy suggestions for China to effectively prevent the negative impact of international political risks. The data used in this study is a monthly index of political risk in 22 major economies from July 1984 to August 2020. The 22 major economies are selected based on a combination of the list of G20 countries and the top 20 countries in terms of global GDP in 2018 and 2019, including 19 countries from the G20 and three countries that are not included in the G20 but among the top 20 countries in terms of GDP. Given that the International Country Risk Guide (ICRG) is the only institution that provides monthly assessment results of political risk, and uses the largest number of indicators to assess political risk, this study takes the political risk index by ICRG as a proxy variable of political risk. The index evaluates political risk from 12 aspects, with higher scores indicating less political risk for countries.
The research results mainly include the following four aspects. First, the average dynamic spillover effects of all countries are 45.16%, and the total dynamic spillover index is between 35.33% and 62.36%; Political risks show transnational contagion effect, and are transmitted through multiple direct and indirect correlations among countries such as politics, economy, society and culture. Second, the outflow index of political risk in each country has a larger fluctuation range than the inflow index, which again confirms the characteristics of transnational spillover of political risk; the outflow and inflow levels of developing countries are slightly higher than those of developed countries, indicating that developing countries in the stage of rapid development face more political uncertainties than developed countries; In addition, Saudi Arabia, Turkey and South Africa, among the major developing countries, show strong characteristics of spillover in both static and dynamic aspects, thus raising the overall spillover level of developing countries; Among developed countries, the spillover effects of political risk in Japan are highly volatile. Third, in general, China's political risk spillover effect is small, ranking low among all countries, with the fluctuation peak occurring after China's accession to the WTO and during the global crisis, indicating that China has a relatively stable political environment and strong ability to resist external interference; China shows larger net spillovers to developed countries than to developing countries. Lastly, Australia, India, and Japan have a strong two-way spillover relationship of political risk with China; South Africa is the main recipient of Chinese political risk spillover, while Switzerland, Argentina and Spain are the main sources of Chinese political risk spillover.

Fuzzy Game Analysis of Considering Public Participation in the Third Party International Environmental Audit of Enterprise

QU Guohua, ZHANG Zhijie, LI Chunhua, Tan Kaichao, QU Weihua, XU Yan, ZHOU Xiaohui

2023, 32(5):  145-152.  DOI: 10.12005/orms.2023.0162

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The environmental pollution caused by the traditional manufacturing mode of enterprises has seriously hindered the sustainable development of ecological civilization. How to promote the independent transformation of enterprises to green manufacturing mode is a big problem faced by our country. As the main body of economic activities, enterprises are the main source of pollution, and the public is an important participant in environmental governance. Promoting public participation in environmental governance will increase the motivation for enterprises to carry out environmental governance. The behavioral decision of both sides is restricted by many factors. Enterprises take up the information superiority position in the game with other governance subjects. The problem of information asymmetry often exists among the game players of environmental governance, and relevant studies prove that the third party international environmental audit can effectively reduce the adverse impact of this problem. Therefore, it is of great practical significance to study the influence and existing problems of public participation on the environmental behavior of high-polluting enterprises under the third-party international environmental audit.In addition, the third-party international environmental audit applications have limitations. In order to overcome this problem, this paper applies the third party international environmental audit to the game strategy composed of triangular fuzzy numbers, and provides another explanation for the application of the third party international environmental audit under the fuzzy environment, so as to expand the application scope of the third party international environmental audit.
Existing third-party international environmental audit models often use traditional classical mathematical theories, which cannot match the uncertain behavior of real activities. Fuzzy game is a game in which the strategy set or winning function is blurred. This method is objective and computationally uncomplicated when dealing with uncertain problems.Based on the triangular fuzzy number and game theory, the game model of public participation in environmental supervision and enterprises joining the third party international environmental auditing is established, the factors affecting the selection of public and enterprise game strategy and its interaction mechanism are discussed. Finally, the triangular structure element method is used to solve the numerical example, which proves the correctness and feasibility of the conclusion.Data simulation values in this paper are derived from China Environmental Annual Report, China Environmental Statistical Yearbook, statistical data from published journals, and official websites of local environmental protection bureaus and ecological environment bureaus.
The results show that compensation for loss of public health, the cost of public participation in regulation, invisible benefits of public goodwill increased, the difference of the profit and cost of joining the third party international environmental audit for enterprise play an important role in deciding whether to join the third party international environmental audit for enterprises.In addition, through the sensitivity analysis of the changes in the values of the main parameters, the game equilibrium results of both sides show that in order to promote the implementation of positive environmental behavior by enterprises, the cost of enterprises joining the third-party international environmental audit should be reduced as far as possible, and the public health loss paid to the public due to environmental pollution caused by enterprises not joining the third-party international environmental audit should be increased. Based on the game model analysis results, this paper puts forward the following countermeasures and suggestions from the perspectives of the public, enterprises and the government.

Research on the Impact of Brand Owner Manufacturer's Self-remanufacturing Decision on Retailer's Fleeing Behavior

CHENG Hongya, MENG Lijun, HU Yuqing, HUANG Zuqing

2023, 32(5):  153-160.  DOI: 10.12005/orms.2023.0163

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The rapid development and maturity of e-commerce and logistics supply chains have provided a good market foundation for the operation and layout of multinational companies in various industries, both at home and abroad. Apple, a giant in the mobile phone industry, and LVMH,one of the world's largest luxury goods groups, are actively developing and distributing various markets in the world. But at the same time, due to differences in market size and consumers' perceptions of products, these cross-market enterprises are facing interference and threats from retailers' stock fleeing behavior, and have many negative impacts on authorized channels' regular products, brand owners, and local market operations. Therefore, in the context of downstream retailers' stock fleeing behavior, this paper explores the impact of brand manufacturers' self-remanufacturing decisions on retailers' stock fleeing behavior and analyzes whether remanufacturing decisions can become an effective management tool for brand manufacturers to respond to the gray market. The research results have certain reference significance for brand manufacturers to implement remanufacturing decisions and control the gray markets.
We consider a cross market supply chain structure model consisting of a brand manufacturer, a retailer, and two independent markets. Looking at the relevant research literature on the gray market, it can be found that most studies have neglected to discuss it from the perspective of remanufacturing, in response to the gray market problem caused by unauthorized retailers fleeing goods. This paper constructs a model of brand manufacturers' non remanufacturing and brand manufacturers' self-remanufacturing, mainly using game analysis methods to explore the inhibitory effect of brand manufacturers' self-remanufacturing decisions on retailers' stock fleeing, as well as the impact on corporate profits, consumer surplus, and social welfare. Firstly, under the condition of brand manufacturers' non-remanufacturing, from the perspective of consumers' evaluation of the value of gray products, this paper analyzes the impact of the gray market on brand manufacturers and fleeing merchants. Secondly, when brand manufacturers conduct remanufacturing on their own, the effectiveness of remanufacturing decisions in controlling fleeing behavior is explored from the perspective of consumer willingness to pay and the economy of remanufacturing. Finally, numerical simulation is used to analyze the changes in the total benefits of the supply chain, consumer surplus, and social welfare,that is, the impact of remanufacturing decisions on retailers' stock fleeing behavior and the inhibitory effect on gray market intrusion from multiple dimensions.
The theoretical and numerical analysis results indicate that downstream retailers' behavior of fleeing goods is not always beneficial to their own profits, as brand manufacturers use price leverage to adjust product prices, which will restrict the sales profits of fleeing retailers. Brands' self-remanufacturing strategy can effectively control retailers' stock fleeing and reduce the price of gray products, which is an effective gray market management method. The higher the economy of remanufacturing (c_r<c_n/2), the more significant the control effect. Brands should increase the promotion of remanufactured products, enhance consumers' value evaluation of remanufacturing products, and further resist the invasion of gray products. In addition, when the economy of remanufacturing and consumers' evaluation of the value of remanufacturing are above a certain threshold, brand manufacturers' self-remanufacturing decisions can improve consumer surplus and social welfare.
The model and conditions constructed in this study are relatively ideal, and factors such as consumer type segmentation, and the size differences of different independent markets can be considered for later discussions. The theory and results obtained in this study provide reference and guidance for international enterprises operating across markets to manage the gray market and make remanufacturing decisions based on product characteristics, and provide theoretical support for enterprises to reasonably respond to market fleeing behavior.

Fuzzy DEA Cross-efficiency Evaluation Method Based on Prospect Theory

MEI Xinnan, WANG Yingming

2023, 32(5):  161-167.  DOI: 10.12005/orms.2023.0164

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Data envelopment analysis (DEA), as a non-parametric statistical method dealing with evaluation problem that utilizes multiple inputs to produce multiple outputs, uses linear programming models to evaluate the relative efficiencies of a group of homogenous decision making units (DMUs). Due to the uncertainty of the decision-making environment and the limited knowledge of decision-makers in real applications, the input and output data of DMUs may be characterized by fuzzy numbers, such as interval numbers and triangular fuzzy numbers. At the same time, the subjective preference of decision-makers (DMs) under uncertain conditions has an important influence on the decision-making process. Aiming at the evaluation of the relative efficiencies of decision-making units in fuzzy environments, a variety of traditional fuzzy data envelopment analysis (FDEA) models are developed in the literature. However, these models may obtain unrealistic results and lack the discrimination power to distinguish efficient DMUs. Meanwhile, the currently published papers involving fuzzy cross-efficiency not only require considerable computational efforts to obtain fuzzy cross-efficiencies, but also adopt the arithmetic average method to aggregate cross-efficiencies and ignore the subjective preference of decision-makers such that the relative importance attached to the cross-efficiencies provided by the different decision-making units are neglected and a convincing result is hard to be attained. The significances of this paper can be summarized as the following two aspects: For one thing, this paper enriches the theoretical research of the cross efficiency method of fuzzy DEA. Both the fuzzy DEA model and the improved interval cross-efficiency model in this paper measure the efficiency of decision making units are based on the unified production frontier, so as to ensure the efficiency comparability between different decision making units and under different α values. For another, this paper expands the application of prospect theory to the efficiency evaluation problem in fuzzy environment. The interval reference point is defined instead of the traditional single reference point, which fully considers the influence of the change of decision-makers' psychological factors on decision-making behavior in the process of aggregating cross-efficiency.
To measure relative efficiency of DMUs in fuzzy environments, the fuzzy DEA cross-efficiency evaluation method based on prospect theory is proposed. Firstly, the α-level-based approach is applied to turn the inputs and outputs data represented by triangular fuzzy numbers into interval numbers, and then an improved interval cross-efficiency model is proposed, which considers all the optimal weights of decision-making units. Subsequently, the prospect theory is introduced to study the problem of interval cross-efficiency aggregation, and the interval reference point is defined to replace the traditional single reference point. Based on the principle of maximizing the prospect of cross-efficiency value of decision-making units, a model is constructed to solve the aggregation weight, and aggregate interval cross-efficiency value to replace the comprehensive prospect of cross-efficiency value. Finally, the preference degree approach is used to compare and rank the interval cross-efficiency values, which can provide a more comprehensive and reasonable result. The proposed method measures efficiencies of decision-making units based on a unified production frontier, ensuring that efficiencies of different decision-making units and under different α values are comparable. Moreover, the interval reference point takes the changes of the decision-maker's psychological factors into full consideration in fuzzy environments. Aggregating the interval cross-efficiency values of decision-making units rather than the comprehensive prospect value retains as much decision information as possible. At last, in order to illustrate the feasibility and validity of the proposed method by comparing it with different approaches, this paper adopts an example that evaluates the efficiency of ten decision making units. Each DMU has two inputs and two outputs, and all input and output data are characterized by triangular fuzzy numbers. Compared to interval DEA models and fuzzy cross-efficiency models proposed by other scholars, it is observed that the result of these methods is significantly different the proposed method. On the one hand, the interval DEA models solve the self-evaluation efficiency of DMUs, and each decision making unit evaluates from the perspective that is most beneficial to itself, resulting in overestimation of efficiency and other problems. On the other hand, fuzzy cross-efficiency models proposed by other scholars don't adopt a unified production frontier to measure the efficiencies of decision-making units. Most importantly, the use of the arithmetic average method has no way to take into consideration the DM's subjective preferences in the efficiency aggregation process. This paper provides an effective way to measure the performances of DMUs in fuzzy environments, and it can avoid the efficiency overestimation problem and obtain a unique ordering of the DMUs.
In this paper, the input and output data of DMUs are both characterized by triangular fuzzy numbers. In future research, we plan to study the approach to evaluate the efficiency of DMUs with different types of fuzzy numbers. Besides, measuring the performances of DMUs can be extended to a hesitant fuzzy environment and regret theory is utilized to replace the prospect theory.

Forecast of Exchange Rate Volatility Based on Data Decomposition and Integration and High-frequency Data Modeling

LI Yongwu, QIN Yiwen, LI Jian, WANG Yashi

2023, 32(5):  168-174.  DOI: 10.12005/orms.2023.0165

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Fluctuations in the foreign exchange market have a wide-ranging impact on the entire economy. The exchange rate is one of the important variables in the foreign exchange market. It is expressed as the exchange rate between the two currencies, which represents the purchasing power of one country's currency on other countries' commodities. Exchange rate fluctuations refer to changes in the value of exchange rates that fluctuate up and down, including currency depreciation and appreciation, and also refer to changes in the value of a currency relative to another country's currency. Volatility is an indicator that measures the quantity and frequency of exchange rate changes. Exchange rate volatility is an indicator to describe the degree of change in the income of foreign exchange financial assets, and it is also one of the methods to measure foreign exchange risk. Exchange rate fluctuations have an important impact on the economic and financial systems. Due to the non-stationary and nonlinear characteristics, accurate forecasting of exchange rate volatility has always been the focus and difficulty of financial research. As a measure of risk, volatility modeling is important not only to researchers trying to understand the dynamics of volatility, but also to policymakers and regulators. Because it is closely related to the operation and stability of the financial market, and the financial market is directly related to the operation and fluctuation of the real economy. Volatility is also an important input parameter for portfolio decision-making models or option pricing, so the measurement of exchange rate volatility is related to forecasting, which is of great significance to investment decision-making, derivatives pricing, and risk management.
With the development of information technology, machines are doing more and more “smart” things such as recognizing faces in photos, recognizing voices, and making predictions. The current research work shows that the forecasting effect based on machine learning is better than other traditional forecasting models. This paper mainly uses machine learning algorithms to build a forecasting model of RMB exchange rate volatility. In order to improve the accuracy of forecasting exchange rate volatility, this paper adopts the realized volatility calculated based on high-frequency data of RMB exchange rate and machine learning method to decompose, integrate and model the data (main data sources: Monopoly data center and Wind database) , an efficient multiscale EEMD-PSR-SVR-ARIMA forecasting model is proposed. The specific process is as follows: First, the complex time series is decomposed into eigenmode functions and trend items of different scales by using the method of Ensemble Empirical Mode Decomposition (EEMD). The core of the SVR algorithm process is the kernel function. What it does is transform the original input space into a high-dimensional space where linear decision boundaries can be easily identified. In the prediction process, the phase space reconstruction method is used to calculate the optimal input dimension of support vector regression, the particle swarm optimization algorithm is used to select its optimal parameters, and then the support vector with the optimal input dimension and optimal parameters is adopted. The regression model makes predictions for different eigenmode functions. On the basis of the EEMD-PSR-SVR model, we build a multi-scale model to improve performance, and use a divide-and-conquer method. The intrinsic mode function is partially predicted by SVR. The trend item belongs to the low-frequency subsequence, and the ARIMA model is used for prediction. For linear low-frequency, the time series has a good prediction effect, which reflects its good at capturing linear trends. Finally, the prediction results of different intrinsic mode functions and trend items are integrated as the final result. The principles of “decomposition and integration” and “divide and conquer” also effectively improve the prediction accuracy and direction prediction accuracy of the model.
At the same time, in order to comprehensively evaluate the prediction effect of the model proposed in this paper, different reference group models will be introduced for comparative analysis, and the prediction performance of the model can be evaluated from three aspects: The relative level of prediction, the absolute level and the direction of prediction. The empirical results show that the EEMD-PSR-SVR-ARIMA model can effectively improve the accuracy of exchange rate volatility prediction. Facing the complex and changeable foreign exchange market environment, there are still a lot of problems in the study of exchange rate volatility, which need to be explored and studied. With the vigorous development of machine learning methods, especially deep learning methods, we will further explore the modeling and prediction of volatility.

Construction of the Intuitionistic Fuzzy Concept Extension Based on Factor Space and Its Application in Decision Making

LYU Jinhui, GUO Sizong, WANG Peizhuang

2023, 32(5):  175-180.  DOI: 10.12005/orms.2023.0166

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Concept is the carrier of knowledge, and its expression and definition are of great significance for the development of modern intelligent science. Due to the difficulty of computers in recognizing the connotationrepresentation of concepts, the field of artificial intelligence attempts to define concepts through extension representation. The essence of the extension representation of concepts is set representation, and therefore, how to determine the membership relationship between objects and concepts is the core issue. In the factor space, based on the Zadeh maximum extension principle and the minimum extension principle, the concept of max type representation extension and min type representation extension are proposed respectively. Furthermore, the feedback extension and envelope of the concept are proposed. Feedback extension envelope is essentially an approximation of concept extension. It should be pointed out that the underlying foundation of this approximation construction is the extension principle. When there is a large sample of data, the extension principle will inevitably lose a large amount of information during the information extraction process, resulting in low approximation accuracy. Considering the ambiguity and uncertainty of concepts, especially when facing complex decision-making problems, the intuitionistic fuzzy relationship portrays the fuzziness of the real world more objectively and delicately, and therefore, discussing the description of intuitionistic fuzzy concepts in factor space is of important practical significance.
Based on this, this article proposes the concept of intuitionistic fuzziness based on the theory of factor space, and starts from the original concept and its opposite concepts, constructs two approximations of intuitionistic fuzziness concept extension using the maximum extension principle and the minimum extension principle, and then defines the double envelope of concept feedback extension (which is essentially a bilateral approximation of concept extension). On this basis, a DFE decision-making method based on feedback extension double layer envelope is proposed. Finally, the operational steps are provided and the above theoretical method is applied through an example.

Storage Space Allocation for Outbound Containers in Automated Container Terminals Using Simulation-based Optimization

YU Mingzhu, LIANG Zhuobin, JIN Bo

2023, 32(5):  181-189.  DOI: 10.12005/orms.2023.0167

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Automated container terminals have been more and more emphasized all around the world. Automated container terminals are facilitated with complex infrastructures designed specifically to handle a large number of containers, and they are playing important roles in international freight transport. In the automated container terminals, there are automated stacking cranes (ASCs) that move containers into and out of the container yard blocks, and automated guided vehicles (AGVs) that move containers inside the terminal yard. The layout and facilities in automated container terminals are different from those in traditional terminals, which bring more challenging operational problems.
Container terminal operators must deal with a wide variety of interrelated logistic problems every day, and the container space allocation in the terminal yard will impact the efficiency and productivity of the terminal yard. In the container terminal yard, the space allocation of outbound containers directly affects the loading efficiency and the berthing time of vessels in the port. Nevertheless, in the existing researches on container allocation problems, the outbound container arriving sequence and the stacking strategy are not considered as the key factors during the space allocation. Management strategies considering these factors are therefore necessary to increase the efficiency and productivity, and thereby reduce the operational costs of related container terminals.
This paper studies the space allocation of outbound containers in an automated container terminal. The random berthing of vessels and random arrivals of outbound containers at the terminal are considered and an integer programming model is established. Some existing researches based on optimization algorithms have been proposed to tackle this problem but they do not pay attention to the uncertain arriving sequence of containers and block balance during the space allocation.Considering these characteristics, we provide a mathematical formulation where the objective is to minimize three parts: The working time of AGV and ASC in the future vessel loading process, and the imbalance between different blocks during the space allocation. We propose a so-called “far-destination container above near-destination container” policy to locate the containers in a certain bay, and a simulation-based genetic algorithm is proposed to solve the problem. In the existing research, the number of rehandles in the container retrieval process has not been well estimated. In the proposed algorithm, a simulation module is established to evaluate the rehandle number during in the future container retrieval process. The output of the simulation module is returned to the genetical algorithm as the fitness measure. The algorithm overcomes the flaws of slow convergence, falling into the local optimum, and premature convergence of the genetic algorithm.
In the computational experiments, different instances are generated to test the effectiveness and efficiency of the algorithm. The experimental results show that the proposed algorithm can solve the problem efficiently. We conduct the sensitivity analysis considering different initial block layouts, different outbound container arriving sequences, and different weight coefficients of objective function. Through the computational experiments, it is found that the average container rehandle number and the imbalance of the blocks increase with the number of allocated containers. The proposed “far-destination container above near-destination container” policy proposed in this paper has good performance in reducing container rehandles.
The research results of this paper provide management insights for the practical space allocation in automated container terminals, and contribute to the efficiency improvement of other related operations in the terminal. The simulation-based genetic algorithm proposed in the paper provides a new solution for the related optimization problems with random factors. This research also enriches the container terminal operation literature.
There are some future research directions: 1) To consider the rolling time horizon model for the container space allocation, in which both space and time factors could be involved in container allocation, and 2) to consider the AGV waiting time in the water side, specifically AGV waiting time for the quay cranes to unload the container from the AGV in the water side.

Time-varying Multivariate NBS Copula Model and Visualized Dependence Analysis of American Stock Index Futures Market

XIAO Zhenyu, WANG Jie, LI Shanshan, SHI Kuiran

2023, 32(5):  190-196.  DOI: 10.12005/orms.2023.0168

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There is a complex dependent structure among the return rate series of financial assets. Multivariate NBS Copula is not only suitable for two- and higher-dimensional dependence structure modeling, but also contains multivariate normal copulas and multivariate symmetrical NBS Copula subclasses. The correlation parameter matrix, tail parameter and skew parameter vector can also be used to flexibly describe the positive and negative dependence, tail dependence and asymmetric dependence between pairs. It is also necessary to consider the time-varying dependence in the modeling dynamic structures. However, there are few researches on the dynamics of the multivariate skew ellipse Copula,mainly including DCC-GHST Copula model, GAS-GHST Copula model and DCC-NCCN Copula model. DCC-GHST Copula model, GAS-GHST Copula model and DCC-NCCN Copula model. In this study, a new time-varying NBS Copula model, namely DCC-NBS Copula model, is proposed for a detailed visual analysis of the dependent structure of the return of the three major stock index futures in the United States.
Kendall's rank correlation coefficient, QD (Quantitative Dependence) coefficient and the normal score's Maria skewness and kurtosis are used to measure the dependence characteristics of the joint distribution. On this basis, a multivariate NBS model is constructed. The parameter set of its distribution includes a correlation parameter matrix, a tail parameter and a skew parameter vector. In order to make the relevant parameter matrix dynamic, the DCC model is introduced and the multivariate time-varying DCC-NBS Copula model is constructed. For the given dependent structure data, the ML (Maximum Likelihood) method can be used to obtain the parameter estimates.
Taking Dow 30 index futures of the Chicago Board of Trade (CBOT), S&P 500 index futures of the Chicago Mercantile Exchange (CME) and NASDAQ 100 index futures as research objects, daily closing price data from January 1, 2005 to December 31, 2020 are selected to calculate daily logarithmic returns. Each closing price series has 4118 data, so each return series has 4,117 data. Data come from the website https://cn.investing.com and are calculated by Matlab software programming.
In order to verify the relative effect of the DCC-NBS Copula model, the CCC-N Copula, CCC-t Copula, CCC-NBS Copula, DCC-N Copula and DCC-t Copula models are selected for comparison. By fixing all skew parameters of NBS Copula, based on CCC-N Copula model, in the dualistic case, three common Archimedes copulas are also considered, namely the Clayton Copula, Gumbel Copula, and Frank Copula. In order to reflect the characteristics of the dependent structure and the model effect, Kendall rank correlation coefficient and Quantile Dependence coefficient are used for visualization analysis, including global and local dependence, symmetric and asymmetric dependence, static and dynamic dependence of the return series.
According to the descriptive statistics of edge distribution, the yield series of the three major stock index futures in the United States have obvious fat tail, asymmetry and time-varying volatility. According to the fitting results of the marginal distribution, NAGARCH model can describe the dynamic characteristics of the rate of return series well. According to the descriptive statistics of the dependence structure, the three major stock index futures in the United States have positive dependence, fat-tailed dependence, asymmetric dependence and time-varying dependence. Among them, the asymmetric dependence shows that the lower tail dependence is stronger than the upper tail dependence. In the dualistic case, the three kinds of Archimedean Copula perform the worst, and the fitting effects of normal Copula, t Copula and NBS Copula improve in turn. Compared with the CCC Copula model, the time-varying Kendall rank correlation coefficient sequence of the DCC-NBS Copula model is basically consistent with the Kendall rank correlation coefficient sequence of the moving sample. It shows that the binary DCC-NBS Copula model can better describe the time-varying dependence of the sample, therefore, and the fitting effect of CCC Copula and DCC Copula also improves in turn. According to the fitting results of dualistic dependence structures, elliptic Copula is superior to Archimedean Copula, asymmetric ellipse Copula is superior to symmetric ellipse Copula, thick-tailed ellipse Copula is superior to normal ellipse Copula, and time-varying ellipse Copula is superior to static ellipse Copula. Overall, the new DCC-NBS Copula model has the best performance. Further research shows that the fitting effects of multivariate dependent structures are basically consistent with those of dualistic dependence structures.

Research on the Heterogeneous Influence of Investors' High-frequency Sentiment on Stock Market Trading Volume: Based on Quantile Vector Autoregressive Model

REN Xianling, LYU Yuzhuo, DENG Lei

2023, 32(5):  197-203.  DOI: 10.12005/orms.2023.0169

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According to “Research Report on Individual Investors in 2019” released by Shenzhen Stock Exchange, the structure of Chinese stock market investors is mainly retail investors. Compared with institutions, retail investors are at a relative disadvantage in terms of information acquisition, screening and analysis ability, and are more susceptible to market sentiment and irrational behaviors, which aggravate stock market turbulence. In theory, the trading behavior of investors is the basis of the capital market. The traditional financial theory takes the assumption of rational man as the premise, focuses on the fundamental information of the market, and ignores the impact of individual investor behavior on the stock market. Behavioral finance theory holds that investor psychology and other micro factors will influence the choice of investor behavior, thus affecting the stock market.
Considering that the stock market trading volume represents the active degree of capital trading in the stock market and reflects the conversion of market trading sentiment, it is an important index for studying the stock market. In addition, under different market conditions, the influence of investor sentiment of different polarity on the stock market may be heterogeneous. Therefore, based on the high-frequency perspective, this paper analyzes the heterogeneous characteristics and mechanism of investor sentiment on stock market trading volume, which is of great significance for our financial risk management and control.
This paper selects the time from January 29, 2018 to September 30, 2019 as the sample period, divides the stock market into three market states: bull market, bear market and volatile market according to the price trend, and tests the robustness of the samples taken during the COVID-19 outbreak. The trading volume of Shanghai Composite Index is taken as the research object, and the data comes from Wind database. Using crawler technology, based on the stock forum post of “East Money Information” as the information source, text analysis is used to construct the investor high-frequency sentiment index with half an hour as the unit, and the data is divided into optimistic and pessimistic emotional polarity, so as to study the intra-day effect of different polar emotions on the stock market, and analyze the characteristics and mechanism of the emotion effect in the stock market from the perspective of high frequency. Furthermore, this paper uses the quantile vector autoregressive model to study the asymmetry between variables, especially the heterogeneity of the tail extreme quantile, and uses the quantile impulse response analysis to study the heterogeneity of the impact effect of investor sentiment of different polarity on stock market trading volume in terms of impact intensity, reaction speed, response time, etc.
The results show that:(1)The influence of investors' high-frequency emotions with different polarity on stock market trading volume is obviously different, and the pulse intensity of investors' pessimism on stock market trading volume is significantly greater than that of investors' optimism, and the attenuation is relatively slow.(2)The influence of investors' high-frequency sentiment on the trading volume of the stock market at the same point varies with the change of market state.(3)In the same market state, the influence of different sub-points on trading volume also has heterogeneity, the pulse intensity of high-frequency investors on trading volume at the lower sub-point is significantly greater than that at the upper sub-point, and the pulse intensity at the middle point is the weakest.
Through the research of investors' high-frequency sentiment and stock market, it is found that the stock market under extreme conditions is more likely to be “dominated” by investors' sentiment, which is not only reflected in the intensity of impact, but also more durable in duration. The research conclusion provides new evidence for the study of stock market changes and affirms the important value of incorporating investor sentiment information into the influencing factors of stock market changes; The use of quantile regression technology can effectively capture the tail information of skewed data, and also provide decision-making reference for regulatory authorities to control extreme risk.

Research on the Financing Efficiency and Influencing Factors of Listed Port Companies in China

WANG Min, LI Huayu, FU Lei, WU Xiaofen

2023, 32(5):  204-210.  DOI: 10.12005/orms.2023.0170

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The port is a kind of logistics infrastructure, which plays the functions of loading and unloading, transshipment, distribution and storage of goods at home and abroad.In the context of China's continuous expansion of the level of opening up to the outside world to adapt to the trend of globalization, ports, as the transport hub of goods around the world, play a key role in connecting domestic and foreign markets and promoting the open economy. The port enterprises are economic organizations with independent legal person status, which operate and account independently and assume sole responsibility for their profits and losses. In order to achieve the business objectives of the enterprise, port enterprises engage in port production, circulation or service economic activities. Port enterprises have played a series of functions such as cargo handling and transportation, industry, commerce and information, which have accelerated the modernization of the port itself and also made great contributions to the development of China's international trade.
But at the same time, due to the characteristics of large investment, long construction period and slow economic cost recovery of port enterprises, financing has become a problem that must be solved in the development of port enterprises. In recent years, with the global economic situation facing downward pressure, the development of the port industry has been hindered to some extent. In addition, China's port listed enterprises are facing problems such as high asset-liability ratio, irrational financing structure, and lack of financing tools, which lead to low overall financing efficiency of port listed enterprises. In this context, whether listed port enterprises in China can achieve reasonable allocation of enterprise funds, improve their own financing capacity, and achieve efficient financing has become a key link in the smooth and healthy development of China 's port industry. It is of great significance to evaluate and analyze the financing efficiency of port enterprises accurately and effectively for solving the financing problems of port enterprises and promoting the development of port industry.
The possible contributions of this paper are as follows: At present, there are few studies on the evaluation of financing efficiency of port enterprises, and dynamic efficiency analysis is not involved. The financing of port enterprises is a long-term and dynamic process. Therefore, the research on the dynamic efficiency of port listed enterprises is more in line with the actual situation. In addition, the relevant research on the factors affecting the financing efficiency of port enterprises and the ways to improve the efficiency also needs to be supplemented.This paper constructs the panel data of port listed enterprises, carries out comparative analysis from multiple angles of time and space, points out the problems of port listed enterprises' financing through empirical research, and puts forward path suggestions for port listed enterprises to improve their financing efficiency.
This paper selects the relevant data of 17 Chinese listed port companies from 2015 to 2019, using DEA and Malmquist index to measure the financing efficiency of them from two dimensions, static and dynamic, and constructing a Tobit regression model to study the influencing factors and degree of their financing efficiency. The empirical research results show that: (1)The financing efficiency of listed port companies is not high, and most companies fail to achieve DEA effectiveness while only 29.41% of them do so. The reason for the low technical efficiency is mainly due to the low pure technical efficiency, which indicates that the port listed enterprises have deficiencies in capital utilization and management, enterprise operation and decision-making. (2)During the study period, the Malmquist index of listed port enterprises has fluctuated up and down, with an overall downward trend. The reason is that the company's scale is too large, resulting in a series of problems such as company and capital management inefficiency, resulting in scale inefficiency. (3)There is a significant correlation among the capital structure of the companies, degree of equity concentration, and macroeconomic development status of enterprises and the financing efficiency of port listed enterprises. Finally, relevant suggestions are given from three aspects: Adjusting production scale, optimizing corporate financing structure, and increasing policy support.

Analysis of the Influencing Factors, Mechanism and Spatial Effect of Chinese Cultural Consumption under the Internet Environment

ZHOU Yanli, RONG Mei, DU Peilin

2023, 32(5):  211-218.  DOI: 10.12005/orms.2023.0171

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In the context of supply-side reform, cultural consumption has become one of the main driving forces for China to expand domestic demand and promote the sound and stable development of the economy with its many hot spots and great flexibility. At the same time, the development of the Internet has facilitated the cultural consumption channels of residents, spawned various new forms of cultural consumption, and had a significant impact on the cultural consumption of residents. However, at present, the gap of cultural consumption of Chinese residents is huge, and its potential is far from being effectively developed and utilized, and there is a large space for cultural consumption. Therefore, it is of great significance to study the influencing factors and mechanism of residents' cultural consumption in the new environment for promoting the optimization, upgrading residents' consumption structure, and promoting the rapid and healthy development of the cultural industry economy. Based on this, this study makes a complete analysis of cultural consumption and its influencing factors from both time and space. First, it breaks through the problem that the existing research only takes the network element as the research background, and analyses its specific impact path on cultural consumption. Secondly, it analyses the individual impact, interactive impact and comprehensive impact mechanism of each factor on cultural consumption, effectively explains the inconsistency of the impact results of each factor on cultural consumption in the current research, and can more profoundly identify the actual action path of each factor. Finally, by analysing the spatial correlation and spill over effects of cultural consumption and its influencing factors, it provides guidance for the coordinated development of cultural consumption among regions.
The research sequence of the article is as follows: Firstly, based on the existing theoretical research and practical development, six factors affecting residents' cultural consumption are proposed, including income, price, investment, education, network, population dependency ratio, and the impact mechanism of each factor on cultural consumption is analysed. Secondly, the article constructs the model of influencing factors of residents' cultural consumption under the Internet environment “Y=F(IN, PD, PI, INV, EDU, INE)”, and puts forward relevant research assumptions. Finally, the model and hypothesis are statistically verified by using MATLAB and STATA software. It includes the analysis of the individual impact of each element on cultural consumption, the impact of the interaction of different elements on cultural consumption, the comprehensive impact mechanism of each element, the spatial correlation and spill over effect of cultural consumption and each element. The article selects the relevant data of cultural consumption in 31 provinces and cities from 2005 to 2016 as the analysis sample. The data of all elements are from China Statistical Yearbook and China Culture and Related Industries Statistical Yearbook. Among them, residents' cultural consumption expenditure is the calculated per capita cultural, educational and entertainment consumption expenditure of residents. The income factor is composed of four types of income weighted by the income of urban and rural residents. The price is expressed by the consumer price index of cultural, educational and recreational residents. The investment includes the whole society's fixed assets investment in culture, education, sports and entertainment and cultural undertaking fees. The educational elements are obtained by the weighted average of the current length of schooling. Network elements are expressed by the proportion of netizens. The population dependency ratio is divided into juvenile dependency ratio and elderly dependency ratio. The spatial weight matrix adopts the combination of the distance matrix of each province and the cultural industry investment matrix.
The research finds that income is the main influencing factor of residents' cultural consumption, and it also affects the relationship between other factors and cultural consumption. The restriction of price on cultural consumption is becoming more and more intense with the increase of income. To effectively alleviate this restriction, a more inclusive price system for cultural products should be formulated. The development of the Internet can reduce the threshold of residents' cultural consumption, and the investment in cultural industry can improve the opportunities of residents' cultural consumption. In the future, we can start with “cultural industry+network”, and use the advantages of the network to improve the added value of cultural industry investment and its impact and guidance on cultural consumption. The consumption of education and its derivatives still account for a large proportion of cultural consumption, which not only restricts the cultural consumption of residents, but also increases the expenditure of cultural consumption at lower levels of education, especially for children. However, at present, the elderly population has more leisure and entertainment time, and the old-age medical security is more perfect, which makes their cultural consumption expenditure increase. Therefore, how to reduce the cost of education and training, and increase the leisure and entertainment time of adults has become the main factor to improve the residents' willingness to consume culture. And the development of the Internet and the integration of culture and high-tech industries can well solve this problem. Cultural consumption in each province has the characteristics of aggregation and obvious spill over effect. Factors such as income, residents' education level, price and Internet development level will have a significant impact on the cultural consumption of residents in neighbouring regions. Therefore, we can consider how to improve residents' cultural consumption willingness from the perspective of regional coordinated development.


Management Science

Research on Governance Mechanism of Opportunistic Behavior in Government Environmental Subsidies

SU Jialu, LI Mingxing, MA Zhiqiang, XIE Haoyang

2023, 32(5):  219-225.  DOI: 10.12005/orms.2023.0172

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Environmental regulation is an effective means to release institutional dividends and promote the harmonious development of economy and environment. Environmental taxation and environmental subsidies are the most common environmental regulatory instruments at this stage, and scholars at home and abroad have used empirical analysis or theoretical modeling to explore the impact of environmental regulation on enterprises' green innovation. Theoretical and empirical experience suggests that government environmental subsidies can provide a certain degree of incentive for firms to carry out green innovation activities. However, some empirical studies have also pointed out that environmental subsidies inhibit the improvement of firms' green innovation ability, and the main reason for this phenomenon is catering to government and managerial opportunism. Accordingly, this paper explores the governance mechanism of firms' opportunistic behavior under environmental subsidies by constructing an evolutionary game model, and dynamically examines the existence and mechanism of firms' reciprocal and opportunistic behavior, to fully utilize the effect of environmental subsidies as an environmental regulation tool and realize the “double dividend” of environmental benefits and economic growth.
This paper analyzes the speculative behavior of enterprises and the government's regulatory choices in the process of environmental subsidies using the evolutionary game approach, and proposes the basic assumptions based on evolutionary game theory. Three conditions can be derived when analyzing the system equilibrium strategy according to the revenue payment matrix, four equilibrium points for conditions 1 and 2, and five equilibrium points for condition 3. With the help of three different conditions, the evolution of the strategy choice of the enterprise and government at different profit and cost sizes can be comprehensively examined. Finally, numerical simulations are conducted using MATLAB to examine the dynamic evolution of enterprise and government strategies over time by assigning different values to enterprise gains and government gains, and to investigate how to suppress enterprise opportunistic behavior and promote the transformation of the government-enterprise relationship to reciprocity, so as to improve the efficiency of government environmental subsidies.
The results show that: a)The enhancement of enterprise and government gains can reduce the occurrence of enterprise opportunistic behaviors. b)Government trust can reduce the occurrence of enterprise opportunistic behaviors, while strengthening regulatory efforts can consolidate government trust, so that the system can reach the final stable equilibrium state. Accordingly, the following countermeasures are suggested: First, build a regional green cooperation platform and promote the formation and development of government-industry-university-research green collaborative innovation network. Secondly, the supervision of local governments should be enhanced while the supervision of enterprises is increased.
The article has enriched the research perspective of the impact of environmental regulation on green innovation of enterprises, but there are also some limitations. First, this study is theoretical and lacks empirical confirmation. In the future, the findings of this study can be empirically tested by collecting and organizing relevant data. Second, the structure and relationship boundaries of the group network are not considered in the construction of the revenue matrix, and the utility model can be optimized based on the complex network perspective in the future.

Influence of Monetary Policy Uncertainty on Business Risk

LI Shengqi, ZHAO Xinyu

2023, 32(5):  226-231.  DOI: 10.12005/orms.2023.0173

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Since the outbreak of the COVID-19, the global economic environment is facing a high degree of uncertainty, and the stable operation of enterprises has been broken. In order to cope with the impact of some anti-globalization behaviors and the “black swan” event of the global epidemic of the novel coronavirus on the economy, various countries and regions have strengthened government intervention and introduced relevant economic policies to stabilize the economic operation. Since the financial tsunami, China has introduced a series of new policies and measures to promote economic growth, such as the “4 trillion yuan” stimulus plan, tax and fee reduction, and optimize the business environment. These measures have become key factors for the stable growth of China's economy, and are also important reasons why China has achieved gratifying development results despite the impact of the novel coronavirus pandemic. However, while frequently intervening and stimulating the economy, the government seems to be increasing macroeconomic uncertainty, especially the uncertainty of monetary policy. For example, affected by the epidemic in 2020, Ren Shuming et al. (2021) pointed out that in order to support enterprises to resume work and production, by the end of April 2020, the central bank had adjusted monetary policy five times. In August, The State Council adjusted the monetary policy twice in a week, and in April 2022, the executive meeting of The State Council adjusted the monetary policy twice in a week, leading to unpredictable monetary policies and uncertainties in business operations. Therefore, the purpose of this paper is to study the impact of monetary policy uncertainty on business risk and clarify the mechanism of monetary policy uncertainty on business risk under the background of rising monetary policy uncertainty and our country being in the critical stage of economic development transition.
The economic behavior of enterprises will be affected by macroeconomic policies, especially the monetary policy has a more direct and obvious effect on the business behavior of enterprises. However, most current studies mainly focus on the impact of economic policy uncertainty on enterprises, or pay attention to how monetary policy uncertainty affects a specific enterprise behavior. Few literatures discuss the impact of monetary policy uncertainty on the overall business situation of enterprises, which is also the theoretical significance of this paper's study on the impact of monetary policy uncertainty on business risk. It has enriched the academic research on monetary policy uncertainty at the micro level, broken through the limitation of risks brought by improper business behaviors of enterprises, and combined the uncertainty of monetary policy with micro enterprises, so as to obtain solutions for the study of business risk from a new perspective. At the same time, China is in the critical period of economic transformation and upgrading. Although the changes in monetary policies and other economic policies are highly subjective and controllable for the government, the information asymmetry in the transmission process will lead to the unpredictability of monetary policies for enterprises. Its frequent changes will not only affect the behaviors of enterprises but also cause the low allocation of resources. Against the above background, the study of the impact of the uncertainty of monetary policy on enterprises in this paper can produce more realistic enlightenment for the adjustment of the real economic policy and how enterprises can better cope with the uncertainty and risk in the business process, which is more conducive to China's economic transformation and upgrading.
This paper selects the financial data of China's A-share listed companies from 2007 to 2020 as research samples to analyze the impact of monetary policy uncertainty on the empirical risks of enterprises, and discusses the role of financing and credit and other moderating effects in the process of monetary policy uncertainty affecting business risks. The influence of monetary policy uncertainty on enterprises of different natures, sizes and industries is further analyzed. In order to maintain the robustness of the empirical results, endogeneity test and robustness test are also conducted in this paper.
It is found that the decrease of monetary policy uncertainty significantly reduces the business risk of enterprises, and this effect gradually increases after the implementation of loose monetary policy. The test of the influence mechanism shows that “financing effect” and “credit effect” are important channels to reduce the operating risks of enterprises. The debt financing and credit allocation of enterprises increase, and the financial situation of enterprises improves, thus alleviating the operating difficulties of enterprises. From the perspective of heterogeneity, monetary policy uncertainty has a stronger effect on the operational risks of private and foreign-funded enterprises, small-scale enterprises and manufacturing enterprises. Finally, in order to ensure the robustness of the research conclusions. In this paper, by replacing the explained variables, changing the sample size and changing the regression method, the robustness test is carried out, and the endogeneity test is carried out by the triple difference model, etc., all of which confirm that the uncertainty of monetary policy will increase the business risk of enterprises. Therefore, this paper enriches the research on the impact of monetary policy uncertainty on microeconomic entities.

Comprehensive Measurement, Network Analysis and Trend Evolution of Cross-market Risk Contagion: Empirical Study Based on Commodity Futures

ZHOU Wei, YANG Sitong

2023, 32(5):  232-239.  DOI: 10.12005/orms.2023.0174

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With the widespread financial market risk contagion or cross-market risk contagion, the multi-market volatility caused by risk contagion tends to be convergent with multiple outbreaks, profound impact, wide diffusion, and long duration. Based on the concern and focus of the market and the industry on this phenomenon, theoretical and empirical studies on the risk contagion phenomenon have become a popular topic in the field of finance. Due to the pervasive and disruptive cross-market risk contagion in financial markets, which may further develop into a national or regional systemic financial risk and financial crisis. Therefore, theoretical, methodological, and empirical studies on the cross-market risk contagion phenomenon are not only of great theoretical value but also of significant practical significance. Considering the correlation and directness of inter-market risk contagion, this paper focuses on China's commodity futures markets, where cross-market risk contagion is more significant and intuitive, and systematically studies the cross-market risk contagion via comprehensive measurement, network analysis, and trend evolution. The possible contributions are as follows: This paper focuses on the cross-market risk contagion phenomenon of commodity futures, including both methodological comprehensive measurement index and contagion network analysis, as well as empirical phenomenon summary and regular overview. The methodology and conclusions help investors, managers, and regulators to understand and grasp the cross-market risk contagion phenomenon and overall characteristics, the magnitude and direction of systemic risk, the transmission paths and impact so that they can formulate reasonable portfolio strategies, production, and operation strategies, and industry market regulation strategies, to reduce the adverse impact of cross-market risk contagion effectively, and provide suggestions for systemic risk management.
This paper's research logic is as follows: Firstly, this paper designs the contagion model from the perspective of methodological synthesis, fusing the multivariate heteroskedasticity model with the Diebold-Yilmaz method to design a new cross-market risk contagion measure index, and proposes a comprehensive cross-market risk contagion measure model based on the trend classification. Secondly, this paper uses five types of network identification methods to expand the analysis of the cross-market risk contagion network. Finally, this paper explores the trend evolution of the cross-market risk contagion phenomenon from the perspectives of the cross-market risk contagion index, contagion capacity, and dynamics of contagion factions. Meanwhile, this paper selects nine commodity futures markets to demonstrate the feasibility and effectiveness of the methodology, theory, and countermeasures.
The study finds that: (1)The cross-market risk contagion in commodity futures markets is interconnecting and time-varying. The paper constructs contagion networks for five categories of trends, and the network line color, thickness, and arrows point to the significance and directionality of the risk contagion. In addition, the upward, downward, and leveling trends of the comprehensive measure index and the dynamic evolutions of the faction category all reflect the time-varying characteristics of cross-market risk contagion from different perspectives. (2)The cross-market risk contagion in commodity futures markets presents market differentiation and impact asymmetry. Among them, the risk contagion and risk being contagion phenomenon are the most significant in metals futures, followed by chemical and agricultural futures. In addition, the risk being contagion of crude oil futures is significantly stronger than its risk contagion, and the risk contagion of copper futures and chemical futures within the downward trend is significantly stronger than the upward trend. (3)The cliques of commodity futures markets can be characterized as overall consistency and individual intersectionality. In the faction category, chemical, metal, and agricultural futures are classified into three different factions. In addition, the risk contagion and risk being contagion are significant for metals futures and chemical futures under different trends, which indicates that they belong to the key nodes and contagion centers of the whole market. (4)The cross-market risks in commodity futures markets are spread through nodes and cliques. The nodes contagion of commodity futures risk cross-market mainly revolves around silver, gold, copper, crude oil, asphalt, and natural rubber futures, which means that the above nodes have greater risk impact and impacted ability within the risk contagion network. At the same time, the results of the contagion faction category show that cross-market risk contagion emerge earlier and is more pronounced within the same faction.