Table of Content

25 November 2025, Volume 34 Issue 11

Previous Issue   

Theory Analysis and Methodology Study

Research on Recycling Strategy of Single/Dual Channel underBackground of Block-chain Technology

WANG Feng, ZHANG Lingrong, XU Hang

2025, 34(11):  1-7.  DOI: 10.12005/orms.2025.0335

Asbtract ( )   PDF (1117KB) ( )  

References | Related Articles | Metrics

Given the deteriorating global climate, China has committed to achieving carbon peaking and neutrality as swiftly as possible. The production sector has become a pivotal area of focus for industrial transformation and development in the context of green and decarbonised efforts. Concurrently, recycling and remanufacturing represent advanced forms of the circular economy and are of critical importance to the advancement of China’s industrial upgrading and transformation. However, enterprises encounter obstacles in these domains, including insufficient recycling channels, challenges in regulating the quality of waste products, and intricate and opaque recycling procedures. Blockchain technology, renowned for its transparency and reliability, provides a comprehensive record of logistics and transaction data, and streamlines transactions, reduces remanufacturing costs and enhances recycling efficiency, which are pivotal factors for the growth of the recycling industry. This paper examines a secondary supply chain comprising a manufacturer and a retailer, with a particular focus on the selection of recycling channels and the development of recycling strategies within a closed-loop supply chain. The manufacturer and retailer jointly invest in a blockchain platform and share the associated technical costs. Within this framework, four two-stage game models are developed to assess whether single-or dual-channel supply chain enterprises should invest in blockchain technology. These models enable the comparison and analysis of recycling price, volume, blockchain technology investment levels, and optimal profits across the four scenarios, thereby exploring the impacts of blockchain technology and various channel recycling modes on the equilibrium decision-making of node enterprises.
   The study indicates that while blockchain technology does not affect wholesale and retail prices of products, it does increase recycling prices, recycling volumes, and overall profits for manufacturers and retailers, irrespective of single-or dual-channel recycling models. The level of blockchain technology investment positively impacts commissioned recycling prices and volumes but is inversely related to indirect recycling prices. Manufacturers benefit more from dual-channel recycling, whereas the opposite is true for retailers. Retailers only profit if the manufacturer’s contribution to blockchain technology input costs exceeds a certain threshold. As the cost coefficient increases, the manufacturer’s profit decreases while the retailer’s profit increases, eventually converging to a fixed value as the cost coefficient approaches infinity. Factors such as the cost coefficient of blockchain technology, the sharing ratio, recycling efficiency, and the unit cost savings from remanufacturing influence the level of blockchain technology inputs, recycling prices, and volumes. Based on these findings, several management implications are proposed. Both manufacturers and retailers should invest in blockchain technology to enhance the recycling of used products, thereby increasing their profits and generating social benefits. Manufacturers should prefer dual-channel recycling, while retailers should opt for single-channel strategies. As market leaders, manufacturers should balance recycling strategy, blockchain technology investment, and profit distribution to motivate retailers’ cooperation and ensure the supply chain’s stable operation.
   In the context of blockchain technology, supply chain enterprises should consider the cost coefficient of blockchain technology input, the cost-sharing ratio, the efficiency coefficient, and the impact of unit cost savings to make informed decisions about blockchain technology investment levels and recycling strategies. Future research could explore corporate recycling strategies that incorporate third-party recycling models within the context of blockchain technology. Additionally, in practice, consumers’ acceptance of recycled products influences single-or dual-channel recycling decisions. Therefore, future research could establish a multi-party game model that considers third-party consumers for further study. The issues surrounding single/dual channel recycling and pricing in the context of blockchain technology are also promising avenues for future research.

Macroeconomic Tail Risk and Financial Market Stability

YAO Shouyu, LU Ling, LI Tong, WANG Chunfeng, FANG Zhenming

2025, 34(11):  8-14.  DOI: 10.12005/orms.2025.0336

Asbtract ( )   PDF (954KB) ( )  

References | Related Articles | Metrics

Chinese economy is currently facing the triple pressure of shrinking demand, supply shocks and weakening expectations, with external challenges such as Sino-US trade frictions and geopolitical conflicts exerting a negative influence. Macroeconomic tail risks refer to the possibility of extreme downturns in the macroeconomy, a topic of widespread concern. The turbulence in the macroeconomic situation has a profound effect on the sentiment, expectations and actions of various financial market participants, making it very easy to cause significant shocks to the financial markets and induce abnormal market fluctuations. Therefore, closely monitoring the impact of macroeconomic tail risks on the stability of financial markets is of great importance for “firmly holding the bottom line against systemic financial risks”. Extant literature underscores the macroeconomic environment as a pivotal external factor affecting firms’ operational sustainability. Corporate management routinely calibrates business strategies and decision-making in response to macroeconomic conditions, including investment and financing decisions, capital structure optimization and innovation initiatives. Consequently, heightened macroeconomic tail risks may induce strategic adjustments by firms, potentially impacting their equity valuations. This study leverages the behavior of micro-firms to examine how macroeconomic tail risks transmit to financial market stability.
   We conduct an empirical analysis of Chinese A-share listed firms from 2007 to 2022 as the research sample, in order to explore how corporate behavior changes during periods of increased macroeconomic risks and its impact on the stability of financial markets. We employ a panel regression model with controls for quarterly and industry fixed effects for the regression analysis, and the standard errors in the model are clustered at the firm level. The financial data of listed firms and individual stock return data are sourced from the CSMAR financial and economic database, while the Goldman Sachs Current Activity Index (CAI) and the Goldman Sachs Financial Conditions Index (FCI) used to calculate macroeconomic tail risks are obtained from the Bloomberg database. The results show that an increase in macroeconomic tail risks is detrimental to the stability of financial markets. The conclusions remain unchanged after conducting a series of robustness checks such as using instrumental variables, adding explanatory variables, changing sample periods and modifying models. Mechanism analysis reveals that when macroeconomic tail risks increase, firms experience rising financing costs, declining investment efficiency, reduced operational capabilities and increased operational risks. This leads to strong motivation for management to conceal negative corporate information, resulting in higher stock price crash risk and threatening financial market stability. Moreover, the exacerbating effect of macroeconomic tail risks on individual stock price crash risk is more pronounced in firms with poorer profitability, concentrated ownership structures and lower analyst attention.
   This study makes two primary contributions to the existing literature. First, it enhances the understanding of how macroeconomic tail risks influence financial market stability by examining their effects on corporate production and operational activities from a micro-level perspective. While previous research predominantly focuses on measuring macroeconomic tail risks and analyzing their sources and consequences from a macro viewpoint, our study addresses the gap in understanding the micro-level mechanisms. Second, it extends the research on the determinants of financial market stability. While existing literature predominantly focuses on the impact of micro-level factors such as managerial characteristics and corporate governance structures on firm decisions and financial market stability, our study emphasizes the intricate interplay between the macroeconomic environment and financial markets. Our findings reveal that heightened macroeconomic tail risks negatively impact corporate operations and increase management’s tendency to conceal adverse information, thereby compromising financial market stability. These insights have significant implications for corporate risk management practices, internal governance mechanisms and policy considerations. Specifically, they highlight the importance of supporting firms during economic downturns to maintain stable business development and promote high-quality economic growth.

Subjective Life Expectancy, Optimal Consumption Decisionsand Demand for Non-life Insurance

CHEN Xuejiao, ZHANG Hongbo, GAO Qingyue

2025, 34(11):  15-21.  DOI: 10.12005/orms.2025.0337

Asbtract ( )   PDF (1373KB) ( )  

References | Related Articles | Metrics

The deepening of population ageing has led to unprecedented perceived pressure on life expectancy and the need for wealth accumulation in households. Consumption decisions that match life expectancy and demand for non-life insurance are an important basis for households to optimise their long-term goals, balancing utility enhancement and risk management. As a result, it is more urgent than ever for residents to make efficient and rational consumer and non-life insurance decisions. Prior to the rational revolution expectations, the life-cycle-durable income hypothesis model was the main theoretical framework for studying the consumption behaviour of the population. For a long time, scholars have been trying to be closer to the reality of the population, gradually introducing term uncertainty and mortality ambiguity into the consumption decision model and non-life insurance demand model, on the basis of which they analyse the optimal consumption strategy and life insurance demand strategy.
   Based on the 2020 China Health and Retirement Longitudinal Study (CHARLS) and the 2020 China Population and Employment Statistical Yearbook, this paper uses the Kohl’s method to portray and measure the subjective life expectancy of China’s residents. The subjective life expectancy of the population is introduced into the consumption and non-life insurance demand models based on the fuzzy aversion to mortality, and the analytical solutions of the robust optimal consumption and non-life insurance demand strategies are derived through the dynamic programming principle, Girsanov’s theorem and the HJB equation. Matlab numerical simulations are applied to examine the extent to which residents’ subjective life expectancy affects residents’ consumption decisions and demand for non-life insurance, as well as the level of change in optimal decisions due to changes in important economic and social parameters. The aim of this paper is to obtain consumption and non-life insurance demand strategies that are more relevant to the reality of the population and provide a more robust wealth preparation for the population to cope with the ageing of the population.
   The analysis leads to the following conclusion: our middle-aged population underestimates subjective life expectancy. The more pessimistic the subjective life expectancy, the higher the level of consumption of the population, which is strongly characterised by “just-in-time” and “consumption distortion”. However, subjective life expectancy does not have a significant impact on residents’ non-life insurance decisions, which is related to the fact that non-life insurance is a short-term insurance product.
   In terms of the optimal consumption of the population, an increase in the risk-free interest rate, an increase in the objective mortality rate of the life table and a deepening of risk aversion significantly increase the level of consumption of the population. In terms of demand for non-life insurance, an increase in the maximum loss ratio ν, the additional premium factor θ, and the average number of insurances taken out by the population κ increase the population’s demand for non-life insurance.
   Important non-life insurance parameters have a “crowding out” effect on residents’ optimal consumption. Increases in the maximum loss ratio ν, the additional premium factor θ, and the average number of insurances taken out by the population κ dampen the population’s demand for non-life insurance. There is a substitution effect as the ratio of consumption and non-life insurance expenditures are inversely proportional to each other.

Study on Optimization and Policy Incentives of Green Credit Pricing Mechanisms

XIA Bing, MA Qi

2025, 34(11):  22-28.  DOI: 10.12005/orms.2025.0338

Asbtract ( )   PDF (1342KB) ( )  

References | Related Articles | Metrics

Green credit is increasingly recognized for its role in supporting low-carbon industry development and facilitating the achievement of carbon neutrality goals. For banks, green credit functions more as a policy-driven financing mechanism than a commercial opportunity. Banks primarily rely on fiscal subsidies, targeted reserve requirements and other policy benefits to achieve profitability while meeting regulatory requirements. To sustain the growth of green credit, enhancing the effectiveness of fiscal subsidies, improving the quality of green credit, and strengthening incentives for enterprise green production are critical. At the core of these efforts is the need to refine the interest rate pricing mechanism for green credit. Currently, green credit interest rate pricing typically follows two approaches: one based on retrospective evaluations of enterprise environmental performance for interest rate incentives, and the other focused on the sustainability of the green credit business, aiming to balance the risks and returns of green projects.
   This paper develops a financing model for manufacturers facing stochastic green demand, considering a traditional manufacturer transitioning to green production by upgrading conventional products to low-carbon emission products and selling them to environmentally-conscious consumers. The success of the green transformation depends on the level of green investment. To alleviate capital constraints in the production process, manufacturers seek green loans from banks. A three-stage process is assumed: in the first stage, manufacturers make early green investments to meet the green financing threshold, while banks disclose their green credit pricing mechanisms; in the second stage, manufacturers obtain green credit and set production levels to maximize profits; in the third stage, manufacturers sell products, generating revenue to repay loan principal and interest. If revenue is insufficient to cover loan payments, it is assumed that the manufacturer will go bankrupt, and the bank will recover all revenue. From the bank’s perspective, two basic pricing models are identified: the emission cap-fixed interest rate model and the risk control-floating interest rate model. First, by analyzing and summarizing the decision-making characteristics and differences between financing parties in these two pricing models, the paper proposes a new emission cap-floating rate pricing model. Then, through a numerical simulation, the effects of incentive policies such as interest rate subsidies and risk compensation on carbon emissions, bank interest rates and corporate profits in different pricing models are explored.
   The results show that in the two basic pricing models: (1)there are optimal values for enterprise output and green investment decisions, with optimal output positively correlated with initial green investment; (2)in the emission cap-fixed interest rate model, banks can constrain corporate carbon reduction by lowering the emission threshold and interest rate, while in the risk control-floating interest rate model, banks adjust the interest rate to balance project returns and principal risk; (3)in the new emission cap-floating rate pricing model, it can simultaneously control corporate carbon emissions and business risks; (4)under risk compensation policies, increasing compensation ratios can effectively manage green credit business risks, reduce carbon emissions, and increase corporate profits.

Optimization of Ride Dialing Problem in Crowdsourcing Model

LI Yanfeng, LIU Xuelin, LIU Mengxin

2025, 34(11):  29-35.  DOI: 10.12005/orms.2025.0339

Asbtract ( )   PDF (1101KB) ( )  

References | Related Articles | Metrics

In recent years, the continuous growth of urban population and economic development have brought great pressure to transportation. Due to the inability of current urban road development to accommodate the increasing number of vehicles, various regions have implemented measures such as license plate and license plate restrictions to control the number of vehicles on the roads. Under these measures, new modes of transportation have emerged as a result, and in order to meet the convenient travel needs of consumers, ride dialing services have emerged. Relying on information technology such as Internet big data, online car hailing service can arrange vehicles for passengers nearby. Compared with fixed public transport, it can flexibly meet the needs of passengers and achieve “point-to-point” transportation. This problem can be described as a ride dialing service company dispatching a group of vehicles to provide shuttle services for passengers with a time window. The vehicles need to pick up passengers at the designated departure point within a specified time window and deliver them to the designated destination, while minimizing operating costs under various constraints. Studying this scheduling problem is of certain theoretical and practical significance to develop the ride dialing industry. It enriches the research content of vehicle routing problems in theory and can provide reference for solving similar problems. In practice, it is expected to provide decision support for ride dialing platforms.
   Therefore, this article first reviews the current research status of scholars at home and abroad on this problem, summarizes the shortcomings of current research, and proposes the optimization problem of ride dialing routes considering crowdsourcing model. This article studies a ride dialing problem composed of self-owned vehicles and crowdsourced vehicles. Taking into account constraints such as time windows, vehicle capacity and the service scope of part-time drivers, the service scope of crowdsourced vehicles is used as a decision variable to minimize the additional cost of empty driving of crowdsourced vehicles to the starting point of passengers while meeting their order requirements. In addition, the total waiting time of all passengers and the compensation cost of part-time drivers are included in the optimization objectives, in order to maximize the satisfaction of passengers and part-time drivers while controlling the total cost. To effectively solve this problem, this paper first uses a greedy insertion heuristic algorithm to construct an initial solution and then designs a variable neighborhood search algorithm based on the characteristics of the problem, and three neighborhood operators are designed in the algorithm. The accuracy of the model and effectiveness of the algorithm are verified through numerical experiments at different case scales.
   The performance comparison analysis between the variable neighborhood search algorithm and CPLEX shows that CPLEX can only solve small-scale cases. As the problem size increases, the variable neighborhood search algorithm designed in this paper exhibits significant advantages, proving the effectiveness of the algorithm proposed in this paper. In the sensitivity analysis section, factors such as crowdsourcing model, crowdsourcing vehicle service radius, number of crowdsourcing vehicles and fixed compensation for crowdsourcing vehicles are analyzed. It is found that using more crowdsourcing vehicles within a certain range and expanding the service radius of crowdsourcing vehicles as much as possible can reduce the total transportation cost to a certain extent.
   The problem model studied in this article is a static deterministic model. From the characteristics of the model, future research can further consider the dynamic needs of passengers, such as an increase in passenger demand, an increase in crowdsourced vehicles, passengers canceling orders, passengers changing destinations, etc. From the perspective of solving algorithms, this article only uses the variable neighborhood search algorithm to solve this problem. Currently, with the development of big data and artificial intelligence, machine learning and other technologies can be used for data prediction and problem solving, making it more realistic.

Distributed Production Scheduling under Cross-enterprise Idle Capacity Sharing

ZHANG Hao, LIAO Yuguang

2025, 34(11):  36-42.  DOI: 10.12005/orms.2025.0340

Asbtract ( )   PDF (1148KB) ( )  

References | Related Articles | Metrics

Shared manufacturing is a new type of production model based on cloud manufacturing technology to share free capacity. This model not only reduces the cost of equipment for user enterprises but also makes up for the lack of capacity of small and medium-sized enterprises due to the surge in orders. However, with the development of the market, manufacturing features such as socialization of manufacturing capacity, interconnection and integration are gradually emerging, which brings many challenges to the production scheduling problem under the shared manufacturing model. For example, scheduling under capacity sharing is mostly collaborative scheduling across enterprises; the production capacity and technology of different manufacturing enterprises are very different; the tasks and needs of customers have become more complex and variable; in addition, the reliability of the products delivered by manufacturing enterprises and the dynamic variability of the production process need to be taken into account. In this environment, it is of great theoretical and practical significance to efficiently integrate the scattered idle production capacity of various manufacturing enterprises, effectively carry out supply and demand matching and production scheduling, and maximize the interests of relevant stakeholders.
   Currently, few studies have focused on the time window of capacity available to enterprises and the effective flow between production and logistics. As for the solution algorithm, the traditional Non-dominated Sorting Genetic Algorithm (NSGA-II) has some limitations, such as the low-quality initial population that leads to slow convergence of the algorithm, the fixed cross-variation probability that reduces the accuracy of the search, which restricts the exploratory ability of the algorithm, and the simple cross-variation operation that cannot realize the evolutionary iteration of the solution, which leads to the algorithm's poor local searching ability.
   In this context, this paper establishes a distributed production scheduling model under cross-enterprise idle capacity sharing by taking into account the limited capacity time window, heterogeneous production performance and inter-enterprise transport collaboration of manufacturing enterprises, aiming at time, cost and quality. Then an improved NSGA-II algorithm is designed to solve the model according to its characteristics. The proposed algorithm uses three-type-population initialization strategies, adaptive cross-variation strategies and variable neighborhood search strategies combined with five operations to strengthen and balance the exploration and development abilities of the algorithm.
   To verify the effectiveness and superiority of the proposed algorithm, we extract three sets of data of different sizes in the example for experiments. The effectiveness experiment of the improved strategy and the algorithm comparison experiment are designed, respectively. Each group of data is independently tested 10 times, and the best results are taken to analyze the performance of the algorithm. Finally, a real case study compares the difference between the proposed algorithm and the traditional scheduling methods of the company in terms of improving capacity utilization.
   The experimental results show that: (1) The proposed improvement strategy is effective, and the proposed algorithm has obvious superiority. (2) The proposed model can be effectively applied to the production scheduling of idle capacity sharing, and the proposed algorithm can make fuller use of the idle resources of the enterprise and improve the capacity utilization rate.
   This study provides some new modeling ideas and algorithmic insights for shared manufacturing. However, in real shared manufacturing environments, where equipment and capacity or order information may often be subject to numerous anomalies, how to build models based on the characteristics of the problem and find efficient solutions in complex and uncertain environments will be an urgent issue to be explored in the future.

Optimal Decision and Coordination of Closed-loop Supply Chain withGreen Innovation Effort in Dynamic Setting

WU Zhihui, ZHANG Yiran

2025, 34(11):  43-50.  DOI: 10.12005/orms.2025.0341

Asbtract ( )   PDF (1261KB) ( )  

References | Related Articles | Metrics

The green innovation is an effective means to improve the product energy efficiency level, and the closed-loop supply chain management can reduce the dependence on original resources and effectively save production costs through the recovery and reuse of resources. The combination of green innovation and closed-loop supply chain can effectively reduce the environmental pollution and resource consumption of products in the whole life cycle, which is more conducive to environmental protection and sustainable development. It should be noted that the production of sustainable products and the processing of recycled products are more complex than traditional products, which means that the green innovation has potential positive and negative impacts on decision-making operations of closed-loop supply chain. Therefore, optimizing operation decisions of closed-loop supply chain by distinguishing between the positive and negative aspects of green innovation is key to pursuing sustainability.
   In this paper, a two-level closed-loop supply chain consisting of a manufacturer and a retailer is taken as the research object. By considering the positive and negative effects from product energy efficiency level on product demand and recycling quantity, a differential game model is constructed to study the dynamic decision-making and coordination of closed-loop supply chain. Firstly, the dynamic evolution process of product energy efficiency level is described by differential equation, and then a differential game model is constructed under centralized and decentralized decision modes. Secondly, the optimal green innovation investment levels, promotional investment levels, product energy efficiency levels, recycling quantities and profits under two decision modes are given by applying the optimal control theory, and the comparative analyses are carried out. Thirdly, the bilateral cost sharing contract is proposed to coordinate the supply chain. Finally, the influences of key parameters on green innovation investment levels, product energy efficiency levels and channel profits are discussed by numerical examples, and the coordination effect of bilateral cost sharing contract is tested.
   The main conclusions obtained in this paper can be summarized as follows: The higher impact from product energy efficiency level on demand can effectively encourage manufacturers to invest more in green innovation, which leads to higher product energy efficiency level. In this case, although the return in the reverse channel will be adversely affected, the overall channel profit can still be increased. The increasing negative impact from the product energy efficiency level on the recycling quantity will enhance the green innovation investment of the product. When cost savings of remanufacturing increase, the manufacturers need to reduce the investment in green innovation to ensure better overall performance of the supply chain. Although the bilateral cost-sharing contract can obtain the same decision values as the centralized mode by restricting the behavior of supply chain members, it may not improve the benefits of the two channel members at the same time.
   The management implications of this paper are as follows: Firstly, in order to promote the high-quality development of closed-loop supply chain, both manufacturer and retailer should actively cooperate. Secondly, in the face of the adverse impact of the product energy efficiency level on the recycling quantity, enterprises should pay attention to improving the impact of the product energy efficiency level on the product market demand, otherwise there may be profit loss. Thirdly, it is not desirable to blindly carry out green innovation by increasing the innovation cost. When the impact of the product energy efficiency level on the recycling quantity becomes smaller or the unit cost savings of remanufacturing become larger, the manufacturer should appropriately reduce the green innovation investment to ensure the maximum benefit and the sustainability of green innovation. Finally, in practice, the bilateral cost-sharing contracts with transfer payments are more practical than traditional bilateral cost-sharing contracts.


Cooperative Decision-making of Customer Allocation amongQueuing Systems with Outsourcing Market

GAO Lijun, LI Jun

2025, 34(11):  51-57.  DOI: 10.12005/orms.2025.0342

Asbtract ( )   PDF (1018KB) ( )  

References | Related Articles | Metrics

When faced with the randomness of demand and the limitations of their own service capacity, queuing service systems often take the form of cooperation to reduce operating costs and improve service efficiency. For example, in the medical service system, China is actively promoting cooperation between comprehensive hospitals and community health service centers to form a medical consortium. Patients in the medical consortium can be referred to each other among the cooperating hospitals, thereby reducing congestion in both comprehensive hospitals and hospitals with lower service efficiency. This service cooperation is a cooperation of service providers through the redistribution of customers. Meanwhile, the existence of private hospitals and third-party healthcare providers amounts to an outsourcing market. Motivated by the above healthcare system service cooperation, this paper investigates the problem of cooperation in queuing systems based on customer allocation strategies in the existence of an outsourcing market. In addition, successful cooperation requires a well-developed compensation and incentive mechanism that fairly distributes the total cost of cooperation to the participants, so as to maintain the stability of cooperation and achieve the cooperation goals.
   In this paper, the research objectives are twofold. One is to construct the service collaboration problem in healthcare system as an M/G/1 queuing system cooperation problem based on customer allocation, provide a general modelling framework for cooperative queuing systems, and obtain an optimal customer allocation strategy that effectively facilitates the sharing of service capabilities among service providers. The other is to propose a cost allocation scheme that can maintain the stability of cooperation by using cooperative game theory. The cost allocation scheme needs to be not only intuitive and easy to calculate but also easy to be understood and evaluated by the stakeholders.
   First, considering the delay cost and outsourcing cost of the system, an optimization model for cooperative decision making of M/G/1 queuing system based on customer allocation when an outsourcing market exists is developed with the objective of minimizing the total cost, and the properties of the optimal customer allocation solution are obtained by the KKT condition. Second, with the cooperative game theory, the cost allocation problem of service cooperation in queuing system is constructed as a corresponding cooperative game model. It is proved that the game satisfies subadditivity and a core cost allocation scheme based on the market competitive equilibrium price is proposed. Under this cost allocation scheme, each subsystem will not cost more if it participates in the cooperation than if it is operated independently, and the cost borne by the members of the sub-coalition under the grand alliance is not greater than the total cost of the sub-coalition, so that no member has an incentive to leave the grand coalition and act alone or form a minor coalition, so the grand coalition is stable. This scheme is easy to calculate and has good applicability and operability. Finally, the numerical results with numerical simulation show that when an outsourcing market exists, cooperation based on customer allocation strategy can effectively reduce the total system cost, regardless of whether customers are outsourced or not. With respect to service intensity, collaboration becomes more favorable as the number of participants increases. Coalition decision makers should actively seek cooperation with the outsourcing market when faced with high service intensity and high variability in service times, which can result in more cost savings.
   On the one hand, this paper extends the queuing cooperation model and enriches the theoretical study of the queuing cooperation game, and on the other hand, it provides scientific theoretical guidance for real-life decision makers in queuing service system cooperation. In this paper, we assume the outsourcing market as an additional queuing system with no capacity constraints, and do not consider the service capacity constraints and delay cost of the outsourcing market, so our next research can take them into account. In addition, there is competition between the outsourcing market and the queuing service system, and how the competition affects the cooperation mechanism and the outsourcing pricing strategy is an interesting research direction for the future.

Research on Vulnerability of Industrial Chain Based on Complex Network

CHU Yanfeng, YUE Zixuan

2025, 34(11):  58-64.  DOI: 10.12005/orms.2025.0343

Asbtract ( )   PDF (1146KB) ( )  

References | Related Articles | Metrics

In the globalized economic landscape, the stability of the industrial chain and ability of independent control have become key indicators of national competitiveness. In recent years, China has made great strides in the fields of biotechnology, electronic information, artificial intelligence, new energy, high-end equipment manufacturing and other industries, and the complete system of industrial chain modernization is maturing. However, there are still many challenges in this process, such as the lack of core technologies, significant import dependence, unilateral trade protection and fragmentation of industry chain links, etc. A series of events such as the ZTE chip incident in 2018 and the “blacklisting” of China’s aerospace companies by other countries in 2020 exposed China’s weak industrial foundation and core technologies are subject to the “blacklisting” by other countries. This warns us that the security and independent control ability of China’s industrial chain needs to be strengthened urgently to cope with the risks brought about by the new trend of globalization.
   The theory of industrial chain describes the network structure of orderly links and interdependence formed by different industrial fields based on technological and economic links in accordance with a certain logical order and geographical distribution. The industrial chain network has horizontal and vertical multi-dimensional spatial characteristics, in which the vertical product chain consists of the input-output relationship among upstream enterprises, core enterprises and downstream enterprises; the complementary and competitive enterprises within the industrial chain expand the horizontal dimension of the industrial chain network, so that the industrial chain is expanded from a single vertical chain form to a vertical and horizontal network form.
   This paper firstly constructs a network model that can reflect the characteristics of the industrial chain. When constructing the industry chain network model, it is necessary to consider the unique characteristics of the industry chain relative to other networks. The value flow, export trade, knowledge spillover, technology flow and behavioral perception derived from the process of industrial development all lead to the flow of capital, products, knowledge, technology and other factors among entities within the industry chain, which reflects the characteristics of the industry chain network that are different from those of the supply chain network. Therefore, this paper firstly adopts the value engineering theory to transform the entity function of the industry chain into the business relationships between entities, which carry multiple factor flows within the industry chain. Then, in order to solve the problem of quantifying the intensity of factor flows, this paper introduces the urban network analysis method to quantify the intensity of different types of factor flows into the intensity of the association of entities at different levels, such as capital, technology, products, etc., and realizes the industry chain network model that can reflect the characteristics of multiple factor flows.
   In the vulnerability analysis part, this paper constructs an impact probability model, maps the strength of multiple business relationships in the industrial chain into network edge weight indicators, then uses network efficiency indicators to calculate network vulnerability, and further explores the relationship between network topology characteristics and its vulnerability. It is found that: (1)there are a small number of key components within the industrial chain network, the failure of these components will have a significant impact on the function of the whole network, and the excessive concentration of the importance of the entity may bring about systematic risk; (2)the vulnerability value of nodes or edges in the industrial chain network has a significant correlation with the node strength and the centrality of the mediator, and these indexes can serve as a reference basis for the identification of the key vulnerability of the industrial chain; (3)analyzing the vulnerability of the industrial chain entity requires a multidimensional approach. The vulnerability of industrial chain entities requires a multi-dimensional and comprehensive consideration of the different functions of entities, i.e., we need to focus on the vulnerability of entities at the level of different business relationships. The vulnerability of a single entity presents significant heterogeneity under the perspective of different factor flows.

q-Rung Orthopair Uncertain Linguistic Multi-criteria Decision-makingMethod Based on Improved WASPAS

WANG Haolun, FENG Liangqing, ZHANG Faming

2025, 34(11):  65-73.  DOI: 10.12005/orms.2025.0344

Asbtract ( )   PDF (1155KB) ( )  

References | Related Articles | Metrics

Due to the rapid development of economy and society, decision-making problems have become more and more complex. The evaluation of alternatives to different criteria contains more vague and uncertain information. Complex practical decision-making problems contain a lot of uncertain information. Decision-makers can hardly rely on accurate data to depict uncertainty. Linguistic evaluation is favored by decision-makers. q-Rung orthopair uncertain linguistic set is composed of uncertain linguistic part and q-rung orthopair fuzzy part. It is an effective and novel expression tool for uncertain and vague information. WASPAS method is a simple decision-making technique which combines the weighted sum model (WSM) with the weighted product model (WPM) and makes the alternatives rank accurately and the results stable.
   Although this method has been extended and applied in various decision-making environments, such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, rough sets and so on, there are still rooms for extension and improvement. Therefore, there are some issues that need to be solved: (1)The existing WASPAS methods have not been extended in the q-ROUL settings. (2)In many methods, the WSM and WPM often use basic algebraic operation rules, but rarely can flexibly adjust the decision results to the decision scenarios. (3)Most of the existing studies on the WASPAS method ignore the interrelationship between any two criteria, but do not consider the interrelationship among multiple criteria. (4)The score function or expectation function is a de-fuzzification method often used by the WASPAS method, but the decision result may be inaccurate due to the loss of some decision information.
   To solve these problems, a novel q-ROUL multi-criteria decision-making method based on the improved WASPAS is proposed in this paper. The Acezl-Asina (AA) operational laws of q-ROUL numbers are introduced based on the concepts of q-ROUL set and AA t-norm and s-norm. The q-ROUL Acezl-Asina Hamy mean (q-ROULAAHM) and q-ROUL Acezl-Asina dual Hamy mean (q-ROULAADHM) operators and their weighted forms (i.e., q-ROULAAWHM, q-ROULAAWDHM) are developed. Then, the maximum deviation model is constructed on the q-ROUL Hamming distance measure to determine the weight vector of criteria. Thirdly, the above aggregation operators and Hamming distance are employed to improve the WASPAS method, so that the decision-making process of this method is more flexible, this method can capture the interrelationship among multiple criteria and the information of decision results is complete and accurate. Finally, the effectiveness and rationality of the improved WASPAS method are verified by a numerical example.

Research on Legal Provisions Knowledge RecommendationAlgorithm Based on Similar Case Generation

SI Linsheng, YAN Yanfei, CUI Chunsheng, LIU Jun

2025, 34(11):  74-80.  DOI: 10.12005/orms.2025.0345

Asbtract ( )   PDF (1189KB) ( )  

References | Related Articles | Metrics

With the surge in judicial cases, traditional text matching methods struggle to meet the precise retrieval demands of massive legal documents due to inefficiency, weak generalization capability and insufficient interpretability. The professionalism and complexity of legal texts require models to not only capture semantic information but also incorporate structured domain knowledge. This study proposes a case recommendation algorithm integrating legal knowledge and case characteristics through constructing a legal article knowledge base and multi-dimensional similarity computation framework, aiming to enhance recommendation accuracy and interpretability. This assists judicial professionals in efficiently locating similar cases, improves transparency and consistency of legal reasoning, and provides a technically valuable pathway for judicial intelligence with both theoretical significance and practical value. Thus, effectively integrating case facts into legal provisions to build an interpretable case recommendation model becomes crucial for enhancing judicial efficiency and decision consistency.
   This paper presents a multi-dimensional feature fusion-based precedent recommendation model with the following core framework: (1) Data acquisition and preprocessing: crawling 215 criminal judgment documents on intentional injury from China Judgments Online and extracting factual descriptions as raw data. Text segmentation using THULAC with dual filtering through general and legal-domain-specific stop word lists (e.g., “public security bureau”, “review”) optimizes text representation. (2) Keyword extraction and legal knowledge base construction: KeyBERT algorithm extracts top-10 case keywords, filtered through BERT’s semantic understanding. Transforming criminal law provisions into element-based structures (e.g., decomposing “fraud crime” into elements like “defrauding public/private property” and “large amount”), stored in Elasticsearch as structured knowledge. (3) Semantic matching and similarity computation: XS-BERT (legally optimized pre-trained model) generates semantic vectors for keywords and legal elements. A weighted similarity function integrates three dimensions: charge overlap (Jaccard index), legal knowledge similarity (vector inner product) and sentence difference (normalized distance). (4) Recommendation and validation: using DCG as core metric, comparative experiments with TF-IDF and Word2Vec baseline models demonstrate superior retrieval accuracy and interpretability.
   The experimental results show significant advantages in DCG@5, DCG@10, and DCG@20 metrics over traditional methods. By integrating legal knowledge bases into deep learning, this model effectively addresses semantic gaps and logical inconsistencies in conventional legal text processing. The algorithm not only improves recommendation precision but also enhances credibility through structured legal provision matching, offering an efficient and reliable solution for judicial AI systems. Future work will extend to multi-offense joint recommendation, courtroom debate perspective integration and cross-jurisdictional adaptability optimization. The case recommendations demonstrate high consistency in charges, legal provisions and sentencing patterns with real cases, validating practical feasibility. This approach assists judicial professionals in rapidly locating similar precedents while enhancing decision interpretability, providing a technically referential framework balancing efficiency and precision. Subsequent research could incorporate external knowledge like trial arguments to further optimize multi-dimensional recommendation mechanisms.

Research on Film Ranking Method Based on Online Rating and Text Reviews

LIU Rui, XU Haiyan

2025, 34(11):  81-87.  DOI: 10.12005/orms.2025.0346

Asbtract ( )   PDF (1015KB) ( )  

References | Related Articles | Metrics

As the representative of human wisdom and creativity, film is the product of modern technology and cultural development, which enriches and improves the quality of people’s leisure life. As a rising star in the arts, film has become an indispensable spiritual nourishment in world culture. With the deep integration of the internet and the film industry, a vast amount of diverse online review data has emerged. How to scientifically and accurately mine viewer preferences and market demand information has become a current hot topic of research. This paper proposes a movie ranking method based on big data environment. The evaluation results can help both viewers choose quality movies and producers understand viewer preferences and market trends so that they can make more targeted marketing decisions. In addition, this paper also make contribution to the evaluation of high-quality films, which helps to enhance the influence and competitiveness of the Chinese film internationally and strengthen the national cultural soft power.
   Firstly, this paper establishes a film comprehensive evaluation index system based on text review data. Web crawling techniques are utilized to obtain online review data of films from media websites and preprocess them. We use the Latent Dirichlet Allocation (LDA) theme model to identify the underlying choice preference of the viewer, and the decision-maker can determine the evaluation index system according to the underlying theme and high frequency topic words generated by LDA topic model, and the experience of the decision-maker. Secondly, the paper combines sentiment analysis with triangular fuzzy number to accurately reflect the fuzziness and subjectivity of the text reviews, avoiding the loss of evaluation information. We construct a sentiment analysis model based on sentiment dictionary, calculate the percentages of negative, neutral and positive sentiment tendencies in text reviews and construct triangular fuzzy number to represent the sentiment analysis results, which make the conversion of text data closer to actual values. Finally, we combine text reviews with online rating information to make up for the lack of considering only one of them and construct a film decision model based on obtaining enough information. The comprehensive ranking function is given by integrating the ranking function based on text information and the average online rating results. A new film evaluation framework is constructed by using the Stochastic Multicriteria Acceptability Analysis - Triangular Fuzzy Number (SMAA-TFN) method to calculate the overall acceptability index of each film and achieve the ranking of different films. In addition, the proposed method can provide the decision-maker with the assistant decision information by using center weight vectors to distinguish movie advantages and disadvantages, identifying competitors through analyzing the dominant relationship between the films. The comprehensive method extends the evaluation dimensions and improves the quality of decision-making.
   In this paper, the real online rating and text reviews of five different types films from the Douban website are selected as the experimental data to illustrate the implementation process of the method. Through a comparative analysis, it is found that the results of the proposed method are more accurate than other models, verifying the effectiveness and superiority of the proposed method.
   In theory, constructing a film ranking model that fully combines online rating with text reviews reduces information bias. The use of triangular fuzzy number to represent sentiment analysis results overcomes the ambiguity and uncertainty characteristics of the text. In addition, the SMAA-TFN evaluation framework proposed in this paper does not require the pre-assignment of subjective weight information or the calculation of objective weight information, overcoming the limitations of existing weight determination methods. In practice, this study leverages a new data source (big data) and integrates it into traditional multi-attribute decision-making methods to evaluate different types of films, providing effective management insights for audiences, producers, etc.

Application Research

Identification of Key Industries of Provincial Carbon Emissions in ChinaBased on Inter-layer Association of Multi-layer Network

XU Lipeng, WANG Wenping

2025, 34(11):  88-94.  DOI: 10.12005/orms.2025.0347

Asbtract ( )   PDF (1183KB) ( )  

References | Related Articles | Metrics

It is crucial for China to achieve the dual-carbon goal by precisely identifying key industries of carbon emissions and achieving orderly and coordinated synergistic emission reduction across provinces. Due to regional disparities in resource conditions and the level of economic and social development in China, each province has formulated low-carbon development goals on the basis of its actual situation, which determines the fact that key industries of carbon emissions have provincial differences and specific spatial distribution. The existing research and practice on key industries of carbon emissions are mostly done from a national perspective, lacking comprehensive consideration of economic and social factors in different provinces.
   Based on this, this paper comprehensively considers the flow characteristics of products and services in industries within provinces, as well as the differential characteristics in the level of economic development and social factors among provinces, from two dimensions: intra-provincial and inter-provincial. And it defines the intra-layer interaction relationships and inter-layer association strength in multi-layer networks. Additionally, by constructing a multi-layer network model of provincial carbon emissions in China based on the provincial input-output data of China in 2012, 2015, and 2017, this paper further improves the PageRank algorithm to rank the importance of nodes in the multi-layer network to identify the key industries of carbon emissions with the characteristics of intra-provincial interactions and inter-provincial associations.
    The empirical results indicate that: (1)Overall, China’s top-ranking industries of carbon emission include high-carbon industries such as smelting and pressing of metals, transportation, storage, and post, as well as chemical industry and other industries, which are mainly distributed in provinces such as Hebei, Liaoning, Hunan, Jiangsu, and so forth, indicating that China’s industrial emission reduction is still concentrated in partial heavy industry provinces. Therefore, carbon reduction strategies for these provinces need to focus on improving carbon efficiency. Meanwhile, the generation and supply industries of electricity and heat play a crucial role in promoting inter-provincial industrial linkages due to their distinctions in fundamentals, livelihood, and high technology content. They have gradually evolved into key industries of carbon emissions in most provinces of China, indicating that ignoring the effect of correlation between economic and social factors in different provinces between industries will have an impact on precise control of carbon emissions in China; nevertheless, reducing emissions in this industry requires cross-provincial synergistic linkage, like power transmission from west to east. (2)Locally, the key industries of carbon emissions in China exhibit certain regional disparities on account of different carbon emission intensities. The key industries of carbon emissions in provinces and cities with lower carbon emission intensities include the production and supply of tap water, construction, and the generation and supply industries of electricity and heat, such as Beijing, Shanghai, Guangdong, and Zhejiang. However, the key industries of carbon emissions in provinces and cities with higher carbon emission intensities include the electronic equipment and machinery manufacturing industry, transportation, and the generation and supply industries of electricity and heat, such as Ningxia, Inner Mongolia, Xinjiang, and Shanxi. This shows that we need to boost the linkage effect between adjacent and cross provincial industries in various Chinese provinces and cities as well as improve the horizontal and vertical correlation relationships in key industries of carbon emissions, in order to achieve regional integrated development of industrial ecology. (3)In the spatial and temporal dimensions, China’s carbon-emitting key industries show certain ‘plate’ characteristics, as well as an increasing proportion of carbon-emitting industries in the tertiary and primary industries, indicating that it is crucial to formulate a personalized regional synergistic emission reduction strategy. The results of this research provide theoretical support for precise synergistic emission reduction strategies in various Chinese provinces.

Slot Exchange and Allocation Optimization of Liner Alliance Considering Demand Uncertainty and Empty Container Repositioning

JIN Zhihong, XING Ben, WANG Wenmin

2025, 34(11):  95-101.  DOI: 10.12005/orms.2025.0348

Asbtract ( )   PDF (1297KB) ( )  

References | Related Articles | Metrics

With the rapid development of the world economy, maritime container transport with its large transport volume and low transport cost can effectively reduce the disadvantages of cargo damage and poor goods. It has become the main mode of transport in world trade to provide efficient and reliable transportation for the owners of cargo. In the actual course of route transportation, how to reasonably allocate container slot on the basis of meeting the market demand of each port pair to maximize the profit of the liner company, that is, the optimization of container slot allocation, is the focus of the operational decision of the liner company. In order to take the market share, expand the scope of operation and enhance their core competitiveness, liner companies adopt the strategic cooperation model of liner alliance to achieve a win-win cooperation. In the context of liner alliance, liner companies mainly cooperate in the form of mutual slot rental, slot exchange, slot rental and joint dispatch of ships. In actual transportation, mutual slot rental is widely used because of its strong flexibility and cooperation in the form of mutual rent on different routes. However, in similar routes, the threshold of slot exchange is low, and slot sharing can be realized only by signing a slot exchange agreement and exchanging the same number of slots. In the case of similar routes, the form of slot exchange can reduce rental settlement and is more flexible than mutual slot renting. Therefore, it is of practical significance to allocate container slot in the form of slot exchange under the background of liner alliance.
   In container liner transportation, there are many factors that affect its transportation. Because of the influence of the world economic environment and seasonality, demand is uncertain. How to overcome the influence of demand disturbance on the optimization of shipping slot allocation and make reasonable decisions in the complex environment has always been a difficult problem for liner companies. In addition, with the development of container transportation, the production of empty containers is essential. How to effectively manage empty containers and reduce the cost of transportation has become a difficult problem for liner companies. Therefore, when it comes to the problem of container slot allocation, it is necessary to comprehensively consider the research and analysis of empty containers in the environment of uncertain demand.
   Starting from the two aspects of container slot allocation and the related overview of liner alliance, this thesis introduces the influencing factors and solving methods of container slot allocation in detail, as well as the advantages and disadvantages of liner alliance and cooperation forms, so as to create a good theoretical foundation for the establishment of the model. Based on the theoretical basis, this thesis considers the cooperative transport of laden empty containers under uncertain market demand in the context of liner alliance, adopts the slot exchange strategy, aims to maximize the profit of liner companies, introduces the distributed robust opportunity constraint method, and transforms the demand uncertainty constraint into an equivalent form that can be easily solved. A distributed robust probability-constrained multi-period stochastic programming model for cooperative transport of laden empty containers is constructed. Finally, CPLEX software is used to solve the problem. The applicability of the model is verified by a series of numerical experiments based on the Asian route among China, Japan and South Korea. The results show that the exchange strategy can increase the profit of the liner company, and reasonable empty container transportation can ensure that the number of available empty containers in the storage yard can meet the transportation demand of the liner company. In this paper, only a single strategy of slot exchange is considered. Future research can consider combining multiple strategies for transportation. Combined strategy can realize the internal profit maximization of a liner company or liner alliance. We can also consider the situation of the cancellation of the reservation and the study of the allocation of the container slot under the overbooking strategy, and establish a dynamic optimization model of the allocation of the container slot according to the market demand.

Integrated Scheduling of Production and Electricity in Glass Deep-processingFlow Shop with Photovoltaic Generation and Storage System

CUI Weiwei, XIAO Zhenlei

2025, 34(11):  102-108.  DOI: 10.12005/orms.2025.0349

Asbtract ( )   PDF (1353KB) ( )  

References | Related Articles | Metrics

With the growing global emphasis on environmental protection and sustainable development, the increasing pressure on companies to reduce energy cost and minimize carbon emissions is increasing. In the manufacturing sector, an energy-intensive industry, traditional fossil fuel energy consumption not only leads to high energy cost, but also has a serious impact on the environment. As a result, more and more companies are looking to renewable energy sources, particularly photovoltaic energy, to supply energy for their manufacturing processes. This transition not only helps to reduce long-term energy costs, but also helps companies to gain an edge in environmental regulations and market competition. In this paper, a photovoltaic (PV) glass deep-processing line shop using a photovoltaic storage energy supply system is used as a research object. It aims to explore how production scheduling can be optimized under photovoltaic energy conditions to reduce total cost and improve productivity.
   In this study, a three-stage mixed integer planning mathematical model is constructed by considering the characteristics of the photovoltaic glass deep-processing line. The model includes three stages: edging, coating and tempering. To effectively reduce the penalty cost of delayed delivery and the energy cost under time-sharing tariffs, we design a meta-heuristic algorithmic framework that combines the advantages of deep learning to optimize the production sequencing of glass deep-processing as well as the storage and deployment of photovoltaic power generation. Among them, the Monte Carlo simulation is used to obtain training sample data for the uncertainty of PV power generation. And the fast approximate evaluation of feasible solutions is achieved by fitting regression through feature engineering and convolutional neural network. The accuracies of the runtime and solutions of different algorithms are compared and analyzed by simulating the data of PV power generation and PV glass deep processing. Sensitivity analysis is then used to explore the impact of different PV power generation scenarios on production cost.
   It is shown that the integrated scheduling scheme incorporating photovoltaic (PV) energy storage systems performs superiorly in terms of the accuracy of runtime solutions. Through numerical experiments, it is verified that the designed convolutional neural network can quickly and accurately evaluate the feasible solutions. Comparison with commonly used heuristic algorithms verifies the effectiveness of this paper’s algorithm in terms of runtime and solution accuracy. Meanwhile, the integrated scheduling scheme in this study also shows significant advantages in terms of solution quality compared with the traditional independent running strategy. The study also reveals the trade-offs between the two core objectives under the integrated scheduling framework, and further compares the production costs in different photovoltaic power generation scenarios, which provides guidance at the practical operational level for the promotion and application of renewable energy in glass manufacturing.
   Further research can consider the real-time adjustment of power control strategies, formulate the real-time control problem as a Markov decision process, and use approximate dynamic methods such as reinforcement learning to optimize the solution. In addition, the optimal size allocation of PV panels and storage plants can be explored from a strategic level perspective. The NPV approach is utilized to measure the overall return of a company constructing a photovoltaic storage and energy supply system from the perspective of the full life cycle of a PV project.

Research on Inventory Financing Considering Credit-basedLoan and Order-based Loan Respectively

CHEN Zhen, ZHANG Renqian

2025, 34(11):  109-115.  DOI: 10.12005/orms.2025.0350

Asbtract ( )   PDF (1013KB) ( )  

References | Related Articles | Metrics

This paper delves into how an online retailer, operating within cash constraints, manages multi-period, multi-product inventory and financing decisions, while grappling with stochastic customer demand. It particularly investigates the use of credit-based loans and order-based loans, available on Chinese e-commerce platforms, typically with a term not more than one year. Order-based loans offer a specialized financing avenue designed to expedite the retailer’s cash flow. Upon acceptance of an order, the platform promptly disburses payments to the retailer upon confirmation of delivery. Subsequently, the retailer repays the loan, along with interest, within a predetermined timeframe. Credit-based loans resemble traditional bank loans, where the e-commerce platform provides a predetermined cash sum to the retailer, and the retailer repays the loan with interest over a specified period.
   To address this issue, we employ the scenario tree method to depict demand uncertainty, in which each demand realization value is represented by a node in the scenario tree. Then, we develop Mixed Integer Programming (MIP) models for two financing scenarios and solve them using the Gurobi 9.0.3 solver in Python 3. The scenario trees are constructed using the moment matching method, considering the mean, variance and skewness of the demand distribution. By minimizing the Euclidean distance between specified moments and those derived from the scenario tree, demand realization values along with the corresponding possibilities are obtained.
   However, when the planning horizon is extended, the scenario tree becomes expansive, leading to an exponential increase in the number of constraints and decision variables in the MIP models. To expedite computation, we employ the forward reduction method to scale down the scenario tree. The concept behind forward reduction is as follows: first, select the scenario with the least weighted distance to other scenarios, where the weights represent the probabilities of demand realizations in the scenarios; then, choose the scenario with the second least weighted distance, and continue this process until the required number of scenarios are selected; finally, add the probability of each unselected scenario to its respective nearest scenario.
   To validate the efficacy and efficiency of our modeling and solving approaches, we utilize real data obtained from an online electronics retailer and fit stochastic demand to lognormal distribution based on the demand data for three products: keyboards, mice and headsets. Historical monthly demand is assessed from the frequency of customer comments scraped from the retailer’s online stores in the Tmall platform. The numerical results demonstrate that the scenario tree method exhibits both in-sample and out-of-sample stability in solving the problem. In-sample stability entails that computational results across different scenario trees should not vary significantly, while out-of-sample stability implies that computational results should remain relatively consistent after fixing the first-stage decision obtained from other scenario trees. Moreover, in terms of the managerial insights gleaned from this study, we observe that both financing services can enhance the retailer’s profit and mitigate cash shortage issues. On the other hand, across various combinations of initial cash, overhead costs, payment delay length, loan quantity, interest rates and demand fluctuation pattern, credit-based loans prove to be more effective in addressing cash shortages compared to order-based loans while order-based loans display strengths when the payment delay length is long. Retailers can leverage the stochastic modeling approach presented in this paper, considering their cost and revenue parameters, to select the appropriate financing services and make informed inventory decisions.

An Optimized Model of Strategy Selection for Crossing theDesert When Weather Conditions Are Known

CHEN Xiusu, CHEN Rui

2025, 34(11):  116-121.  DOI: 10.12005/orms.2025.0351

Asbtract ( )   PDF (1281KB) ( )  

References | Related Articles | Metrics

The National Mathematical Modeling Competition for College Students, which began on September 10, 2020, has three questions for the undergraduate group: A, B, and C, which are furnace temperature curve, crossing desert, and credit decision-making for small and medium-sized enterprises. The number of teams participating in the national competition for topic selection is 8722 for question A, 8888 for question B, and 19619 for question C; the distribution of submission teams for question A is 8344, question B is 8398, and question C is 18500; the total number of teams choosing questions A and B is much smaller than that choosing question C. This indicates that in the judgment of the contestants, most of them believe that problems A and B are relatively difficult to solve, and the most important and difficult thing is the establishment of the corresponding mathematical model. Especially for problem B, how to accurately represent the constraints of reality with the expression of the model is a very difficult innovative process in model construction. However, in the authoritative commentary of this competition, only the modeling direction of some constraints was briefly suggested, and the specific expression construction of each realistic constraint model was not given, which is sufficient to illustrate the difficulty of modeling this problem. This article explores the game of crossing the desert, where players use a map and initial funds to purchase a certain amount of water and food (including food and other daily necessities), starting from the starting point and walking in the desert. On their way, different weather conditions may be encountered, and the optimal decision problem is to supplement funds or resources separately in mines and villages, reach the destination within the specified time, and maintain as many funds as possible.
   Take whether the player reaches (or is in) a j region on an i day as the decision variable, and take the initial capital, load limit, resources consumption, and daily movement of the player between adjacent regions in the map as the constraint conditions, to reach the end within the specified time, and maximize the remaining capital at the end of the destination as the goal. A constrained nonlinear integer programming model for optimal strategy selection of players crossing the desert is established. The constraint expression that the player can only reach one area and move between adjacent areas is given before reaching the end point. With symbol function, argmax function and absolute value, a unified expression of water and food resources consumption is constructed under two different choices of staying and walking when players arrive at non-mining areas and three different choices of staying, mining and walking when players arrive at mines. The objective function containing the expression structure of mining income is established. The decision problem of the optimal strategy selection for crossing the desert under the condition of known weather conditions is studied.
   By solving the corresponding model, it is obtained that the optimal strategy for the player in the first level of the desert is to reach the destination on the 23rd day, mine in the mine for 8 days and rest for one day to obtain the maximum remaining capital of 10,430 yuan. The optimal strategy for the player in the second level of the desert is to reach the destination on the 29th day and mine in the mine for 14 days to obtain the maximum remaining capital of 12,345 yuan. And the general ideas and methods for modeling the optimal strategy selection for crossing the desert when the weather conditions are known are given.

Realized Volatility Decomposition and Prediction Based on Level of Intraday Returns

QU Hui, SHANGGUAN Peng

2025, 34(11):  122-128.  DOI: 10.12005/orms.2025.0352

Asbtract ( )   PDF (1075KB) ( )  

References | Related Articles | Metrics

The modelling and forecasting of asset volatility are critical for financial risk management and derivatives pricing. Instead of treating volatility as an unobservable variable, recent works in this area tend to construct the Realized Volatility (RV) estimator proposed by ANDERSEN and BOLLERSLEV (1998) using high-frequency intraday prices and then model the observable RV estimator directly, most of which apply the Heterogeneous Autoregressive (HAR) model proposed by CORSI (2009) and its various extensions.
   To improve the prediction ability of the HAR class models, some researchers further decomposed the realized volatility. Among them, ANDERSEN et al. (2007) decomposed the realized volatility into the continuous component (C) and the jump component (J), constructed the HAR-RV-CJ model which separated the contribution of continuous and jump components and achieved better forecasting performance. PATTON and SHEPPARD (2015) decomposed the realized volatility into the Realized Positive and Negative Semivariances (RS) based on the sign of intraday returns, constructed the HAR-RV-RS model which characterized the intraday leverage effect and achieved better forecasting performance as well.
   Considering that intraday returns of different levels can have different amount of valuable information for future volatility, this study decomposes the realized volatility into the high-return variation, the medium-return variation and the low-return variation based on the level of intraday returns, and constructs the HAR-RV-R model that separates the contribution of these three variations following the HAR-RV-RS model of PATTON and SHEPPARD (2015).
   The empirical experiments using intraday five-minute prices of the Shanghai Stock Exchange index show that the HAR-RV-R model has a better in-sample fit and out-of-sample forecasting performance than the benchmark HAR-RV-RS model, which proves that the realized volatility decomposition based on the level of intraday returns is more effective than the decomposition based on the sign of intraday returns. Furthermore, such a superiority of the proposed HAR-RV-R model is consistent as the prediction horizon increases from one day to one week and then one month, and the superiority of the HAR-RV-R model is statistically significant, as justified by the Diebold-Mariano test. Further examination of the explanatory power of the high-return variation, the medium-return variation and the low-return variation on future volatility shows that the medium-return variation has outstanding explanatory power compared with the high-return variation and the low-return variation.
   In order to compare the explanatory power of the high-return variation, the medium-return variation and the low-return variation of different time horizons, we extend the HAR-RV-R model to the HAR-RV-RHML model, which includes the daily, the weekly and the monthly high-return variations, medium-return variations and low-return variations as regressors. The empirical results show that the HAR-RV-RHML model has a significantly better out-of-sample prediction performance than the HAR-RV-R model for all the three forecast horizons, and the explanatory power of the daily, the weekly and the monthly variations is clearly different, with the monthly variables having the strongest explanatory power.
   Finally, considering that the medium-return variation is essentially a removement of the jump component of the realized volatility, this study compares the explanatory power of the medium-return variation with that of various continuous volatility estimators constructed with major jump identification methods. The empirical evidence points out that the model introducing the medium-return variation has a stronger out-of-sample forecasting performance, which confirms that the realized volatility decomposition based on the level of intraday returns is simple but effective.
   The proposed realized volatility decomposition method and the corresponding empirical results are valuable for practical applications such as financial risk management and derivatives pricing. Further extensions include the appropriate decomposition of the realized covariance matrix so as to improve the prediction ability, which can contribute to the practical application of asset allocation.

Research on Prediction of Transfer and Delisting of NEEQ EnterprisesConsidering Macroeconomic Indicators

LI Jie, DING Shuhan, YANG Fang

2025, 34(11):  129-135.  DOI: 10.12005/orms.2025.0353

Asbtract ( )   PDF (1198KB) ( )  

References | Related Articles | Metrics

The low listing threshold, limited liquidity and relaxed regulatory environment of the National Equities Exchange and Quotations (NEEQ) make investors face the dilemma of quality judgment when choosing enterprises. The existing research on financial crisis early warning of NEEQ enterprises can help investors avoid high-risk enterprises. But it can’t screen out high-quality enterprises with high growth potential. In addition, the existing research often focuses on the internal operating conditions of enterprises, but ignores the impact of macroeconomic factors on enterprise development. Therefore, this study will expand the research perspective from the financial crisis prediction of the NEEQ enterprise to the multi-classification prediction of the transfer, maintenance and delisting of NEEQ, consider the influence of macroeconomic factors on the future development of the NEEQ enterprise, and establish an effective forecasting system of the transfer and delisting of NEEQ enterprises by integrating macroeconomic indicators into enterprise micro-business ones. The purpose of this study is to help investors find high-quality enterprises, avoid investment risks and realize investment returns, so as to promote the rapid development of NEEQ enterprises, realize the goal of “cultivating excellence” of NEEQ and promote the sustainable development of national economy and industry.
   To simultaneously predict the three development states of NEEQ enterprises including the transfer, maintenance and delisting of NEEQ, this study considers the influence of macroeconomic factors on the development of enterprises, selects macroeconomic indicators and constructs the prediction indicator system of NEEQ enterprises’ transfer and delisting with enterprise micro-business indicators. On the basis, a forecasting model is constructed to verify the influence of macroeconomic indicators on the transfer and delisting of NEEQ enterprises. Firstly, according to macroeconomic theory and related research, this study selects macroeconomic indicators such as GDP growth rate, savings rate and consumer confidence index, and integrates them into enterprise micro-business indicators to form a prediction index system of the transfer and delisting of NEEQ enterprises, which provides index system support for the establishment of machine learning classification model. Secondly, the prediction of the transfer and delisting of NEEQ enterprises is transformed into a three-classification problem of machine learning, and the prediction model of the transfer and delisting of NEEQ enterprises is constructed by using OVO decomposition strategy, random forest, GBDT, XGBoost and LightGBM algorithms and Bayesian optimization algorithm. Finally, based on the empirical study of 9334 enterprises that were transferred, kept and delisted from 2020 to 2022, this study analyzes the impact of macroeconomic indicators on the transfer and delisting of the NEEQ enterprises, and provides relevant management suggestions for investors.
   The research results show that: (1)Based on the micro-business indicators of enterprises, the introduction of macroeconomic indicators can significantly improve the prediction effect of the transfer and delisting model of NEEQ enterprises, and the Accuracy, Precision, Recall, F1-Score, Kappa and G-means are increased by 12.15%, 13.18%, 14.32%, 17.22%, 32.16% and 17.89%, respectively. Among the four prediction models constructed in this paper, XGBoost has the best prediction performance, and its Accuracy, Precision, Recall, F1-Score, Kappa and G-means reach 93.39%, 86.22%, 85.28%, 85.62%, 84.34% and 85.40%, respectively.(2)According to the ranking of importance, four of the top five indicators belong to macroeconomic indicators, which are ranked first, second, fourth and fifth, respectively, which verifies the importance of macroeconomic indicators. (3)The most important macroeconomic indicators are the PMI, entrepreneur confidence index, GDP growth rate and money multiplier M2. Therefore, when analyzing the development prospects of enterprises, investors should not only pay attention to the micro-business conditions such as registered capital, scale and profitability of enterprises, but also analyze the current macroeconomic environment, such as PMI, entrepreneur confidence index, GDP growth rate and money multiplier M2, so as to better understand the external environment and internal conditions faced by enterprises and make better investment choices.
   This study extends the research perspective from the financial crisis prediction of NEEQ enterprises to the multi-classification prediction of the transfer, maintenance and delisting of NEEQ enterprises, and brings the macroeconomic indicators into the index system, which not only provides a new research perspective for related research, but also provides investors with an important tool to identify potential risks and discover investment opportunities.

A Predictive Model for Security and Stability of Lithium-ion BatteryIndustry Chain Based on Price Modal Combinations

LI Jianfei, PENG Han, DONG Yutong

2025, 34(11):  136-142.  DOI: 10.12005/orms.2025.0354

Asbtract ( )   PDF (1432KB) ( )  

References | Related Articles | Metrics

In recent years, the global lithium-ion battery market has frequently encountered difficulties in supply and demand imbalances, leading to drastic fluctuations in the prices of key products within the industrial chain, having significant impacts on the security and stability of the lithium-ion battery industrial chain. Major new energy consuming and manufacturing countries, represented by the United States and China, have introduced various measures aimed at safeguarding the security and stability of the lithium-ion battery industrial chain and supply chain. Price, as the core signal in a market economy, plays a crucial role in the smooth transmission within the industrial chain, directly affecting its security. Therefore, from a macro perspective of the industrial chain, focusing on the degree of smooth transmission of price fluctuations across multiple nodes and delving into the intrinsic relationship between price fluctuations and the security and stability of the industrial chain, this research holds significant practical implications and provides valuable reference for promoting efficient coordination and sustainable development of the lithium-ion battery industrial chain.
   This paper views the lithium-ion battery industrial chain as a typical complex adaptive system with dissipative structures, dividing its dissipative structures into two subsystems: internal dissipation, i.e., the security and stability state of the industrial chain, and external dissipation, i.e., the degree of “synchronous resonance” in prices across nodes in the lithium-ion battery industrial chain. Based on the entropy flow model, the degree of synchronous resonance of the industrial chain with multiple prices is regarded as the external entropy flow, the safety and stability state as the internal entropy flow, and the collision results of the two entropy flows as the evolution results of the lithium-ion battery industrial chain system. Specifically, this paper first employs the fast ensemble empirical mode decomposition (FEEMD) algorithm and the nonlinear autoregressive (NAR) neural network to build a combined model, accurately predicting the market prices of three key nodes in the lithium-ion battery industrial chain: spodumene, lithium iron phosphate and lithium iron phosphate power batteries. Subsequently, the fluctuation states of the prices of these three nodes are combined into modal signals, and based on the Hidden Markov Model (HMM), conducting an in-depth assessment of the hidden state of safety and stability by observing the synchronous price resonance across multiple nodes in the industrial chain. Finally, this paper uses historical data from the Shanghai Metals Market in China as samples to verify the effectiveness and accuracy of the model.
   Through the prediction results of the FEEMD-NAR model, this paper finds that the price trends of spodumene, lithium iron phosphate and lithium iron phosphate power batteries all exhibit a trend of a decline first and then a rise, and as the supply and demand in downstream markets gradually balance, the market prices of midstream and upstream nodes will gradually return to a reasonable range. Furthermore, through modeling and solving using the HMM model, this paper reveals the following important findings: (1) Over the next period, with the prices of spodumene and lithium iron phosphate power batteries steadily rising, anode material lithium iron phosphate prices are more volatile and staggered fluctuations, leading to a reduction in the degree of synchronous resonance across the entire industrial chain and a decline in security and stability.(2) The degree of security and stability of the lithium-ion battery industrial chain is closely related to the smooth transmission of prices. When “synchronous resonance” occurs across the industrial chain, the level of security and stability will be high; whereas, when “asynchronous misalignment” occurs in the price mode combination, the level of security and stability of the industrial chain will be generally low.

Steel Surface Defect Location Algorithm Based on Multi-scale Feature Fusion

CHENG Cong, WU Suyang, LYU Shanshan, LI Anran

2025, 34(11):  143-150.  DOI: 10.12005/orms.2025.0355

Asbtract ( )   PDF (1453KB) ( )  

References | Related Articles | Metrics

In the field of steel production, surface defects pose a common challenge that can significantly degrade the quality of the final products. Surface defects in steel exhibit high background similarity and often involve defects of small size. Manual inspection and traditional machine learning methods for defect detection confront issues of insufficient accuracy and low efficiency due to these characteristics. Therefore, innovative techniques are needed to ensure accurate detection. The work of this study lies in the development of an advanced pixel-level detection algorithm called Multi-scale Feature Fusion Network for Steel Surface Defect Localization (MFF-Net) for steel surface defect detection. This algorithm is capable of identifying steel surface defects and accurately localizing these defects, contributing to the enhancement of quality control in the steel industry.
   Aiming at the problem that the classification accuracy still needs to be improved in steel surface defect detection and the localization of small size defects is not good, the proposed MFF-Net algorithm relies on the U-Net architecture, proving effective in small size target image segmentation tasks. By incorporating an attention mechanism, the algorithm enhances the network’s capacity to capture crucial features related to steel surface defects, thereby improving overall performance. To overcome the challenges posed by high background similarity and small size defects, a receptive field enhancement module is integrated into the network. This module facilitates the fusion of multi-scale defect features, contributing to improved accuracy in identifying subtle defects. A joint loss function is utilized during training to continuously adjust network parameters, cleverly addressing the issue of imbalanced defect categories and ensuring the robustness and balance of defect feature learning. Additionally, the algorithm employs data augmentation techniques to expand the dataset, actively enhancing the network’s generalization and overall robustness. The integrated approach implemented in the MFF-Net algorithm solidifies its effectiveness in recognizing and accurately locating steel surface defects in various scenarios. In summary, the MFF-Net algorithm addresses the challenges of category imbalance and insufficient data by incorporating attention mechanisms, receptive field enhancement modules, improved loss functions and data augmentation, thereby enhancing the accuracy of classification and localization of small-scale steel surface defects.
   The experimental results reveal that the proposed algorithm achieves an mIoU (mean Intersection over Union) of 84.53% on the NEU-Seg dataset, improving by 1.29% compared to existing state-of-the-art models. The algorithm demonstrates a high level of accuracy in identifying defect types, positions and shapes, meeting the requirements for steel surface defect detection in real-world scenarios.

MDD-based Reliability Analysis of Low-energy WSN Systems

JIA Xue, YANG Zimin, MO Yuchang

2025, 34(11):  151-157.  DOI: 10.12005/orms.2025.0356

Asbtract ( )   PDF (1356KB) ( )  

References | Related Articles | Metrics

A Wireless Sensor Network (WSN) system consists of sensor nodes that can sense and transmit physical information of environments or objects being monitored. Infrastructure monitoring is an important class of WSN applications, where a WSN is used to continuously monitor and assess the health status of an infrastructure structure with only little human activity. An infrastructure monitoring WSN enables early detection of problems, further activating the precautionary mechanism to ensure public safety and environment protection. For example, a damaged segment in a railroad track could lead to serious human injures and property loss; the early detection of the damaged segment can prevent major accidents. Similarly, an early detection of leaks of hazardous material such as crude oil and natural gases can prevent accidents, such as pollution and the eruption of fires.
   Due to the limited resources of sensor nodes, WSN systems may face many challenges, such as energy consumption, communication quality and data processing efficiency. Data fusion provides an important technology to address these problems with great potential in achieving efficient information gathering and analysis, and energy utilization and communication reliability have been improved significantly. Reliability analysis, as an essential indicator of the system’s stability, plays a pivotal role in reducing the occurrence of failures, ensuring the system’s consistent operation, enhancing the system’s performance and reducing maintenance costs. Consequently, research on the reliability of low-energy WSN systems has gradually become a hot topic.
   Modern WSN systems often use a large number of sensor nodes to perform a set of information gathering and analysis computations. The sensor nodes are often heterogeneous due to factors such as different suppliers, model types and operating environments. In addition, these sensor nodes typically exhibit more than two performance levels or states corresponding to different computing powers and demands.
   Reliability modeling and analysis of large-scale WSN systems is difficult. State-space-based methods like Markov or semi-Markov chains are potentially applicable. However, they suffer from the state-space explosion problem when analyzing medium or large-scale systems, and are typically limited to integrable time-to-failure distributions. On the other hand, discrete event simulations can be used to handle arbitrary types of distributions, but they generally require a lot of computational time and can only offer approximate results.
   Recently, an efficient algorithm based on the Multi-valued Decision Diagram (MDD) has been proposed to analyze multi-state systems. MDDs are efficient graph-based data structures for symbolic representation and manipulation of multi-valued logical functions. Based on Shannon’s decomposition theorem, MDDs can represent multi-valued logical functions as a rooted and Directed Acyclic Graph (DAG) in the form that is both canonical and compact through two reduction rules, “merging isomorphic sub-trees” and “deletion of useless nodes”. MDDs have provided an efficient method for reliability analysis of diverse systems, such as multi-state systems, phased-mission systems and large-scale networks.
   In this work, we make new extensions of the MDD model for analyzing low-energy WSN systems with heterogeneous and multi-state components. Firstly, the system and reliability problem of low-energy WSN based on PEGASIS protocol are defined. Then a top-down approach to generate a MDD model based on data fusion is proposed, which uses the several truncation and merging operations to avoid the generation of a large number of unnecessary MDD nodes, resulting in a compact MDD model. Finally, the system reliability evaluation is carried out based on the constructed MDD model. Once the MDD model is constructed, the reliability of the system can be analyzed and evaluated based on different failure time distributions.
   The constructed MDD model can be used to efficiently calculate the reliability of a low-energy WSN and the proposed method is illustrated through a specific example, and the results show that the proposed MDD method can solve the reliability evaluation of low-energy wireless sensor network systems with different configurations and failure parameters of sensor nodes. Moreover, compared to traditional reliability evaluation methods, the proposed method significantly improves the efficiency of reliability calculation. The MDD method can effectively alleviate the combinatorial explosion problem of enumeration methods and perform efficient reliability analysis of low-power WSN systems.
   The reliability analysis is the foundation of low-energy WSN system optimization. The results of reliability analysis can provide reliable data support and result analysis for actual industrial projects. The results of reliability analysis can be used by the system designer to obtain the optimal design of the network topology and node deployment strategy to meet some of his needs in industry and make cost-effective and optimal decisions on the number of sensor nodes configured in the system and the resources allocation of the sensor nodes themselves. Consequently, our future work focuses on related optimization problems, such as the optimization of energy management and chain structure in sensor nodes, with the objective of further enhancing the availability and survivability of low-energy wireless sensor network systems.

Model and Algorithm for Scheduling Home Care NursingStaff Considering Seniors’ Psychological Ordeal

LIU Yong, HUANG Yufei

2025, 34(11):  158-165.  DOI: 10.12005/orms.2025.0357

Asbtract ( )   PDF (1072KB) ( )  

References | Related Articles | Metrics

Currently, the elderly care services in China exhibit a “9073” structural characteristic, meaning 90% of elderly individuals choose home-based care, 7% opt for community-based care,and the remaining 3% select institutional care. In the home-based care service system, the primary reliance is on caregivers who provide in-home services. Existing research often explores caregiver scheduling from the perspective of optimizing service center operational costs. The mental health of the elderly is a crucial factor affecting their quality of life, and the timeliness of services has a significant impact on their mental health status. To address the optimization of elderly care worker scheduling, this study introduces psychological stress fuzzy time window constraints, comprehensively considering the differentiated needs of the elderly and the skill level match of caregivers, and constructs a bi-objective programming model to minimize both the psychological burden costs for elders and the operational costs for service centers.
   In response to the NP-hard characteristics of the new model, this paper designs a two-part encoding method based on the NSGA-II algorithm. The first and second parts represent service sequence and service level, respectively. Based on the sorting results of non-dominant relationships and crowding distance, and combined with binary tournament selection of individuals, a selection operation is designed. Aiming at the characteristics of the two-part encoding, crossover and mutation operators are designed to achieve the update operation of algorithm individuals. Meanwhile, an adaptive local search strategy is developed for the elite individuals, incorporating dynamic adjustment of local search probability to fully tap into their potential and seek higher quality solutions, further enhancing the algorithm’s local optimization capability. Finally, to retain higher quality solutions, a new solution acceptance criterion is designed to determine whether to accept a new solution based on three conditions.
   Taking Wujin district in Changzhou, Jiangsu province as an example, and utilizing the standard VRPTW dataset proposed by Solomon, case instances of three different scales are generated. Initially, the characteristics of the problem are analyzed using a small-scale case to verify the feasibility of the new model. The study examines the impact of the maximum psychological distress threshold on the psychological distress of the elderly using a small-scale case as an example. The results indicate that as the maximum psychological distress threshold decreases, the total psychological distress cost for the elderly declines, while total operational costs increase. When this threshold decreases, the time window for elderly individuals to receive services will shrink, and caregivers must arrive within this window to avoid substantial psychological distress costs. Finally, in small, medium, and large-scale cases, the performance comparison analysis shows that the algorithm presented in this paper exhibits better optimization performance than MOPSO and NSGA-II. The new algorithm demonstrates strong overall optimization capabilities across different scales, effectively balancing the psychological burden on the elderly and operational costs, achieving dual optimization of these two objectives. The home care personnel scheduling model and algorithm developed in this study, which consider the psychological burden on the elderly, provide a new and competitive solution for home-based elderly care research. Considering the scheduling of home care workers under emergency situations will be a direction for future research.

Scheduling Optimization of Family Doctors for PatientOptional Service Modes and Time Windows

ZHOU Yufeng, ZHAO Yimeng

2025, 34(11):  166-172.  DOI: 10.12005/orms.2025.0358

Asbtract ( )   PDF (1254KB) ( )  

References | Related Articles | Metrics

Based on the era characteristics of digital healthcare service development and the personalized needs of patients, this study proposes a home doctor scheduling optimization problem that incorporates selectable service modes and time windows for patients. In this problem, patients can choose from three service modes: home visits, outpatient services and online consultations. The home healthcare team assigns doctors to patients based on their specific circumstances. Each doctor is capable of providing services to any type of patient but can only offer one type of service per day. During the daily schedule, doctors must provide services within the time windows set by the healthcare center. Doctors differ in terms of skill level and ability, and patients have individualized skill requirements. To ensure service quality, the doctors’ skill levels must meet the patients’ skill demands.
   For home visit services, the scheduling problem is a variant of the Vehicle Routing Problem (VRP). Doctors depart from the healthcare center, serve patients, and then return to the center after completing all visits. For online consultation services, the scheduling problem is also a VRP but includes a virtual start and end point. Doctors begin their services from the virtual start point and return to the virtual end point after finishing all consultations. In outpatient services, patients visit the healthcare center to receive care. This study uses queuing theory to quantify the outpatient services, where the set of doctors and their respective patients form independent queuing systems.
   To achieve a comprehensive optimization of the three service modes, while accounting for the distinct characteristics of each mode, a unified model is constructed. The model aims to optimize the following key objectives: the routing cost and waiting time associated with home visits, the waiting time generated during online consultations, the waiting time for patients in outpatient services and patient preference satisfaction. Patient preference satisfaction is represented by the sum of the skill gap between doctors and patients and the familiarity between them. The problem to be addressed involves determining the service mode for each doctor as well as their visit routes to patients. The objective is to minimize the weighted total cost, and the problem is formally described using a Mixed-Integer Nonlinear Programming (MINLP) model.
   Given the characteristics of the model, an Improved Adaptive General Variable Neighborhood Search (IAGVNS) algorithm is designed to solve the problem. The algorithm employs a list-based method for individual encoding. The first part of the list represents the doctor identification number, the second part denotes the service type assigned to the doctor, and the third part specifies the set of patients served by the doctor. The improvement strategies for the algorithm include: using the Forward Start Intervals Algorithm to handle multiple time windows; designing an initial solution construction heuristic based on the insertion algorithm; developing five local search operators, including four intra-service mode operators and one inter-service mode operator; applying a multi-neighborhood transformation strategy for local search; and improving the local search process using the Metropolis criterion from the simulated annealing algorithm. These improvements enhance the algorithm's adaptability across different solution spaces, effectively balancing the exploration and exploitation processes of the algorithm.
   The performance of the IAGVNS algorithm is tested using instances of varying scales and compared with the traditional General Variable Neighborhood Search (GVNS) algorithm and Tabu Search algorithm. The algorithm’s performance is evaluated using optimal values, worst-case values, mean values and standard deviations as criteria. The numerical experiments show that IAGVNS outperforms in most instances, particularly in terms of standard deviation, where it demonstrates superior performance over GVNS. The sensitivity analysis results indicate: (1)The weighting parameters have varying degrees of influence on the overall scheduling scheme and mutually constrain one another. Decision-makers can adjust parameter values according to decision preferences or actual conditions. (2)The matching between doctors and patients also requires a balanced consideration.

Research on Evolutionary Mechanism of Telemedicine TechnologyEmpowering Patients with First-time Consultation in Grassroots Hospitals

DU Tao, WANG Xiaohu, LI Jinyu, BAI Mangmang

2025, 34(11):  173-179.  DOI: 10.12005/orms.2025.0359

Asbtract ( )   PDF (1272KB) ( )  

References | Related Articles | Metrics

With the continuous promotion of China’s hierarchical diagnosis and treatment system, China is faced with the realistic dilemma of unbalanced medical order, weak grass-roots medical service capacity, and the sinking of high-quality medical resources. Realizing the first diagnosis of patients in primary hospitals has become the basis and premise to solve the above problems, and promoting the construction of telemedicine in the medical association is an effective solution to effectively promoting the first diagnosis of patients in primary hospitals. However, in practice, there are two bottlenecks: the lack of both patient acceptance and incentives in grass-roots institutions. It is urgent to reveal the strategy interaction behavior and dynamic evolution mechanism between doctors and patients in the process of technology adoption, which not only helps to expand the theoretical analysis framework of digital medical adoption behavior, but also provides an empirical basis for the government to design incentive policies and optimize the resources sinking mechanism, which is of great significance in promoting the construction of hierarchical diagnosis and treatment pattern. From the perspective of evolutionary game, this study analyzes the stable conditions of telemedicine technology promotion through a dynamic game model, and provides theoretical support for optimizing resources allocation and policy design.
   Based on the hypothesis of bounded rationality, this study constructs an evolutionary game model between patient groups and primary medical institutions. The strategy space of patient groups is {use, not use}; the strategy space of primary medical institutions is {use, not use}. The model includes multidimensional parameters, including medical cost, sinking utility of medical resources, reimbursement ratio of medical insurance, reputation loss and cure rate of primary medical institutions, and describes the payment structure of strategic interaction between the two sides by establishing an income matrix. The evolution path of group strategy proportion is described by copying dynamic equation, and the evolution stability strategy of dynamic equation set is determined by using the local stability of Jacobian matrix system, and the parameter sensitivity is verified by a numerical simulation. In order to ensure that the parameter values are not only in line with the theoretical assumptions, but also close to the real scene, the parameter settings are comprehensively derived from the provincial health statistics bulletin and related literature, so as to ensure the reliability and comparability of the empirical basis. Through a numerical simulation, the influence of different parameter changes on the evolution path and stable state of the system is dynamically displayed.
   It is found that the system only converges to two stable points: {not used, not used} or {used, used}, and its convergence direction depends on the parameter structure and its critical level. Specifically, reputation loss has a significant threshold effect, which can effectively promote the technology adoption behavior of medical institutions; the sinking utility ratio of medical resources needs to be at a high level to encourage patients to choose remote services; the proportion of medical insurance reimbursement is the key economic lever that affects patients’ decision-making. It is worth noting that the cure rate at the grass-roots level presents the “ability paradox” effect: the low cure rate stimulates the technical needs of both doctors and patients, while the high cure rate inhibits the willingness to adopt. The case analysis and numerical simulation results based on Yanchuan county show that the system can be effectively guided to the evolutionary equilibrium of doctor-patient collaboration through the tilt of medical insurance policy, the dynamic adjustment of resource sinking efficiency, and the strengthening of institutional accountability and performance constraints. From the perspective of the evolutionary game, this study provides a theoretical reference for the mechanism design and policy optimization of telemedicine enabling primary care, and has enlightenment significance for the construction of a hierarchical diagnosis and treatment system based on primary care.

Research on Symbiotic Mechanism of Public Demand and GovernmentResponse from Perspective of Ecosystem:Taking People’s Network as Example

QIN Ruiqing, ZHANG Peng, LIU Jing, ZHAO Chenyang, QIN Xi

2025, 34(11):  180-186.  DOI: 10.12005/orms.2025.0360

Asbtract ( )   PDF (1183KB) ( )  

References | Related Articles | Metrics

With the update and development of the Internet era, the interaction between the people and the government has been updated from the traditional way of interaction to the way that depends partly on the Internet. The people are more and more inclined to express their opinions and respond to problems through the Internet. In this trend, if the government fails to respond and solve the problems raised by the people on the Internet in a timely manner, the people’s trust in the government will be ruined. The People’s Daily Online message board for local leaders is a public space connecting the country to society in the digital age, and its original intention is to enhance people’s trust in the government and support for it through a two-way interactive mechanism. However, the problem of insufficient responsiveness arises, actually. New government media such as People’s Daily Online is an important channel for the Party and the government to contact and serve the people, and its implementation effect is the embodiment of the government’s social governance ability and the concept of governing for the people. Therefore, how to create a benign ecosystem of coordinated development and circular evolution is crucial.
   The Lotka-Volterra model is one of the most well-known models in ecology and is widely used to describe dynamic equilibria and interactions between the predator and prey. The theoretical basis of the model is to model the change of the number of two key populations in the biome over time. The model is derived from the abstraction of natural selection, resources competition and the relationship between biological populations in the ecosystem, which provides a powerful mathematical framework for the quantitative research of ecology. The two differential equations at the core of the Lotka-Volterra model can give the relationship between the periodic fluctuations of the number of predators and preys caused by the interaction between them.
   In this paper, the ecological Lotka-Volterra model is used to study the dynamic evolution process of the symbiotic relationship between the government and Internet user on the network platform, and the symbiotic model between them is predicted according to the symbiotic degree relationship, as well as the dynamic evolution of the corresponding Internet user’s comments and the government’s replies. In this paper, the data of the epidemic period from 2020 to 2022 are excluded, and the data of the annual number of netizens’ comments and government responses from the leadership message board of People’s Daily Online from 2006 to 2020 are selected as the experimental data of this paper.
   The experimental results show that the symbiotic pattern between the government and Internet user was parasitic between 2006 and 2007, and from 2008, the symbiotic structure between them has begun to change to a mutualistic symbiotic pattern. It is further proposed that the time for the government’s response and netizens’ demands to achieve symmetrical mutualism was 2022, and they can evolve from a similar symmetrical mutualism model to symmetrical mutualism. In the future, the amount of netizens’ comments and government responses in the leadership message boards of People’s Daily Online at different provinces, cities and counties can be collected respectively, and the symbiotic evolution dynamic model constructed in this paper can be applied to compare the evolution trend of symbiotic behaviors in many cities in China, explore the development degree of symbiotic relationships in different cities, and then provide references for optimizing the management and services of different types of new government media.

How Can Government Subsidies Guide Manufacturing Industry toAchieve Comprehensive Green Transformation?——Analysis Based on Improved RDEU Model

JIANG Chen, CHEN Juhong, WANG Hao, FENG Tinglong

2025, 34(11):  187-194.  DOI: 10.12005/orms.2025.0361

Asbtract ( )   PDF (1978KB) ( )  

References | Related Articles | Metrics

As environmental pollution issues become increasingly severe, the comprehensive green transformation of the manufacturing industry has emerged as an urgent and important topic. However, the characteristics of high investment, high risk, long cycles, and slow effects, combined with many companies’ pursuit of short-term benefits, result in a lack of motivation and willingness to implement green transformation. In this context, the government, as the regulatory body for the green transformation of manufacturing enterprises, needs to take certain incentive measures to reduce the cost pressure of green transformation for manufacturing enterprises, thereby promoting their implementation of green transformation. However, from a long-term perspective, excessive government subsidies not only increase the fiscal burden of local governments but also suppress the innovation ability of enterprises. Therefore, in promoting the green transformation of the manufacturing industry, it is important to find a balance point that considers both the government’s fiscal capacity and stimulates the long-term transformation motivation of enterprises. Therefore, designing a reasonable government subsidy mechanism that can effectively stimulate manufacturing enterprises’ motivation and willingness for green transformation, while avoiding significant financial pressure on local governments, is the key to achieving a comprehensive green transformation of the manufacturing industry.
   This paper first builds an improved Rank-Dependent Expected Utility (RDEU) model based on the RDEU theory and evolutionary game theory, considering the impact of government subsidies and transformation costs on the emotions of local governments and manufacturing enterprises. Secondly, it solves and analyzes the evolutionary stability of game strategies of local governments and manufacturing enterprises under different emotional combinations, thereby revealing the possible changes in their decision-making behaviors of governments and manufacturing enterprises under different emotional combinations of states. Finally, the above results are analyzed through numerical simulation, and the sensitivity analysis of the amount of government subsidy and transformation cost in the scenario of pessimistic local government and optimistic manufacturing enterprises is conducted to further explore the impact of government subsidy and enterprise transformation cost on decision-making.
   The research finds: (1) When local governments are in a rational state, there is a natural contradiction between socio-economic growth and environmental protection in their view, leading to a rational choice by local governments not to prioritize “efforts for environmental protection”. This choice does not promote the green transformation of manufacturing and is detrimental to sustainable development of society. (2) When local governments are influenced by emotions, if the government subsidy is low, although the government may have an optimistic attitude towards implementing subsidy policies. However, such a low subsidy policy is unlikely to motivate manufacturing enterprises to choose green transformation, which is still disadvantageous to the sustainable development of society. (3) Only when local governments show pessimistic emotions, and manufacturing enterprises are in a rational state or optimistic emotions, can this promote local governments to implement subsidy policies and manufacturing enterprises to choose comprehensive green transformation, which will benefit the sustainable development of society. (4) For manufacturing enterprises with different transformation costs, there are different optimal subsidy intervals. (5) Enterprises with lower transformation costs are more likely to exhibit shortsighted behavior, caring more about the benefits they can obtain from government subsidies. In contrast, those with higher transformation costs are often less sensitive to the amount of government subsidies.
   Based on these findings, this study provides decision-making recommendations for governments and manufacturing enterprises, aimed at helping achieve the comprehensive green transformation of the manufacturing industry, thereby promoting the sustainable development of society.

Research on Impact of Global Financial Cycle and its Uncertaintyon RMB Exchange Rate Fluctuations:Empirical Analysis Based on GJR-GARCH-MIDAS Model

LU Yan, QIN Guoting

2025, 34(11):  195-201.  DOI: 10.12005/orms.2025.0362

Asbtract ( )   PDF (1107KB) ( )  

References | Related Articles | Metrics

Exchange rate is the cornerstone of macroeconomic stability. Maintaining exchange rate stability is one of the important objectives of the central bank’s monetary policy control and is also an important guarantee for China to build a new development pattern with domestic cycle as the main body and domestic and international double cycle promoting each other. Since the “7.21” exchange rate reform in 2005, the volatility of the RMB exchange rate has been increasing, so it is crucial to understand the sources of RMB exchange rate fluctuations and improve the tracking and forecasting of RMB exchange rate fluctuations. At present, China’s level of openness continues to rise, and the internationalization of the RMB is gradually increasing, which makes the role of global financial factors in determining RMB exchange rate fluctuations attract extensive attention from both the practical and theoretical communities.
   With the deepening of global economic and financial integration, various financial indicators, such as asset prices, credit growth, institutional leverage and cross-border capital flows, show a trend of coordinated movement, i.e., there is a “global financial cycle”. As a concise summary of changes in the international economic and financial situation, will the global financial cycle have an impact on RMB exchange rate fluctuations? What are the mechanisms behind and the characteristics of its impact? Especially in the special case of dual offshore and onshore exchange rates for the RMB, what is the difference in the impact of the global financial cycle on the two exchange rates? Against the background of the continuous rise in RMB exchange rate volatility in recent years and the intensification of fluctuations in the global financial cycle, an in-depth analysis of the impact of the global financial cycle on RMB exchange rate fluctuations will not only be conducive to the improvement of monitoring and risk management of fluctuations in the foreign exchange market of the RMB, but also provide useful insights for Chinese enterprises and supervisors to timely prevent and resolve the risk of large fluctuations in the RMB exchange rate due to the shocks of the global financial cycle.
   This paper focuses not only on the global financial cycle level shock, but also further on the global financial cycle uncertainty shock. Based on analyzing the theoretical mechanism of the global financial cycle level shock and its uncertainty shock affecting the RMB exchange rate volatility, it empirically examines the impacts of the global financial cycle level and uncertainty shocks on the RMB exchange rate volatility by using the GJR-GARCH-MIDAS model. It overcomes the problem of variable frequency mismatch and portrays the asymmetric leverage effect of exchange rate volatility to improve the accuracy of the model estimation results. Secondly, this paper further compares the impacts of global financial cycle level shocks and uncertainty shocks on the long-term volatility of the RMB exchange rate and their dynamic characteristics, as well as the heterogeneity of the impacts on the onshore and offshore RMB. Finally, recursive estimation is used to forecast RMB exchange rate volatility out-of-sample, and the forecasting accuracy of the global financial cycle and its corresponding model on RMB exchange rate volatility is evaluated by measuring MAE and RMSE indicators and constructing portfolios.
   The research shows that: First, global financial cycle horizontal shocks (cycle downturns) and uncertainty shocks will have a significant positive impact on RMB exchange rate fluctuations, and the impact of global financial cycle uncertainty shocks on RMB exchange rate fluctuations is stronger. The persistence is higher than the level of global financial cycle shocks. Second, the impact of the global financial cycle on the onshore and offshore RMB exchange rates is heterogeneous. Compared with the offshore RMB exchange rate, the horizontal shocks and uncertainty shocks of the global financial cycle have a weak impact on the fluctuations of the onshore RMB exchange rate. Weak as they are, their effect lasts longer. Third, the ability of global financial cycle uncertainty to predict RMB exchange rate fluctuations is higher than that of the global financial cycle average level. Models containing global financial cycle uncertainty indicators have stronger explanation and prediction capabilities for RMB exchange rate fluctuations, and can bring higher investment performance to investors.

Effects of Commercial Banks’ Multi-regional Operations on Inter-provincial Trade:Evidence from Banking Deregulation

TIAN Lin, HU Jun, MA Yin

2025, 34(11):  202-208.  DOI: 10.12005/orms.2025.0363

Asbtract ( )   PDF (1005KB) ( )  

References | Related Articles | Metrics

Inter-provincial trade has been a powerful engine behind economic growth under the new development pattern in China. Inter-provincial trade patterns can not only reflect the comparative advantages and division networks of trading regions, but also help to identify the feedback effects of regional economic influences, drawing a meaningful implication for the coordinated regional economy development. Existing literature focusing on the influencing factors of interregional trade has widely supported that the trade patterns are significantly determined by statically district differences in factor endowments. However, how the development of financial markets contributes to trade is a relatively unexplored area of the trade theory.
   In fact, a well-developed financial market has long been confirmed as one of the vital determinants of market integration. The theoretical conjecture that financial development would matter for economic growth has pointed out that the higher degree of financial market development may promote specialized production in a country or a region, and benefit industries with increasing returns to scale by optimizing the allocation of capital and mobilizing and pooling savings, which in turn affects its trade patterns.
   Following the line of the reasoning, this paper empirically examines how the commercial banks’ multi-regional operations may impact trade patterns across province-pairs, by employing the No. 143 deregulation policy on entrants and outlets’ expansion of commercial banks at the prefecture-level in China as a natural experiment. We pair 30 China’s provinces using the 2007 and 2012 inter-regional input-output data, and find that the geographical diversification of banks induced by the deregulation increases both the volumes and shares of inter-provincial trade in China through information spillover benefits. It is also found that such favorable effect is especially stronger for the central and western regions with lower level of financial development and less customer base advantages of city commercial banks. In addition, we show a heterogeneous effect of banks’ cross-regional expansion across industry sectors. As the market opens up, multi-regional banks would facilitate external financing conditions of the trading and capital-intensive sectors by easing information frictions, thus enhancing the overall activeness of trade flows across regions. Finally, we argue that the banking integration effect on inter-provincial trade actually is different, depending on bank types in different regions. The benefits of multi-market city commercial banks are only available for the inter-provincial trade across central and western regions.
   We expect to contribute to existing literature in three important ways. Firstly, this paper identifies the mechanisms underlying the linkage between multi-market banking and inter-provincial trade. Banks are generally believed to be efficient information producers. The advantage that multi-regional banks possess in resolving information problems has implications for trade across regions since it would be more effective in collecting soft information on borrowers and reducing screening and monitoring costs through widely dispersed operations in different economic environments. Secondly, this paper offers additional evidence on the pivotal role of banking in establishing dynamic comparative advantage of trade through a more efficient resources allocation. Trading and capital-intensive industrial sectors enjoy a greater benefit from banking integration, and the easier financial access to the sectors gives rise to a pattern of credit advantage which determines trade structure across regions. Thirdly, this paper shows the heterogeneity of banking deregulation effects at the provincial level in China. Banking market characteristics have an intrinsically ambiguous effect on economic growth. Only when taking into consideration of multi-dimensional perspectives in a unified analysis framework covering the characteristics of both banking market and real economy, can we comprehensively grasp the theme of operating mechanism in the relationship between banking development and economic growth.
   Overall, this paper demonstrates an importance of finance in the integration of interregional trade, where factor mobility and reasonable allocation could be enhanced with the space overall arrangement of finance. It provides additional empirical evidence on finance-growth nexus, and may offer an enlightenment for policy making aiming at improving financial efficiency and unifying the national market in China.

Management Science

Survival of the Fittest or Mutual Benefit?An Analysis of Blockchain Adoption Strategy in Organic Agricultural Product Supply Chain

YE Fei, CHEN Jun, LIANG Lunhai, YAN Hui

2025, 34(11):  209-216.  DOI: 10.12005/orms.2025.0364

Asbtract ( )   PDF (1317KB) ( )  

References | Related Articles | Metrics

In the organic agricultural product supply chain, the lack of a comprehensive traceability and regulatory system to ensure product quality and safety often leads to consumer doubts when purchasing organic products. Consumers are uncertain about whether the organic products they buy are produced strictly according to organic standards. Moreover, organic agricultural products generally sell at higher prices than at conventional ones, which makes it challenging for them to compete effectively with conventional agricultural products, despite their superior quality. Therefore, providing consumers with access to reliable traceability information is essential for the growth of organic agricultural enterprises. Blockchain, a widely adopted digital technology, offers decentralization, transparency, openness and immutability, ensuring full lifecycle traceability of organic agricultural products. In a competitive market, organic agricultural products can leverage blockchain technology to track and share quality data throughout the supply chain. This enhances their differentiation from conventional products, strengthens brand image, and builds consumer trust.
   However, adopting blockchain technology can be costly, and market competition may result in inconsistent willingness among upstream and downstream firms. Additionally, the disclosure effectiveness of blockchain can be affected by Internet of Things (IoT) devices. In other words, organic agricultural enterprises must carefully balance among costs, trust enhancement effects and market competition when making blockchain adoption decisions.
   Therefore, this study focuses on the organic agricultural product supply chain in a competitive market, where both organic and conventional farmers sell products through fresh e-commerce platforms. We develop a Stackelberg game-theoretical model to analyze the incentives of organic farmers and e-commerce platforms to adopt blockchain technology. By comparing the scenarios before and after adopting blockchain technology, we analyze the adoption strategies of both parties and their impacts on pricing decisions, supply chain member profits and social welfare.
   We find that the adoption of blockchain technology in the organic agricultural supply chain is mainly driven by the product brand value added by blockchain and the investment cost of blockchain adoption. When the brand value added is low, both organic farmers and fresh platforms can only benefit if the investment cost is also low. However, when the brand value added is high, it outweighs the negative impact of the investment cost, allowing both parties to benefit from blockchain adoption. Considering the competition with conventional agricultural products, the organic farmer is more inclined to adopt blockchain, while the e-commerce platform shows less willingness. As competition intensifies, the organic farmer’s willingness to adopt it increases, while the platform’s willingness decreases. Interestingly, blockchain adoption can benefit both the organic farmer and platform without negatively affecting the conventional farmer. In certain scenarios, such that when the brand value added is weak but investment cost is moderate, or when the brand value added is strong and the investment cost is high, blockchain adoption can lead to a win-win-win outcome for the organic farmer, conventional farmer and platform. Whether the blockchain adoption can improve consumer surplus and social welfare is also influenced by the brand value added and the investment cost of blockchain. Blockchain adoption can improve consumer surplus and social welfare when both the organic farmer and e-commerce platform benefit from it.
   This study provides decision support and management insights for promoting the application of blockchain technology in the agricultural product supply chain. However, the model assumes a scenario where agricultural output is determined and farmland is sufficient. In reality, factors like climate and pests can affect agricultural product output, potentially limiting production. Future research could explore how output fluctuation and production constraint affect blockchain adoption strategy in the organic agricultural product supply chain.

Research on Financing Decision of Carbon Emission ReductionTechnology R&D from the Perspective of Carbon Tax

XIA Xiqiang, LIU Cong, WANG Zhongze

2025, 34(11):  217-223.  DOI: 10.12005/orms.2025.0365

Asbtract ( )   PDF (1305KB) ( )  

References | Related Articles | Metrics

In response to the environmental problems we are currently facing, governments around the world have taken a series of measures to reduce carbon emissions. The implementation of carbon tax policy significantly affects the production and operational activities of enterprises, prompting them to transition towards more low-carbon and environmentally friendly production methods. In this context, more and more enterprises have chosen to increase their investment in Research and Development (R&D) for carbon emission reduction technologies. However, not all of these enterprises have sufficient funds to effectively advance the R&D of such technologies. In practice, Small and Medium-sized Enterprises (SME) with financial constraints often have financing demands for carbon emission reduction technology R&D projects. It is worth noting that there are risks of failure in the R&D of carbon emission reduction technologies, which weakens SME motivation for R&D and affects their R&D and financing decisions. Therefore, it is necessary to conduct an in-depth study on whether enterprises will choose to carry out carbon emission reduction technology R&D activities by means of financing under the carbon tax policy. How do the risks associated with R&D of carbon emission reduction technologies impact the collaboration between SME and financial institutions in the realm of carbon finance? Based on the aforementioned considerations, this paper constructs a game model between a capital-constrained enterprise and a financial institution, compares and analyzes the optimal decision-making of both the baseline model and the carbon emission reduction technology R&D financing model. The aim is to derive an optimal R&D financing strategy for the supply chain that accounts for the risks associated with R&D failure.
   The main conclusions of this paper are as follows: Firstly, when the success rate of R&D is low, if the enterprise does not have bankruptcy risk, both product sales and profits for the enterprise and the financial institution will consistently exceed those in the baseline model. However, if the enterprise does face bankruptcy risk, the baseline model effectively mitigates the risks for both parties, ensuring that their profits remain intact. Secondly, the carbon tax rate is also an important factor affecting the choice of financing strategy. A higher tax rate increases the bankruptcy risk for enterprises, prompting those with limited liability capacity to adopt a more aggressive production and R&D strategy. Finally, it is important to note that the carbon emission reduction technology R&D financing model is not universally beneficial for the environment. The effectiveness of emission reduction technology R&D in promoting environmental protection is contingent upon specific conditions being met regarding carbon emissions per unit of product and the success rate of R&D.
   This paper establishes a theoretical foundation for the government to formulate an appropriate carbon tax rate, while promoting the collaboration between enterprises and financial institutions to mitigate emissions. Additionally, it examines the boundary conditions necessary for the effective carbon finance cooperation between these entities, offering both theoretical and practical support for SME carbon financial financing activities and their regulation in the context of carbon tax. However, this paper acknowledges certain limitations that need further exploration in future research. Firstly, it focuses solely on a supply chain comprised of a single enterprise and a single financial institution. In reality, supply chains typically involve multiple participants, and the decision-making processes of these members are influenced by a variety of factors, making their decision-making challenges more complex. Secondly, this study assumes that the financing interest rate set by financial institutions is fixed. Future research could incorporate financial institutions into the decision-making process to investigate the financing strategies of enterprises when the interest rate is endogenous.

Joint Decision-making on Yield and Innovation Intensityunder Cost-reducing Innovation

DAI Jiansheng

2025, 34(11):  224-231.  DOI: 10.12005/orms.2025.0366

Asbtract ( )   PDF (972KB) ( )  

References | Related Articles | Metrics

Technological innovation can reduce unit manufacturing costs, which in turn influences production decisions. Changes in production quantities can also affect the level of technological innovation. In this context, we address the joint decision-making problem of technology innovation intensity and product output for a manufacturing firm by constructing a newsvendor model involving two decision variables. First, we prove the existence of an optimal solution to this problem. Subsequently, we establish a sufficient condition-by imposing a mild restriction on the demand distribution function-that ensures the uniqueness of the solution. We then analyze the impact of the firm’s innovation capability, retail price and unit production cost on its strategic decisions with the following key findings:
   The firm’s innovation capability significantly influences both the intensity of technological innovation and its output strategy. Generally, higher innovation capability-up to a certain threshold-leads to greater output and stronger innovation intensity. Beyond this threshold, further increases in innovation capability no longer impact output or innovation intensity. Notably, if the firm has no innovation capability, no technological innovation will be undertaken.
   Retail price and unit production cost also play a critical role in determining both the intensity of technological innovation and the firm’s production strategy. Output and innovation intensity increase with retail price, though their relationship with unit production cost is more complex. As retail prices rise, output continues to increase, while innovation intensity grows until it reaches a maximum point. However, the relationship between innovation intensity and unit cost is not monotonic. There are two key exceptions: (1) when innovation capability is particularly strong, optimal innovation intensity will rise with production costs; and (2) output decreases as unit costs rise, especially when production costs at least double the minimum cost achievable with current technology. From an industry-wide perspective, firms with higher pre-innovation costs continue to face higher post-innovation costs than those with lower initial costs, despite adopting more intensive innovation strategies.
   Additionally, we propose a hypothesis regarding the demand distribution function that guarantees the uniqueness of the optimal solution. This hypothesis can serve as a reference for future research. Furthermore, we provide criteria to determine whether this hypothesis holds for uniform, negative exponential and normal distributions. Notably, the methods outlined in this paper, particularly for normal distribution, can be extended to other continuous distributions, such as the power, Weibull, gamma and beta distributions.

Research on Mechanism of “False Promotion” Based onPromotional Expansion and Long-term Effects

LIU Meng, ZHANG Kai, GAN Hongcheng

2025, 34(11):  232-239.  DOI: 10.12005/orms.2025.0367

Asbtract ( )   PDF (1311KB) ( )  

References | Related Articles | Metrics

In order to maintain the growth of retail sales, e-commerce platforms regularly organize online promotional activities, and third-party merchants on the platform actively participate in them. Promotional activities can attract a large number of users in a short period of time. For example, during the 2023 Tmall Double 11 promotional period, the cumulative number of visiting users exceeded 800 million, with more than 20 million new purchasing users and over 140 million orders added, achieving market expansion during the promotional period. However, some merchants may experience a decrease in product sales after the promotion. For instance, Amazon merchants saw a drop in order volume after the Prime Day promotion. Therefore, promotional activities initiated by the platform not only increase demand during the promotional period, helping merchants to expand market share and attract new customers in the short term (promotion expansion effect), but also have an impact on the long-term product demand of merchants (long-term effect of promotion). The short-term expansion effect during the promotion and the long-term effect after the promotion add complexity to the decision-making process of the platform and merchants, causing merchants to continuously adjust pricing strategies during the promotional cycle, leading to “false promotions.” The internal reasons and mechanisms for merchants to engage in false promotions are not fully understood at present. Based on this, this paper analyzes the impact of the expansion effect and long-term effect of promotions on the strategic choices of platforms and merchants, reveals the internal mechanism of merchants' false promotions, and helps platforms and merchants to formulate more reasonable promotional strategies and improve promotional effects.
   This paper starts with the pre-promotion period as the research starting point, and based on previous research, constructs a dynamic game model of the platform and third-party merchants during the pre-promotion, the promotional and the post-promotion period. At the same time, considering the short-term expansion effect of the promotional activities and the possible positive long-term effect, negative long-term effect and no long-term effect in the post-promotion period, the paper discusses the impact of the expansion effect and long-term effect on the promotional strategies of the platform and third-party merchants. This paper attempts to answer the following four questions: (1)How should e-commerce platforms and merchants formulate different stages of promotional and pricing strategies when there is an expansion effect and long-term effect of promotion? (2)How do the expansion effect and long-term effect affect the optimal strategies of platforms and merchants? (3)How do we expose the internal mechanism of merchants’ “false promotion” behavior? (4)How do e-commerce merchants interact in price at different stages?
   The research findings find that, during the promotional period the promotional strength provided by the platform increases with an expansion effect and an increase in the commission ratio. When there is a significant negative long-term effect, the platform tends to adopt a conservative promotional strategy. Merchants will reduce the price in the pre-promotion period when the market expansion effect is small and there is a negative long-term effect, and increase the price in the pre-promotion period when the expansion effect is large. Merchants will increase the price during the promotional period and make the price higher than the basic price in the no-promotion situation in two cases: first, when the long-term effect of the promotion is negative; second, when the long-term effect is positive and relatively small, and the expansion effect is moderate. When the long-term effect of the product is negative, it will be more likely to produce false promotions, and an increase in the expansion effect will increase this possibility. It is also found that when the expansion effect of promotions is significant, false promotions can also reduce consumer surplus.
   This paper enriches the theoretical research related to e-commerce promotions and provides a basis for decision-making during the promotional period for e-commerce platforms and third-party merchants. In the future, platform promotions with returns will be considered; at the same time, the competition between platforms and merchants will be included in the research context.