Table of Content

25 December 2025, Volume 33 Issue 12 Previous Issue   

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Review of the Development of Agent based Modeling and Order Book Market Modeling Yian Cui, Lijian Wei, Xiong Xiong 2025, 33 (12):  1-12.  doi: 10.16381/j.cnki.issn1003-207x.2022.0524 Abstract ( 44 )   HTML ( 0 )   PDF (622KB) ( 20 )   Save Agent based modeling is an important application of artificial intelligence in the financial sector and a key tool in financial regulatory technology. It is increasingly evolving into an advanced instrument for regulatory technological innovation and has garnered widespread attention from both academia and industry in recent years. As the agent based modeling research paradigm continues to mature， three major research directions have been formed： individual behavior modeling， market mechanism modeling， and model calibration. The research approach to individual behavior modeling has progressively evolved from simplicity to complexity （how to replicate more stylized facts） and then from complexity back to simplicity （whether complex models can explain more market dynamics）. Market mechanism modeling is relatively easier to understand， and the modeling process itself is highly important. Researchers continue to dedicate efforts to uncovering the interactive mechanisms between individual behavior and market structures in complex emergent market phenomena. Model calibration has gradually become a standard step in agent based modeling research， reflecting the increasing standardization and maturity of the research paradigm. The order book model， as the trading mechanism most closely aligned with real market operations， has become a focal point of research in agent-based modeling in recent years. Currently， the agent-based order book models are already capable of replicating some of the stylized facts observed in empirical studies， providing important credibility assurance for constructing order book mechanism models that are highly consistent with the A-share market. References | Related Articles | Metrics

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The Dynamic Information Spillover Co-movement Between Commodity Market and Dry Bulk Shipping Market: A Time-frequency Dependent Analysis Bin Meng, Shuiyang Chen, Haibo Kuang 2025, 33 (12):  13-25.  doi: 10.16381/j.cnki.issn1003-207x.2023.0340 Abstract ( 18 )   HTML ( 0 )   PDF (22899KB) ( 6 )   Save The commodity market and shipping market are two vital components of the modern global economy， reflecting the allocation of goods and logistics resources. Shipping accounts for over 90% of global cargo transportation， while commodities provide the material foundation for global economic activities. Their interconnectedness forms a cornerstone for global economic and international trade stability. However， recent major events such as pandemics and geopolitical crises have caused significant fluctuations in commodity prices， leading to cyclical fluctuations in the shipping market， destabilizing this cornerstone. There is an urgent need to comprehensively examine the micro-level linkage mechanisms and dynamic information spillover effects between the commodity market and dry bulk shipping market. This involves studying the interdependencies between commodity markets and shipping markets at multiple scales and exploring the underlying factors contributing to the instability of the shipping-commodity cornerstone. It focuses on 12 types of dry bulk charter rates， including those for Capesize and Panamax vessels for 6-month， 1-year， and 3-year periods， as well as rates for trans-Pacific， trans-Atlantic， and Far East to Europe routes. It also examines 6 categories of metal/non-metal ores （including iron ore， copper， aluminum， lead， zinc， and coal） and 8 categories of agricultural products （wheat， soybeans， corn， cotton， cocoa， sugar， rice， and coffee） using weekly data from December 18， 2009， to September 10， 2021. In-depth analysis of the dynamic dependency relationships and time-frequency information spillover between dry bulk shipping and commodity markets is conducted. The study employs Continuous Wavelet Transform （CWT） to reveal the time-frequency dependency and potential lead-lag relationships between the two markets. Additionally， a two-stage time-frequency overflow index model is constructed， Maximum Overlap Discrete Wavelet Transform-DY （MODWT-DY）， to characterize the multiscale dynamic information spillover effects of the two markets.Empirical results demonstrate that there exists a strong time-frequency dependency relationship between dry bulk shipping and metal/non-metal ores， characterized by high-frequency complexity， mid-frequency crisis correlation， and low-frequency long-term stability. Agricultural products show more high-frequency dependency relationships due to factors such as climate uncertainty and their perishable nature. Aluminum dominates the three charter rates and plays a significant role in price discovery. At low-frequency scales， aluminum， copper， coal， and zinc exhibit price discovery functions for dry bulk charter rates. All charter rates demonstrate a price discovery function for iron ore， with this dependency relationship experiencing superimposed and spreading effects amid events like the US-China trade war and pandemics. These research findings hold significant implications for governments seeking to enhance global resource allocation capabilities and increase the price influence of important commodities， thus promoting a new development pattern of “dual circulation”. They also provide important references for market investors and risk managers in assessing price and risk trends and can guide market operations for shipowners. Figures and Tables | References | Related Articles | Metrics

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The Influence of Information Spillover on Liquidity Risk Contagion in Bank-firm Guarantee Network Zhinan Li, Jingyue Lei, Peilong Shen 2025, 33 (12):  26-40.  doi: 10.16381/j.cnki.issn1003-207x.2024.0427 Abstract ( 17 )   HTML ( 0 )   PDF (2111KB) ( 21 )   Save During China’s three default waves in 2008， 2011， and 2015， the impact of corporate defaults was transmitted to financial institutions. However， the risk did not remain contained； it further propagated to other firms. This resulted in a significant number of normally functioning firms—those that do not belong to the same industry chain and lack direct transactional relationships but are interconnected through the same bank or guarantee institution—falling into liquidity crises one after another. This phenomenon cannot be explained by existing risk transmission mechanisms. Therefore， the phenomenon is explained from the perspective of information spillover and its impact on liquidity risk contagion in the bank-firm guarantee network is explored. A multi-layer bank-firm guarantee network model is constructed including an interbank lending network， a supply chain commercial credit network， a supply chain financing guarantee network， a commercial guarantee network between firms and guarantee institutions， and a loan network between banks and firms. The information is divided into bankruptcy information and liquidity information， and computational experimental methods are used to study the influence of information spillover on liquidity risk contagion in the bank-firm guarantee network.Research indicates that in the absence of external shocks， information spillover tends to increase the number of liquidity bankruptcies for banks and firms. Conversely， during periods of external shocks， information spillover leads to an increase in firm liquidity bankruptcies while simultaneously decreasing bank liquidity bankruptcies. The parameter sensitivity analysis reveals that in highly uncertain external environments， information spillover positively influences banks by enhancing business stability and mitigating external risks. Improving banks' risk awareness and risk appetite， along with making positive information disclosure， can help mitigate the adverse effects of information spillover and reduce the extent of liquidity risk transmission. When bank-firm leverage levels are high while firm management ability is low， information spillover may exacerbate liquidity risk contagion. Furthermore， if there is a liquidity information spillover between firms， they can timely adjust accounts receivable to restrain liquidity risk contagion. The impact mechanism of information spillover on risk contagion in bank-firm guarantee networks is investigated， contributing to the research on risk contagion between banks and firms. The research findings provide theoretical insights into mitigating information asymmetry between banks and firms and reducing the adverse effects of information spillovers on liquidity risk contagion within these networks. Figures and Tables | References | Related Articles | Metrics

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Hybrid Multivariate Regression Forecasting for Gold Prices: A Decomposition-Reconstruction-Ensemble Methodology Zhaorong Huang, Zhengyang Song, Bo Yang, Nengmin Zeng, Le’an Yu 2025, 33 (12):  41-56.  doi: 10.16381/j.cnki.issn1003-207x.2024.2305 Abstract ( 26 )   HTML ( 0 )   PDF (3172KB) ( 9 )   Save The pricing mechanism of gold assets is particularly complex， and it is difficult to fully reveal the rich multidimensional information contained in its inherent feature space through a univariate time series analysis framework. A hybrid regression model with robust decomposition and hierarchical integration strategies for gold price prediction is proposed from the perspective of decomposition-reconstruction-ensemble， effectively exploring the synergistic effects of mixed financial influencing factors at different time scales. Firstly， a stable variational mode decomposition （SVMD） technique is developed to extract the stable center frequency component boundaries of the gold price sequence for continuous feature learning. Then， using the Hurst exponent as a memory reconstruction index， the decomposition boundary is reconstructed into short-term， medium-term， and long-term scale components. Subsequently， utilizing the advantages of feature selection from scaled principal component analysis regression and minimum maximum concavity penalty regression， a hybrid linear regression （HLR） is constructed to extract important financial features for prediction at different time scales， thereby improving the overall prediction generalization ability. Finally， the hierarchical ensemble method integrated the prediction results of the original layer， reconstruction layer， and component layer to obtain a reconciliatory gold price prediction value. The effectiveness of the proposed model in the three steps of decomposition， reconstruction， and ensemble is validated on the international gold futures price dataset， and the advantages of the proposed model are compared with various prediction models and decomposition modeling strategies in existing research. Figures and Tables | References | Related Articles | Metrics

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Population Aging and Economic Growth in China: An Empirical Study Based on GaR Model Linhai Zhao, Xiaohang Gan 2025, 33 (12):  57-70.  doi: 10.16381/j.cnki.issn1003-207x.2025.0004 Abstract ( 23 )   HTML ( 0 )   PDF (2232KB) ( 14 )   Save With the deepening of population aging in China， how to cope with the impact of aging on economic growth and tap the potential of economic growth has become one of the important issues for China’s high-quality economic development. The driving factors of economic growth are analyzed through the MRW model （the total output function is Y=K^{\alpha }{H}^{\beta }{(AL)}^{\mathrm{1}-\alpha -\beta }， where Y is total output， K and H are physical capital and human capital respectively， L is labor force， A is technical level， and αand β are output elasticities）. Then， using the GaR model combined with quantile regression， and based on China’s time-series data from 1990 to 2023， the impact of population aging on economic growth and the regulatory role of related variables is examined. The research results show that aging is significantly negatively correlated with economic growth in a large range of quantiles； physical capital investment， human capital investment， and technical level have regulatory effects on this negative inhibitory effect in a large range of quantiles. The deficiency of traditional mean regression is made up for in the research on tail risks of economic growth， a new perspective is provided for understanding the relationship between population aging and economic growth， and the conclusions and policy recommendations have certain reference value for related academic research and the formulation of economic policies to cope with aging. Figures and Tables | References | Related Articles | Metrics

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Relationship Specific Investment and Firm Debt Default Risk: Study Based on Structural Analysis Approach and Multiple Nonlinear Mediation Model Ran Huang, Mengyuan Li, Yaqi Zhao 2025, 33 (12):  71-86.  doi: 10.16381/j.cnki.issn1003-207x.2023.0405 Abstract ( 12 )   HTML ( 0 )   PDF (1221KB) ( 3 )   Save Recently， under the impact of the Sino-US trade turbulence and the outbreak of the novel coronavirus pneumonia epidemic disease （COVID-19）， China’s macroeconomic economy is still facing relatively larger challenges. Operational and liquidity risks in both transnational and domestic supply chains have continued to increase， and firm debt default risks have become increasingly significant. Relationship-specific investment （RSI）， as one of the important characteristics of the supply chain， features high exclusivity， a relatively large scale， and a long investment cycle. RSI has both a positive and negative impact on supply chain firms and thus increases the uncertainties in their debt default risk. Therefore， it has become an important issue in the field of supply-chain risk management to explore the influence of RSI on the debt default risk of supply chain firms.Under the framework of structural analysis of credit risk， why firm profit flows could be used to measure firm debt default risk based on the relationship between firm profit and total asset value is discussed. Then， the theoretical analysis is carried out to explore how the RSI influences the debt default risk of firms by affecting firm profit， return volatility， and cash flow risk. In addition， the relevant mechanisms are also tested through mathematical demonstration. Finally， the nonlinear panel regression model is constructed， and the sample data of listed manufacturing companies in China’s A-Share stock market from 2012 to 2021 is used to conduct empirical analysis. It tests the total effect， the indirect effect， and the mediating effect of RSI on supply chain firms’ debt default risk.The result shows that as RSI increases， both firm profit and return volatility present inverted U-shaped changes， while cash flow risk demonstrates U-shaped changes. Based on the structural analysis approach， a higher firm’s profitability helps decrease the firm’s debt default risk， while return volatility and cash flow risk increase the firm’s debt default risk. Under the combined influence of these three factors， the firm default risk shows U-type changes. Furthermore， the moderating effects of the firm’s role in the supply chain and of the external contract environment are studied. The result shows RSI has a significant non-linear effect on the default risk of parts-and-components supplying firms. However， it has almost no impact on product manufacturing firms. Meanwhile， it is also found that， in a good contractual environment， RSI could lead to more profits and less cash flow risk， thus improving firm credit quality more obviously. It is because a good contractual environment helps decrease customer opportunism and strengthen the stability of the supply chain. In a worse contractual environment， the positive return effect of the RSI may be offset by the negative risk effect， and thus it only plays a limited role in improving firm credit quality.The supply chain firms’ debt default risk is studied from the perspective of relationship-specific investment. It expands the framework for analyzing the impacts of supply chain characteristics on firm default risk. The application of the credit risk structural analysis method in empirical research is innovated by evaluating credit risk based on the profit flows and coordination mechanisms of the supply chain. It extends the traditional structural models of credit risk that focus only on the change in a firm’s asset value. This structural approach could be used to access the effects of other important supply chain characteristics， such as supplier/customer concentration and trade credit， on firms’ creditworthiness. Additionally， it is practically valuable in helping financial institutions， such as banks， and other investors evaluate a firm’s RSI decision more comprehensively and manage and control the firm’s default risk more accurately. Figures and Tables | References | Related Articles | Metrics

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Data Investment and Pricing Decisions for Online Platforms Considering Data Advantage Xuan Xi, Yulin Zhang 2025, 33 (12):  87-99.  doi: 10.16381/j.cnki.issn1003-207x.2024.0136 Abstract ( 21 )   HTML ( 0 )   PDF (1519KB) ( 10 )   Save As data emerges as a pivotal driver of the robust growth of the platform economy， the competition for data resources among many online platforms becomes more and more intense. Focusing on the impact of data dominance on online platforms， a two-stage game model is employed to explore the data investment and user pricing decisions of competing platforms. Three scenarios are considered： monopoly， differentiated competition with multi-homing advertisers and differentiated competition with single-homing advertisers. The results show that the market attractiveness of data investment always depends on the tradeoff between the marginal effect of data investment on users and the data trading value in all scenarios. However， in competitive environments where data differentiation exists， data advantage does not always incentivize the platform to increase the level of data investment or pricing decisions. Instead， data investment strategies are shaped by the platform's capacity to monopolize the data market. With the increasing effect of data investment on consumers' utility， the data investment strategy of competing platforms will undergo four stages of evolution： from “no investment” to “platform investment with data disadvantage，” onward to “both investments”， and eventually culminating in “platform investment with data advantage”. When compared to the scenario involving multi-homing advertisers， the single-homing advertisers setup yields a diminished level of data investment alongside elevated prices. More importantly， the single-homing behavior of advertisers amplifies the platform's dual competition encompassing both user engagement and data， thereby dampening the attractiveness of investment while simultaneously boosting the platform's potential to monopolize the data market. Figures and Tables | References | Related Articles | Metrics

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Research on the Mechanism of Local High-Quality Teaching Resources Empowering the Innovative Development of Military Academy Professional Education Based on the System Dynamics Guitao Wang 2025, 33 (12):  100-109.  doi: 10.16381/j.cnki.issn1003-207x.2023.0857 Abstract ( 18 )   HTML ( 0 )   PDF (1523KB) ( 6 )   Save In response to the challenges posed by the rapidly evolving forms of modern warfare， effectively integrating local high-quality teaching resources into the professional education system of military academies has become an imperative for enhancing the quality and efficiency of talent cultivation. However， inherent differences between military and civilian sectors in training orientations， organizational cultures， and policy mechanisms create potential obstacles in the process of resource absorption and integration. If not properly managed， the mere "introduction" of resources may not translate into effective systemic "empowerment." This study aims to uncover the empowerment mechanism from "resource input" to "capability generation." By constructing a system dynamics model， it analyzes the causal relationships among core elements such as military occupational requirements， talent quality development， military-civilian cultural integration， and policy regulations. The study designs 12 different scenarios for simulation experiments to evaluate the effects of empowerment under various strategies， thereby providing a scientific basis for optimizing resource integration pathways， mitigating potential risks， and achieving stable， sustained empowerment. Figures and Tables | References | Related Articles | Metrics

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A Study of Action Selection Policies for Multi-Agent Nash Q-Learning Model Song Han, Can Li 2025, 33 (12):  110-120.  doi: 10.16381/j.cnki.issn1003-207x.2023.0142 Abstract ( 11 )   HTML ( 0 )   PDF (1011KB) ( 2 )   Save Optimization of action selection strategy for multi-agent Q-Learning model is one of the urgent problems to be solved in the simulation process of complex economics games. The forced ε-greedy action selection policy is introduced into the multi-agent Nash Q-Learning model， the effect of this action selection policy is compared with the classical ε-greedy policy through game experiments， and the effect of this action selection policy on the computational speed and convergence of the algorithm is explored； at the same time， theoretical verification of the veracity of the algorithm is carried out according to the experimental results， and a theoretical verification of the algorithm based on the nature of the multi-agent model is given. The simulation results show that forced ε-greedy is suitable for more complex games involving more state actions and more rounds， at which time it can effectively improve the running performance of the multi-agent Q-Learning algorithm， but due to its nature of initially increasing the exploration of actions， which will consume some rounds and lead to a decrease in the equilibrium convergence rate. Therefore， the performance improvement brought by enforcing ε-greedy versus the lost equilibrium convergence rate is an issue that users need to weigh when applying this policy.The main contributions of this paper are：1） comparing the model performance under the classical action selection strategy and the extended action selection policy， and conducting theoretical validation of the algorithm's veracity based on the simulation results， and summarizing the characteristics of the application of this extended policy in the multi-agent Nash Q model； 2） based on the analysis of the theoretical properties of multiple multi-agent models and the process of proof of convergence， the conclusion of the generalization of this action selection policy is given for the multi-agent reinforcement learning algorithms with action selection link provide a strategy to enhance the model computing speed and improve the convergence results. This promotes the operation efficiency of complex reinforcement learning models in economics， and to a certain extent， it can reduce the cost of experiments， and increase the number of experiments within the allowable range of computational volume to improve the reliability of experimental results. Figures and Tables | References | Related Articles | Metrics

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Partial Ordinal Priority Approach Considering Pareto Optimal Identification for Multi-attribute Decision-making Renlong Wang, Rui Shen, Hong Chi, Xueyan Shao, Mingang Gao 2025, 33 (12):  121-133.  doi: 10.16381/j.cnki.issn1003-207x.2023.1805 Abstract ( 22 )   HTML ( 0 )   PDF (1476KB) ( 6 )   Save The Partial Ordinal Priority Approach （OPA-P） for multi-attribute decision-making （MADM） is introduced in this study. This approach builds upon the Ordinal Priority Approach， with a linear optimization model for MADM weights based on partial order ranking. It utilizes a partial order accumulation transformation to create an adversarial Hasse diagram， portraying comparative advantages and disadvantages among alternatives. OPA-P concurrently derives weights for alternatives， criteria， and experts by incorporating expert preferences. By integrating the adversarial Hasse diagram， it identifies Pareto optimal alternatives and hierarchically classifies them， facilitating optimal selection. To assess the efficacy of the proposed approach， it focuses on evaluating spontaneous combustion hazards in Goaf areas， validating the approach by comparing it against relative research findings. In contrast to conventional MADM approaches， OPA-P leverages more stable and readily available partial order rankings as input data， making it better suited for contexts requiring precise decision-making data. Its outcomes exhibit heightened stability and adeptness in identifying potential Pareto solutions in MADM. Figures and Tables | References | Related Articles | Metrics

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Optimization Model for Judging Large-scale Innovative Competitions Based on Experts’ Weights Determined by Cross-entropy Dongwei Guo, Yingming Zhu, Yulei Chen, Yao Zhang 2025, 33 (12):  134-145.  doi: 10.16381/j.cnki.issn1003-207x.2023.1775 Abstract ( 15 )   HTML ( 0 )   PDF (979KB) ( 4 )   Save The judging of innovative works is somewhat subjective， and ratings may vary considerably from one expert to another. The establishment of a scientific evaluation method for innovative competitions is of great significance in promoting the fairness of competitions and increasing the motivation for innovation. Some of the problems of the current large-scale innovative competition evaluation program are addressed in this paper， including the problem of rational allocation of competition works， the problem of scientific adjustment of abnormal scoring， and the problem of determining expert weights， etc.， and a complete set of evaluation program is established that can effectively reduce the harm of scoring errors.Firstly， a mathematical model of “bundled cross-assignment” of competition entries is developed. On the one hand， the model requires that every two entries be bundled together and distributed to the experts for evaluation， which ensures that each entry has a comparable object so that unreasonable scores can be adjusted. On the other hand， the model requires that there be as much cross-evaluation of works between any two experts as possible， thus ensuring that the workload of each expert is as close as possible， and at the same time， the scoring characteristics of any two experts can be compared and analyzed to provide reliable and objective data for the determination of experts' weights. In addition， a property of the optimal solution of the model is discussed， and a greedy algorithm for solving the local optimal solution is designed. Secondly， according to the principle of majority rule， a simple and practical model for adjusting the scores of a few experts is established based on the ratio of the scores of the majority of experts on the two works. The model can reduce the impact of random errors to a certain extent， and can effectively attenuate the unfairness caused by individual experts' misjudgment and deliberate high or low scores. Thirdly， a cross-entropy-based weighting method is established， which characterizes the experts' judging scores by means of “intrinsic information” and cross-entropy， and then designs a reliable formula for calculating experts' weights. The weights， to some extent， can improve the defect that the premise assumptions of the standardized scoring method may not be valid in the evaluation of large-scale competitions， and at the same time， the weights can further minimize the range of the scores for most of the works. Finally， in order to test the effectiveness of the method proposed in this paper， the mathematical modeling competition works of H university are used as experiments， and the five evaluation schemes are compared and analyzed by four evaluation indexes， namely， ranking difference degree， Spearman’s rank correlation coefficient， experts' scoring error degree， and works' controversy degree， and the results show that our method improves the Spearman’s rank correlation coefficient， works' controversy degree， and experts' scoring error degree and decreases the ranking difference degree， which shows that our method is more scientific and reasonable than others， and is able to make a fairer and more objective evaluation and ranking for the works of the competition.Four avenues for further investigation on this subject matter. （1） The competition entries’ distribution model proposed in this paper is a mathematical model of a class of assignment problems， and further research is required to develop an algorithm for its optimal solution. （2） The adjustments are introduced to the original scores， and the calculation formula can be discussed for transforming these scores （e.g.， standard scores）. （3） In order to address systematic and random errors in expert scoring， it is necessary to explore reasonable methods for assigning weights to experts. （4） Can the scores of t experts for each competition entry be changed into the interval number according to reasonable rules， and then the entries are evaluated and ranked based on the method proposed in literature ［11］. Figures and Tables | References | Related Articles | Metrics

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Max-NPV Multi-project Scheduling Optimization under Dual Resource Constraints Hua He, Zhengwen He, Fangfang Cao, Nengmin Wang 2025, 33 (12):  146-159.  doi: 10.16381/j.cnki.issn1003-207x.2023.0973 Abstract ( 15 )   HTML ( 0 )   PDF (1347KB) ( 6 )   Save In reality， the implementation of multiple projects often involves two types of resources， namely， renewable and non-renewable resources. Renewable resources include labor， machinery， and facilities that are not depleted and can be reused during project execution. Therefore， these resources have a defined availability on a per-unit time. On the other hand， non-renewable resources typically consist of raw materials， components， and funding， which are consumed once they are invested in a single-project and cannot be reused for other projects. Hence， the availability of non-reusable resources is defined for the entire duration of the project. In a multi-project environment， if a renewable resource for a specific project is idle during a certain period， it can be shared with other projects， making it a global resource. Conversely， for non-renewable resources， temporary surpluses in a single project are generally not shared with others， treating them as local resources.It is noteworthy that during the project implementation process， activities generally have multiple execution modes based on the type and quantity of resources invested， leading to different durations and costs. Overall， the choice of activity modes is constrained by the project budget. Project budgets fall under the category of non-renewable resources， with availability defined over the entire project cycle， once invested in the project， they cannot be reused. Therefore， in terms of non-renewable resources， managers need to allocate them reasonably to individual projects to ensure the smooth completion of projects and the ultimate achievement of expected goals.This research delves into the multi-project resource-constraint scheduling problem， specifically tackling the challenge of optimizing net present value （NPV） under dual resource constraints-renewable and non-renewable resources. The objective revolves around allocating non-renewable resources to individual projects， determining activity modes based on the availability of these resources， and subsequently scheduling activity starting times within the confines of shared renewable resource availability. Therefore， managers are tasked with manipulating three sets of decision variables-non-renewable allocation for individual projects， activity modes， and starting times of activities for a single project-to maximize the NPV of multiple projects.In the subsequent sections， a literature review is conducted to demonstrate the theoretical significance of this research. Next， an optimization model for maximizing net present value in multi-project scheduling under dual resource constraints is constructed， and the basic properties of the model are analyzed. Subsequently， a three-tier nested tabu search heuristic algorithm for solving the model is designed， and improvement measures for the algorithm are proposed based on proposed properties. Finally， extensive computational experiments are conducted to validate the model and the algorithm. Results have shown that the three-tier nested tabu search algorithm with improvement measures proves to be the most effective algorithm for solving the problem of maximizing net present value in multi-project scheduling under dual resource constraints. Additionally， sensitivity analysis reveals： the renewable resource factor （RF） has a negative impact on NPV， while RS positively affects NPV. As project deadlines， the number of milestones， intermediate payment ratios， and advance payment ratios increase， project net present value also increases. However， the discount rate negatively influences net present value. An increase in non-renewable resources contributes to the improvement of NPV in multi-projects. Figures and Tables | References | Related Articles | Metrics

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Integrated Recovery of Passenger and Cargo Considering the Belly Space of Passenger Aircraft Yuzhen Hu, Sirui Wang, Jianxia Liu 2025, 33 (12):  160-170.  doi: 10.16381/j.cnki.issn1003-207x.2024.2264 Abstract ( 9 )   HTML ( 0 )   PDF (1677KB) ( 3 )   Save In recent years， the air passenger and cargo transport business in China has surged. Many large civil aviation companies not only carry passenger traffic but also utilize the belly holds of passenger aircraft to transport a significant amount of cargo， with the growth rate of cargo volume far exceeding that of dedicated freighters， making it one of the main modes of air transport. However， abnormal flights caused by weather， maintenance， and other factors can lead to simultaneous disruptions in both passenger and cargo itineraries， resulting in more complex recovery operations for airlines. In response to this， an integrated passenger and cargo flight disruption recovery model is first established， utilizing time-space network technology to depict the transfer networks for aircraft， passengers， and cargo. Furthermore， for the cargo transfer network without transfer limit constraints， disruption-clearing strategies （DCS） and limited generation-augmentation strategies （LGAS） are proposed to enhance solution efficiency. Subsequently， a case study using real data is conducted， comparing the integrated recovery model with a non-integrated model， revealing significant advantages in cost savings for recovery in the proposed integrated model. Finally， the reasons for redundant calculations in cargo recovery are analyzed and the process is optimized using the proposed strategies， finding that DCS can reduce solution time with almost no increase in cost， while LGAS can further improve computational efficiency and has specific usage techniques. Figures and Tables | References | Related Articles | Metrics

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Research on Emergency Blood Delivery Problem Based on Multiple Drones Zhongbao Zhou, Enming Chen, Ruiyang Li, Wenting Sun, Jianmai Shi 2025, 33 (12):  171-184.  doi: 10.16381/j.cnki.issn1003-207x.2023.1557 Abstract ( 15 )   HTML ( 0 )   PDF (2899KB) ( 11 )   Save Blood emergency delivery plays a crucial role in the treatment of wounded patients. The application of drones provides new opportunities for improving blood emergency delivery， yet many challenges， including blood perishability， poor drone range， and blood-to-injury correspondence， encountered in using drones for blood emergency delivery have been overlooked. A novel blood delivery problem is considered， distinct from current relevant research， which effectively avoids the impact of post-disaster road damage on blood emergency delivery and alleviates drone range anxiety while ensuring fairness in blood emergency delivery and the quality of blood transfusions for patients. During the scheduling period， there may be multiple wounded patients in need of blood transfusions in hospitals. Drones carrying blood products depart from temporary blood centers to hospitals for blood emergency delivery. Drones are required to deliver blood to hospitals within agreed-upon times as much as possible. Meanwhile， drones can choose to recharge at hospitals to extend their flight range. After completing all blood delivery tasks， drones must return to the blood center. The cumulative duration is minimized as the objective， considering the perishability of blood， the correspondence between blood and patients， the fairness of blood delivery， and the time cost of drone charging. A mixed-integer programming model is established， and effective inequalities are proposed based on the problem characteristics. Furthermore， based on the problem characteristics and through an adaptive mechanism combining Variable Neighborhood Search （VNS） and Adaptive Neighborhood Search （ANS）， an improved Adaptive Variable Neighborhood Simulated Annealing algorithm （I-AVNS-SA） is designed to solve the model. Finally， extensive computational experiments are conducted using datasets adapted from the Solomon VRPTW benchmark and the dataset of the Turkish earthquake. Experimental results show that， for small-scale instances， the I-AVNS-SA algorithm is comparable to solving the MIP model optimally， with significantly reduced computation time. In large-scale instances， the algorithm outperforms SA， LS， and GA algorithms recently used to solve related problems in terms of objective function value， CPU time， convergence， and stability. Additionally， through sensitivity analysis， scientific reference is provided for whether hospitals are equipped with drone charging facilities and the configuration of drone quantities. In conclusion， the study of the UAV blood delivery problem is refined through a comprehensive consideration of the challenges and the design of efficient models and algorithms. Figures and Tables | References | Related Articles | Metrics

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Adaptive Multivariable Grey Prediction Model Driven by Euler Polynomials and Its Application Yuanping Ding, Yaoguo Dang, Junjie Wang 2025, 33 (12):  185-199.  doi: 10.16381/j.cnki.issn1003-207x.2023.1297 Abstract ( 16 )   HTML ( 0 )   PDF (2664KB) ( 3 )   Save The data of energy environment system under different time scales have different trend characteristics. Aiming at the prediction of data series with linear， nonlinear， or partially linear， partially nonlinear mixed trends， the Euler polynomial with nonlinear time disturbance parameters is constructed to flexibly characterize the complex trends of the data series， and then the adaptive multivariable grey prediction model driven by Euler polynomial is proposed. The difference and discrete forms of the model are derived based on the grey derivative information covering principle and the Euler polynomial mean value formula， and the optimization framework of Euler polynomial order and nonlinear time disturbance parameters based on the grey Wolf optimization algorithm is given. On this basis， the time response formula is solved by mathematical induction. Finally， examples of monthly PM2.5 in Beijing and annual total energy consumption in Jiangsu Province with different trend change characteristics are selected to verify the validity of the model， and then the model is applied to the quarterly electricity consumption forecast of Guangzhou. Meanwhile， the robustness and stability of the new model are verified based on Monte-Carlo simulation analysis and different proportions of training set modeling， indicating that the newly constructed multivariate grey prediction model can adaptively fit and predict data series with different trend change characteristics. Figures and Tables | References | Related Articles | Metrics

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Research on Cost Prediction of Electric Power Equipment Manufacturing Enterprises under Multi Value Chain Collaboration Based on Data Mining Xiaomin Xu, Shipeng Zheng, Zhiyi Wang, Runkun Yao, Luoyun Guan 2025, 33 (12):  200-213.  doi: 10.16381/j.cnki.issn1003-207x.2023.1836 Abstract ( 12 )   HTML ( 0 )   PDF (5576KB) ( 2 )   Save In the context of multi value chain collaboration in power equipment manufacturing enterprises， enterprise cost prediction is influenced by multiple factors such as supply chain， production chain， marketing chain， and service chain. In order to improve the accuracy of cost prediction for power equipment manufacturing enterprises and improve the level of cost management， a cost prediction model is constructed based on data mining technology， which combines firefly algorithm （FA） and sparrow search algorithm （SSA） optimization BP neural network （FA-SSA-BP）. Firstly， using network data mining technology， a database of influencing factors for multi value chain collaboration in power equipment manufacturing enterprises is constructed； Secondly， the Pearson correlation coefficient and grey relational analysis （GRA） method are used to screen the influencing factor library and determine key factors； Then， a FA-SSA-BP cost prediction model is constructed， in which FA-SSA strengthened the global search ability， avoided premature falling into local optima， and improved convergence accuracy. Subsequently， based on the relevant data of the main business Ring Main Unit of Q power equipment manufacturing enterprise， the prediction model constructed in this paper is used for cost prediction and error analysis， and the prediction effect is compared with other optimization models. The results indicate that the model proposed in this paper significantly reduces prediction errors and effectively improves the accuracy of cost prediction compared to the comparative model. Then， sensitivity analysis is conducted on key factors， pointing out the impact of different influencing factors on enterprise costs. Finally， based on the above research， targeted cost management suggestions are proposed for power equipment manufacturing enterprises under multi value chain collaboration. The cost prediction model constructed in this paper and the proposed management suggestions lay a solid foundation for cost management in power equipment manufacturing enterprises under multi value chain collaboration. Figures and Tables | References | Related Articles | Metrics

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Research on Multi-sourcing Procurement Model and Procurement Pricing Decision of Emergency Materials under Dual Reserves of Physical Goods and Production Capacity Lin Zhang, Jun Tian 2025, 33 (12):  214-225.  doi: 10.16381/j.cnki.issn1003-207x.2023.0143 Abstract ( 14 )   HTML ( 0 )   PDF (1145KB) ( 2 )   Save Jointly reserving emergency supplies by government and enterprises is an inevitable measure to improve the government's ability to respond to emergencies. In order to implement emergency material reserve and supply agreement cooperation， the government and enterprises need to jointly solve some practical problems. On the one hand， for the government， enterprises participating in emergency material supply agreement cooperation will face risks and cost pressures brought about by demand uncertainty. Correspondingly， guiding enterprises to participate in agreement cooperation has become the primary issue that the government needs to solve. Furthermore， in order to ensure the supply capacity of emergency supplies， government emergency management departments usually establish （or need to establish） cooperative relationships with multiple enterprises. Correspondingly， how to establish cooperative relationships with numerous enterprises in an efficient and fair way has become a key challenge that the government urgently needs to solve.On the other hand， for agreement enterprises， participating in emergency material supply agreements means facing uncertainty in the demand for emergency materials. Correspondingly， how much and in what form （physical products and production capacity） to reserve will inevitably become important decisions for enterprises to reduce the risks and increase the benefits of agreement cooperation. These decisions will also have an impact on the cost and benefits of the government authority when establishing the social emergency material reserve capacity.Aiming to address the above-mentioned issues， a cooperation model between the government and several emergency material production enterprises is built based on quantity flexibility contract. Government flexible procurement pricing decisions are analyzed under the situation that enterprises can reduce the agreement cooperation cost by coordinating the physical reserves and the production capacity reserves. At the same time， the enterprises’ flexible supply quantity decisions and the physical reserve quantity decisions are studied. Based on the Stackelberg game led by the government authority， the enterprises’ decisions are first analyzed， followed by the government’s decisions. Numerical examples are also used to find more properties.Several key findings are proposed. First， under the principle of cooperative fairness， with the increase of the government's regular reserves quantity， the government will alternately increase the level of flexible procurement prices for different enterprises. Second， enterprises need to make decisions based on their production cost/efficiency level among all agreement partners from a system perspective. Third， even if the reserve materials are highly likely to be purchased by the government， the agreement enterprises should actively use the production capacity reserve method instead of maintaining excessive physical reserves.From the theoretical perspective， it enriches the research results of emergency material procurement management from the perspectives of the number of contracted enterprises， the diversity of reserve types and the fairness of cooperation. From the practical perspective， theory-based decisions and managerial insights can be provided for the government authority to economically increase the social reserve capacity from a broader perspective. At the meantime， the enterprises can also learn how to makedecision for decreasing the risks and costs when joining in the social reserve system. Figures and Tables | References | Related Articles | Metrics

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Condition-based Maintenance Model for a System with Hidden Failures Based on Competing Failures and Mission Failures Mengying Han, Li Yang, Qingan Qiu, Fengming Kang 2025, 33 (12):  226-238.  doi: 10.16381/j.cnki.issn1003-207x.2024.1568 Abstract ( 15 )   HTML ( 0 )   PDF (2282KB) ( 4 )   Save Systems with hidden failures typically do not perform missions continuously， but mission failures in such systems can lead to substantial economic losses and severe damage. Condition-based maintenance （CBM） is an effective strategy to enhance system reliability and mitigate the risk of hidden failure. A CBM model is developed for systems subject to competing and hidden failures by incorporating mission failure behavior and a multiple inspection policy. The arrival of system missions follows a Poisson process， and the outcome of mission execution depends on the system's state. Periodic minor inspections， which are imperfect， are scheduled to detect the system’s state， while major inspections， which are perfect， are conducted only upon mission failure. Under this policy， all the possible scenarios for minor inspection preventive renewal， major inspection preventive renewal， minor inspection corrective renewal， major inspection corrective renewal， and age-based preventive renewal are analyzed， and the probabilities of each scenario occurring are derived. Consequently， the CBM model is formulated using the Renewal Reward Theorem， aiming to minimize the long-run expected cost rate， with the minor inspection interval serving as the decision variable. Finally， the effectiveness and applicability of the proposed maintenance model are validated through comparisons with two other models. The results indicate that considering mission failure due to defective systems is crucial when constructing maintenance models for multi-state systems with hidden failures， and that multiple inspection policy offers greater cost advantages than single inspection policy when the minor inspection cost is below a threshold level. Figures and Tables | References | Related Articles | Metrics

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Market Optimization Strategy for Remanufactured Products Considering Insurance Claims Model and Government Subsidies Limin Zhang, Kai Li, Tao Zhou 2025, 33 (12):  239-252.  doi: 10.16381/j.cnki.issn1003-207x.2023.0455 Abstract ( 13 )   HTML ( 0 )   PDF (2343KB) ( 4 )   Save To stimulate the market vitality of remanufactured products， the government and enterprises are implementing an innovative model of “insurance+remanufacturing”. Insurance claims patterns and remanufacturing rate concession decisions by insurance companies significantly impact the promotion of remanufactured products. A critical issue to explore is optimizing the market for remanufactured products by setting up a reasonable insurance claim model， remanufacturing rate concessions， and government subsidies. Considering two claims models of insurers， dynamic game models are constructed to investigate the effects of insurance claims models， remanufacturing rate discounts， and government subsidy strategies on the equilibrium decisions， demand， and profits of closed-loop supply chain stakeholders. The results show that：The insurer’s operating model of only incorporating remanufactured parts into the repair parts system is not always the optimal way to promote remanufactured parts. When the insurance claim rate is higher than a certain threshold， the insurer can promote more remanufactured parts by incorporating both new and remanufactured parts in the repair parts system. Government subsidies can increase the profit of the remanufacturer， and subsidizing consumers who use remanufactured parts is more beneficial than subsidizing the remanufacturer， but it is also more damaging to the manufacturer’s profit. However， the impact of government subsidies on the insurer’s profits also depends on the subsidy amount. Specifically， government subsidies can increase the insurer’s profit when the subsidy amount is large， and subsidizing consumers who use remanufactured parts can increase the profit more than subsidizing the remanufacturer， but government subsidies will hurt the insurer’s profit when the subsidy amount is small. Finally， numerical experiments are carried out to verify this paper's important conclusions and inferences. The findings of this paper will not only provide management insights for manufacturers/remanufacturers and insurers， but will also provide decision support to governments in developing their subsidy strategies. Figures and Tables | References | Related Articles | Metrics

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Dynamic Pricing with Consumer Loss Aversion and Online Reviews Xiao Wang, Chaolin Yang 2025, 33 (12):  253-263.  doi: 10.16381/j.cnki.issn1003-207x.2023.0375 Abstract ( 13 )   HTML ( 0 )   PDF (1229KB) ( 2 )   Save Consumers are generally loss averse and are often reluctant to purchase emerging goods of uncertain quality. Before purchasing a good， consumers may browse the rating information displayed on the review platform to form a clearer perception of the quality of the good， mitigating the impact of loss aversion. Then， after purchase and use， consumers may return to the review platform to give their ratings. A multi-stage dynamic pricing model is constructed for a monopoly firm under the online review scenario that considers consumers' loss aversion. The firm maximizes its revenue by dynamically adjusting its price to balance earning higher current revenue and accumulating rating information more quickly. Due to the complex inter-period impacts of pricing and the stochastic and non-linear properties of the evolution process of the state variables， the exact optimal strategy for the problem is difficult to find. Therefore， a deterministic treatment based on the fluid approximation method is carried out， from which a pricing strategy with asymptotic optimality is found. Furthermore， the impact of consumer loss aversion on pricing is discussed. Finally， the effectiveness of the policy is verified through numerical experiments based on real data from Amazon. The experimental results find that the dynamic price strategy can enhance revenue by about 10% compared to the fixed price strategy. On the basis of the dynamic price strategy， further adding the portrayal and treatment of consumer loss aversion can further enhance revenue by about 1%. In addition， the dynamic fluid matching strategy proposed in this paper has the form of a semi-closed solution and thus has the advantage of high computational efficiency. It contributes both in theory and application. In theory， it is proved that the pricing strategy based on the fluid approximation method is asymptotically optimal. In terms of application， the results of this paper can provide guiding suggestions on pricing for related firms. Figures and Tables | References | Related Articles | Metrics

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Order Decision and Supply Chain Coordination Considering Demand Uncertainty and Return Heterogeneity under the BOPS Mode Shouyu Ma, Yanqing Liang, Feng Xiong, Penghui Lou 2025, 33 (12):  264-273.  doi: 10.16381/j.cnki.issn1003-207x.2023.0522 Abstract ( 10 )   HTML ( 0 )   PDF (846KB) ( 4 )   Save The emergence of omni-channel new retailing such as BOPS has played an important role in reducing online and offline channel conflicts， promoting channel integration and enhancing consumers’ shopping experience， and has been widely used in fast fashion and other industries. Considering mixed procurement channels composed of traditional procurement and drop-shipping， the heterogeneity of multi-channel returns and the randomness of consumer demand， tthe retailer’s optimal ordering decision and the coordination of supply chain contract under the BOPS mode are analyzed.Based on the game theory， the onmi-channel ordering decision newsboy model under the BOPS mode is established with the goal of maximizing profits. The centralized decision-making is taken as the benchmark of the supply chain optimal strategy. The analysis results show that the retailer’s expected profit is a concave function about the order quantity， and there is a unique extreme value to maximize the retailer’s expected profit. The return rate of different channels has significant differences on the retailer’s optimal order quantity. Then the coordination effect of buy-back contract and revenue sharing contract on decentralized supply chain is investigated. The findings indicate that neither the buy-back contract nor the revenue sharing contract can achieve supply chain coordination in BOPS mode due to the problem of return. Therefore， a new threshold buy-back revenue sharing contract is proposed to coordinate the supply chain. The analysis shows that the threshold buy-back revenue sharing contract of joint buy-back and revenue sharing can achieve supply chain coordination and Pareto improvement， and reach the global optimization of the supply chain.It can further enrich and improve the research theory of omni-channel field， and provide theoretical guidance for enterprises’ operational decision-making and supply chain collaborative operation under the BOPS mode. Figures and Tables | References | Related Articles | Metrics

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Internal Financing Decision of Contract Farming Supply Chain Under Government Subsidy Lin Li, Yongkang Lu 2025, 33 (12):  274-284.  doi: 10.16381/j.cnki.issn1003-207x.2022.0183 Abstract ( 12 )   HTML ( 0 )   PDF (1320KB) ( 3 )   Save Contract farming supply chain is one of the popular research areas in recent years， which can use different financial instruments to fundamentally relieve the financial pressure of agricultural production， and ensure the balance of supply and demand in the market. However， the risk-averse characteristic of farmers curbs their willingness to finance when they face with financing and interest rates. In order to relieve the pressure on farmers' interest rates and increase farmers' willingness to finance， the government has been implementing some subsidy policies. Based on this， this paper considers a contract farming supply chain consisting of a company， a farmer and the government. In this model， the company signs a contract with the farmer， agreeing to purchase all of the farmer's agricultural products at the end of the production period. At the same time， the company provides the farmer who is limited capital production financing aimed to help the farmer purchase agricultural materials such as seeds and fertilizers. In addition， the government provides interest subsidy at the end of the production period to relieve the pressure on the farmer's interest rates （i.e.， a subsidy is paid to the farmer based on the amount of the financing after the production period）. Different from the company's perspective in many literatures， this paper aims to explore how government influences the quantity planted by farmer and purchasing price of company to improve the supply chain and social welfare by formulating loan subsidies in the “company + farmer” mode of contract farming. And different from the simple assumption about farmers' initial capital in many literatures on supply chain financing， this paper classifies farmers based on the amount of their own capital. Specifically， three decision models are developed： no financing model （NF）， financing model without government subsidy （FNS）， and financing model with government subsidy （FS）， in which production quantity， purchasing price， financing ratio， and government subsidy discount ratio are considered as decision variables. Given different own capital scenarios， the decisions made by the different participants are compared， and a differentiated government subsidy policy is proposed. The main findings include：（1） government subsidy can improve social welfare， but the effects are affected by the amount of farmer's capital， which implies the necessity of setting differential subsidy ratio based on classification of production funds； （2） government subsidy is more significant for the first and second type of farmers to increase production and income and improve social welfare， but it is not suitable for the third type of farmers； （3） for the second type of farmers， the company would suppress the purchase price to limit the profit growth of farmers and capture the benefits of the subsidy. Based on this， an improved government subsidy solution integrating the discount subsidy and a minimum purchasing price strategy is proposed. The range of key purchasing price parameters was offered， which can help the government adjust the profit proportion between company and farmer， and further guarantee the stability and development of the contract farming supply chain. This paper provides a decision reference for the government to formulate diversified and targeted agricultural policies. Figures and Tables | References | Related Articles | Metrics

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Balanced Purchasing Strategy Based on Information Endowment Difference under Dual Supply and Demand Relationship Xinjun Li, Li Wang 2025, 33 (12):  285-293.  doi: 10.16381/j.cnki.issn1003-207x.2023.0028 Abstract ( 10 )   HTML ( 0 )   PDF (1604KB) ( 2 )   Save From the perspective of information ownership and the way of competition in the market. The two-tier supply chain consisting of the integrator of production and marketing， retailers and external supply markets. Retailers have private information of market demand and use order quantities to transmit information. Based on the Lexicographical Maximum Sequential Equilibrium， the integrator of production and marketing analyzes the unique equilibrium and formulates the optimal equilibrium wholesale price to get the unique equilibrium solution. The results show that when the information is shared among supply chains， the integrator of production and marketing and retailers with cooperative competition use signal transmission to make strategic game decisions， under the LMSE criterion， the only separation equilibrium exists when the wholesale price is lower than the threshold value， and the only pooling equilibrium exists when the wholesale price is higher than the threshold value， the uncertainty of demand and the intensity of external competition are the main factors affecting the formulation of equilibrium wholesale price. When the external competition is intense， retailers gain more when the demand uncertainty is high， while the income of the integrator of production and marketing is the opposite； When external competition is not intense， retailers use private information to obtain lower wholesale prices and thus higher profits， while the profits of the integrator of production and marketing increases under the condition of high demand uncertainty. In addition， the value of market demand information increases with the increase of demand uncertainty. Under the separation equilibrium， when the market fluctuation is higher than the threshold value， the profit of the integrator of production and marketing increases with the wholesale price.The wholesale price is set close to the wholesale price under the pooling equilibrium if the integrator of production and marketing chooses the separate equilibrium； If the integrated of production and marketing chooses a pooling equilibrium， the wholesale price is set close to the price offered in the external supply markets. When the demand uncertainty is less than a certain range， separate equilibrium is used when the external competition intensity is relatively high， and pooling equilibrium is used when the external competition intensity is low. When the demand uncertainty is greater than a certain range， only pooling equilibrium can be adopted.This study can be further expanded by， for example， considering cost， other external factors， and risk preference， and exploring the effect of Bertrand-price competition on the profits of both parties； In addition， how other contracts affect the model can be studied. Figures and Tables | References | Related Articles | Metrics

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E-Closed-Loop Supply Chain Decision Making Considering Trading Modes and Power Structures Fuan Zhang, Hongxin Zhang, Qingli Da 2025, 33 (12):  294-305.  doi: 10.16381/j.cnki.issn1003-207x.2023.1578 Abstract ( 13 )   HTML ( 0 )   PDF (1992KB) ( 5 )   Save With the development of mobile Internet technology and the popularization of e-commerce， more and more manufacturers choose to cooperate with online platforms to carry out sales and recycling business， thus forming an E-Closed Loop Supply Chain （E-CLSC）. However， in the process of cooperation between manufacturers and platforms， the diversity and complexity of transaction modes and channel power structures have an important impact on the pricing decisions of the supply chain. Meanwhile， with the wide application of carbon trading mechanism， it is also important to deeply analyze the role of carbon trading mechanism on E-CLSC.In view of this， both sales and recycling services are considered as endogenous variables in the context of the carbon trading mechanism， two transaction modes and three channel power structures are considered， six game models are constructed and the equilibrium results are derived by solving them respectively. Firstly， the role of carbon trading mechanism is analyzed in depth， then the equilibrium results concerning different channel power structures are compared and analyzed， and finally the two trading models are discussed through a set of numerical values.The results show that （1） the implementation of carbon trading mechanism is effective in controlling carbon emissions， but supply chain members should also pay attention to the impact of carbon price fluctuations on pricing and service decisions， specifically， the increase in carbon price will lead to an increase in retail and recycling prices， and the impact on the level of service is closely related to the transaction modes and the power structure of the channel. （2） E-CLSC optimal pricing and service decisions are significantly affected by differences in transaction modes and channel power structures. In the self-operated model， the Nash gaming supply chain has the lowest retail price， the highest recycling price， and the highest forward and reverse channel service level； while in the agency model， the platform-dominated supply chain has the lowest retail price， the highest recycling price， and the lowest forward and reverse channel service level. （3） From an economic point of view， members themselves tend to bring more revenue to themselves when they act as dominant players， and the choice of transaction mode depends on the level of commission. （4） From an environmental point of view， the dominant supply chain with the lowest carbon emissions is significantly affected by differences in transaction modes. Figures and Tables | References | Related Articles | Metrics

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Cooperation Policy Selection and Pareto Improvement Analysis of the Green Substitutable Product Supply Chain Kebing Chen, Yingqi Kong, Di Xiao, Dong Lei 2025, 33 (12):  306-317.  doi: 10.16381/j.cnki.issn1003-207x.2022.1936 Abstract ( 11 )   HTML ( 0 )   PDF (1925KB) ( 3 )   Save The rapid development of economy， society and cities is leading to environmental pollution and shortage of natural resources worldwide， and it also makes the issue of green environmental protection the focus of attention. Now that the issue of green development has received extensive attention from all parties， how to conduct green supply chain cooperation has also become the focus of business management research. Motivated by these actual problems， a secondary supply chain composed of one traditional products manufacturer， one green manufacturer and one retailer is constructed. Both manufacturers sell their products through the retailer. There is substitutability between these two products.As a member of the supply chain， each manufacturer can trade with the retailer through the wholesale price contracts， or cooperation with the retailer based on the revenue sharing. In addition， for the green manufacturer， besides of adopting a revenue-sharing contract， he can also choose to cooperate with the retailer to share green manufacturing costs to maximize his profit. Therefore， the following three possible cooperation modes are discussed in this paper： （i） the traditional manufacturer chooses to cooperate with the retailer based on the revenue sharing contract， and the green manufacturer doesn’t cooperate with the retailer； （ii） the traditional manufacturer doesn’t cooperate， and green manufacturers choose to cooperate with the retailer based on revenue sharing and greening cost sharing； （iii） the traditional manufacturer chooses to cooperate with the retailer based on revenue sharing， and the green manufacturer and the retailer carry out revenue sharing and green production cost sharing cooperation. Based on this， the optimal decision-makings and the profits of supply chain members under different cooperation modes are studied， and through comparative analysis， which cooperation strategy the supply chain members should choose is explored to be most profitable under the green and substitutability supply chain. Finally， supply chain coordination under the three cooperation strategies is discussed.The main insights obtained from this article are as follows. First， under the three cooperation models， the retail/wholesale prices of both green and traditional products are directly proportional to the consumer’s green sensitivity coefficient. Second， under the circumstance of manufacturer-led， the revenue-sharing cooperation with the retailer has a positive effect on the green manufacturer to improve the greenness of products； the cost-sharing cooperation between the green manufacturer and the retailer will also help them improve the greenness of green products. The finding shows that the greater the proportion of green manufacturing costs borne by the retailer， the higher the green level that green products can achieve. Besides， under these three cooperation models， the optimal profit that each manufacturer can obtain when only traditional manufacturer cooperates with the retailer is the smallest， indicating that green manufacturer should actively seek to share revenue and green production cost to increase their own profits； and the optimal profit of each manufacturer is proportional to the proportion of the green manufacturing cost borne by the retailer. It can be seen that the green manufacturer can increase the manufacturer’s profitability by cooperating with the retailer. However， the retailer only has a poor improvement effect and obtains lower profits when only cooperating with the manufacturer producing the traditional product， and can obtain higher profit by cooperating with both manufacturers. Finally， from the perspective of the whole supply chain， the coordination of supply chain can be realized only when both manufacturers cooperate with the retailer. It is also shown that Pareto improvement can be achieved between channel members when the green cost sharing ratio is in a certain range. Figures and Tables | References | Related Articles | Metrics

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Selling Strategy Choices in the Presence of a Discount Factor and Sales Promotion at the Advance Period Zhenkai Lou, Zhiying Wang, Huimin Zhang, Jinhui Pang 2025, 33 (12):  318-327.  doi: 10.16381/j.cnki.issn1003-207x.2022.1373 Abstract ( 12 )   HTML ( 0 )   PDF (859KB) ( 7 )   Save In practice， the term advance sale mainly refers to a marketing practice in which the seller offers buyers opportunities to make purchases before the time of consumption. Advance sale endows a retailer with the opportunity for recovery cost and earning profit as soon as possible， and lowers the inventory risks. For a retailer with financial stress， one main target is to sell out her products early， which may be achieved by advance selling. From this perspective， a same amount of profit earned at the advance-selling period is of more value than that earned at the spot-selling period. In order to acquire demand information and recoup the cost， more and more retailers adopt the advance-selling strategy and make promotion at the advance-selling period. A discount factor is brought in in this research to measure profits earned at different periods. The protagonist of this paper is a retailer who arranges a two-period sale and respectively prices her produces at different periods. Sales prices of the two periods are declared once the advance-selling period begins， which implies the retailer is able to regulate and control the demand of each sale period by setting different prices. In order to counteract the shortcomings of future goods sold in the advance-selling period， sales promotion may be adopted at this period. According to whether to make promotion or not in the advance-selling period， the discussed topic is grouped into two categories， and examine six selling strategies. By analyzing the achieved results， the understanding is formalized and several propositions are shown. Some meaningful conclusions are drawn through these propositions： （a） it is shown that “advance sale + spot sale” is always optimal when sales promotion is not adopted； and （b） “advance sale + spot sale” mode under sales promotion made in the advance-selling period cannot be optimal when the strain level of the retailer’s finance is low. Numerical examples are designed to analyze the sensitivity of each crucial parameter and examine the change of a retailer’s selling strategy. Figures and Tables | References | Related Articles | Metrics

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Strategic Inventory Decisions of a Retailer in a Supply Chain with Learning Effect Fen Lu, Wanqi Lv, Xiaochang Wu, Jizi Li, Yong Wang 2025, 33 (12):  328-336.  doi: 10.16381/j.cnki.issn1003-207x.2022.2797 Abstract ( 15 )   HTML ( 0 )   PDF (817KB) ( 7 )   Save The learning effect refers to the phenomenon where production costs decrease with time as a result of repeated and accumulated production activities. Considering the significant role of production quantity on the learning effect， the additional products in the supply chain may be ordered or produced--which are known as strategic inventory. In order to accumulate production experience， the manufacturer may reduce the wholesale price to stimulate the retailer to order more and the retailer may hold the strategic inventory， moreover， to bargain with the manufacturer in the future. Therefore， it is necessary to study the retailer’s strategic inventory behavior in a supply chain with learning effect， which raises the following questions： What are the firms’ strategic inventory holding and order decisions under the existence of the learning effect？ How do the learning rate and strategic inventory affect the equilibrium decisions and profits of supply chain members？ Does the learning effect change the role of strategic inventory？To address these questions， a two-period Stackelberg game supply chain model consisting of a manufacturer with learning effect as the leader and a retailer who may strategically hold inventory as the follower is established. The trend of the firm’s prices and profits is explored over time. It also investigates how strategic inventory and the learning effect affect decisions and profits. The profits of the firms with strategic inventory and those without strategic inventory has been compared， and the impacts of the learning effect on strategic inventory are examined.By examining the impact of key factors such as the learning effect and the strategic inventory on the supply chain equilibrium decisions， it is observed that the retailer holds strategic inventory when the inventory holding cost is reasonable. However， the retailer can only benefit from his own strategic inventory behavior only when the inventory holding cost is low， and the retailer adopts a loss strategy in the first period to maximize the overall profit. The enhancement of the learning effect hampers the retailer's strategic inventory when both the learning rate and inventory holding cost are low. These conclusions can provide theoretical guidance for the future research on strategic inventory and learning effects. Furthermore， firms can also gain practical experience from our study. Figures and Tables | References | Related Articles | Metrics

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Study on the Strategic Selection of Blockchain Implementation Entities in Closed-loop Supply Chain Yuyan Wang, Junhong Gao, Qiaoyun Yun 2025, 33 (12):  337-348.  doi: 10.16381/j.cnki.issn1003-207x.2025.0427 Abstract ( 19 )   HTML ( 0 )   PDF (1984KB) ( 16 )   Save A recycling quantity discount factor is introduced and consumer preference is incorporated for blockchain-enabled traceability of remanufactured products. It constructs a closed-loop supply chain （CLSC） system comprising a manufacturer and a retailer. Four blockchain implementation modes are systematically analyzed： 1） no blockchain， 2） manufacturer-only implementation， 3） retailer-only implementation， and 4） full-chain implementation. Utilizing a Stackelberg game framework， the impact of each mode on optimal pricing， blockchain traceability levels， recycling rates， market demand， supply chain member profits， and social welfare is investigated. Key findings indicate： （1） Contrary to the full-chain mode， retailer-only implementation yields higher traceability levels， recycling rates， market demand， and social welfare. This stems from retailers' direct consumer access， enabling more effective capture of blockchain's trust premium while avoiding manufacturer underinvestment due to high costs； （2） Under low blockchain cost efficiency， full-chain implementation induces the highest product pricing due to cost pressures， potentially eroding price competitiveness. Under high cost efficiency， retailer-only implementation commands higher prices， demonstrating superior ability to monetize traceability advantages； （3） At intermediate manufacturer cost-sharing ratios， retailer-only blockchain implementation emerges as the unique Pareto-optimal equilibrium for the CLSC， achieving a manufacturer-retailer win-win outcome. Figures and Tables | References | Related Articles | Metrics

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Omni-channel Policies for a Retailer with Buy-online-and-pickup-or-return-in-store Based on Inventory Control Ju Zhao, Ye Yuan, Zonghong Cao, Jie Min 2025, 33 (12):  349-359.  doi: 10.16381/j.cnki.issn1003-207x.2023.0774 Abstract ( 14 )   HTML ( 0 )   PDF (2087KB) ( 5 )   Save With the rapid development of e-commerce， retailers are continuously broadening their distribution channels and evolving their retail models towards omni-channel retail. Among the various omni-channel strategies， the buy-online-and-pickup-or-return-in-store （BOPRS） approach is the most commonly utilized. The BOPRS strategy facilitates information disclosure， consumers enabling to experience and check if the products fit their needs， thus allowing them to access real-time inventory information at offline stores. Considering the information disclosure role of BOPRS， consumers’ channel-switching behaviors and their effects on retailers’ offline inventory under the BOPRS strategy are examined. The main research question concerns the condition under which the retailer can benefit from implementing BOPRS strategy. To address these questions， newsvendor models with and without the BOPRS strategy are developed， consumers’ channel selection behaviors are analyzed， the effects of the BOPRS strategy on the retailer’ offline inventory level and profit are studied， and the conditions are identified under which retailers should implement the BOPRS strategy. The results show that the BOPRS strategy has altered the optimal inventory levels and product availability for offline stores. Only when the product matching rate is high and the hassle cost of online shopping is low will the optimal inventory of offline stores remain unchanged. Otherwise， the optimal inventory of offline stores will increase. Additionally， when the marginal profit of retailers selling products through offline stores is higher than that of online channels， the optimal inventory of offline stores will increase with the matching rate， and the level of product availability will always increase. Contrary to our instinct， implementing the BOPRS strategy by retailers is not always beneficial. When the matching rate of the product is high and the hassle cost of online shopping is not too high， it is advantageous for retailers to implement the BOPRS strategy. Moreover， the retailer can also make more profits by choosing products with a higher matching rate （or lower hassle cost of online shopping） to implement the BOPRS strategy. Figures and Tables | References | Related Articles | Metrics

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Research on Value Creation Mechanism of Service Innovation Empowered by Digitalization in Manufacturing Enterprises Jianqiang Luo, Yueming Hu 2025, 33 (12):  360-368.  doi: 10.16381/j.cnki.issn1003-207x.2023.0415 Abstract ( 20 )   HTML ( 0 )   PDF (1377KB) ( 14 )   Save As an advanced manufacturing model， service-oriented manufacturing is an important way for China's manufacturing industry to climb towards the high-end of the value chain. It has distinct innovative characteristics， specifically reflected in product and service innovation. Initially， product innovation could bring higher excess profits for manufacturing enterprises. However， due to technological diffusion and product homogeneity， the value created by given product technology gradually weakens， making service innovation an important way to expand the value space. In order to enhance the barriers to entry for services， service innovation supported by product technology internally and empowered by digitalization externally is still necessary. Although practical experience of manufacturing enterprises shows that service innovation empowered by digitalization can achieve value creation expansion， the relationship between digitalization empowerment， product technology and service innovation is complex， and the value creation process of service innovation empowered by digitalization is not clear.To address this issue， aiming to maximize the value creation of service innovation， within the given product technology lifecycle， a dynamic model of service innovation value creation empowered by digitalization is constructed. By solving the model， this article analyzes the impact of customer demands and digitalization empowerment on the value creation of service innovation， which reveals the mechanisms of service innovation value creation empowered by digitalization. By comparing different parameter values， numerical simulation is applied to verify conclusions of the model， as well as clarify the relationships between product technology and service innovation in the process of service innovation value creation empowered by digitalization.The research findings show that， firstly， the value creation of service innovation in manufacturing enterprises is the result of joint action， where enterprises actively respond to market service demand and pay attention to the supportive effect of product technology on service innovation. Secondly， the value creation of service innovation empowered by digitalization in manufacturing enterprises has a critical threshold. Thirdly， the level of service innovation empowered by digitalization will exhibits a feature of “transcending”. The research findings expand the perspective of service innovation value creation， enrich the research of the intrinsic relationship between product technology and service innovation in the digitalization context， and guide the development of service innovation as well as value creation expansion in manufacturing enterprises. Figures and Tables | References | Related Articles | Metrics