Table of Content

25 August 2025, Volume 33 Issue 8 Previous Issue   

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Extreme Risk Spillover among Global Stock Markets Based on Transformer-LSTM Quantile Regression Yinhong Yao, Xiaoxu Wang, Wei Chen, Zhensong Chen 2025, 33 (8):  1-13.  doi: 10.16381/j.cnki.issn1003-207x.2024.1601 Abstract ( 31 )   HTML ( 0 )   PDF (7035KB) ( 24 )   Save The increasing global economic uncertainty and the frequent occurrence of extreme events have made the precise measurement of extreme risk spillover effects in global stock markets a crucial approach for addressing cross-border financial shocks. Existing studies exhibit certain limitations in comprehensively considering the nonlinearities， long-term dependencies， and multivariable interactive effects of time series. Therefore， a Transformer-LSTM quantile regression model is proposed that leverages the multi-head attention mechanism in the Transformer to process multiple attention mechanisms in parallel， while extracting the temporal characteristics of the data. This approach aims to more accurately capture the temporal evolution of extreme risks in global stock markets and examine risk spillover effects during the full sample period and crisis periods such as a financial crisis through constructing spillover networks. Based on empirical results from weekly stock index data of 19 countries from December 2001 to March 2024， the findings are as follows： （i） The proposed model demonstrates superior predictive power compared to the Multilayer Perceptron （MLP）， Long Short-Term Memory （LSTM） network， and Transformer models. （ii） The spillover effects in cross-country stock markets exhibit asymmetry over the full sample period. Notably， there is a significant risk spillover effect in the U.S. stock market， while Chinese stock market shows no obvious risk spillover or receiving effects. （iii） During crisis events， extreme risk spillovers increase and asymmetry intensifies. During the financial crisis， the risk spillover effects from the U.S. are significant， with notable bidirectional spillover across multiple countries’ stock markets. During the European debt crisis， risk spillover effects are primarily concentrated in European countries’ stock markets. The risk impact from the U.S. stock market on China notably strengthens during the Sino-US trade friction. During the COVID-19 pandemic， stock markets of developed countries such as the U.S. and the U.K. remain the main sources of risk spillover. The proposed model offers new insights into capturing the extreme risk spillover in financial markets， which is important for risk management in global stock markets during times of crisis. Figures and Tables | References | Related Articles | Metrics

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Constrained Optimal Risk Sensitive Execution Problem with Stochastic Market Depth Weiping Wu, Yu Lin, Chengneng Jin, Zhenpeng Tang 2025, 33 (8):  14-25.  doi: 10.16381/j.cnki.issn1003-207x.2023.1408 Abstract ( 19 )   HTML ( 0 )   PDF (3455KB) ( 12 )   Save As an investment decision-making problem， the optimal execution problem is a topic of considerable interest among finance scholars and professionals. Simultaneously， these realistic factors—stochastic market liquidity， investors' aversion to execution risk， and regulatory restrictions on trading behavior—all significantly influence the optimal trading strategies. Therefore， building on the assumption of stochastic market depth， the optimal execution problem with trading constraints encountered by investors exhibiting constant absolute risk aversion （CARA） utility is investigated. Following the market dynamics of the limit order book （LOB）， an optimal execution model is constructed， taking into account risk management and trading constraints. Subsequently， an analytic execution strategy is presented using the dynamic programming approach. The results show that rational investors avoid placing orders in opposite directions simultaneously， and the optimal strategy is characterized by a piecewise linear function of the remaining order quantity. The numerical examples suggest risk-averse investors tend to execute large orders early in the trading period to avoid risk， compared to risk-neutral investors. Furthermore， aside from effectively managing the risk of asset price changes and stochastic fluctuations in liquidity， this model has also proven its capability to enhance the effectiveness of execution strategies. Additionally， market depth and trading constraints play significant roles in influencing both execution risk and the effectiveness of the strategy. In summary， the results emphasize the importance of accounting for both stochastic market liquidity and trading constraints when addressing the optimal execution problem for risk-averse investors. Figures and Tables | References | Related Articles | Metrics

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Strategic Trading and Market Quality under Ambiguous Volatility Jingzhou Yan, Zhongfei Li, Jie Mao, Xingyi Li 2025, 33 (8):  26-36.  doi: 10.16381/j.cnki.issn1003-207x.2022.2628 Abstract ( 14 )   HTML ( 0 )   PDF (2437KB) ( 4 )   Save Since the outbreak of COVID-19， financial markets worldwide have experienced frequent significant fluctuations， occasionally triggering circuit breaker mechanisms， leading to numerous instances of stocks plummeting. The vast changes in market volatility and its uncertain distribution have made it challenging for investors to accurately predict market dynamics， a phenomenon referred to as ambiguous volatility. Consequently， how ambiguous volatility affects market liquidity is asked. How does it impact investors' trading intensity， expected wealth， and trading volume？ These questions are not only important but also intriguing for policymakers and investors alike. In light of this， a continuous-time market microstructure model incorporating ambiguous volatility within a strategic trading framework is constructed and stochastic optimal control theory is applied to solve the corresponding Hamilton-Jacobi-Bellman-Isaacs （HJBI） equation， thereby explicitly revealing the impact of ambiguous volatility on informed traders' trading strategies and market quality. The findings indicate that an increase in ambiguous volatility leads to a decrease in the optimal trading intensity of informed traders， worsened market liquidity， a reduction in the expected total trading volume of risky assets， and a decrease in informed traders' expected wealth and overall profits. These effects contrast sharply with those observed under constant volatility. It not only deepens our existing understanding of the differences between ambiguous and constant volatility but also provides theoretical guidance for designing market mechanisms under conditions of uncertain volatility in this paper. It emphasizes the importance of reducing market ambiguity to maintain normal and healthy market operations and to prevent systemic financial risks， highlighting the need for further empirical research to accurately measure ambiguous volatility. Figures and Tables | References | Related Articles | Metrics

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Multidimensional Spillover of International Emerging Assets and Chinese Traditional Assets: Based on the Quantile VAR Network Xiong Wang, Jingyao Li, Xiaohang Ren, Zongrun Wang 2025, 33 (8):  37-49.  doi: 10.16381/j.cnki.issn1003-207x.2023.0327 Abstract ( 16 )   HTML ( 0 )   PDF (4538KB) ( 5 )   Save Financial market risks in the context of economic globalization have taken on new characteristics of cross-regional spillovers and cross-contagion. An in-depth examination of multidimensional risk spillovers between international emerging assets （fintech， bitcoin and green bonds） and domestic traditional assets （stock market， crude oil， commodity and metal markets） under different market conditions is provided based on the GARCHSK model and the quantile VAR network framework. Firstly， the static results suggest that spillovers between assets are asymmetric， with spillovers generally higher at the extreme quartiles than at the middle quartile， and significant differences in left- and right-tail spillovers across dimensions. Secondly， rolling window analysis shows that asset price returns， volatility， skewness and kurtosis spillovers all exhibit significant time-varying characteristics， and that the pattern of such spillover relationships over time is more driven by external shocks. Finally， through network structure analysis， it is found heterogeneity in the structural characteristics， direction of contagion， intensity of action and risk centres of the targeted spillover network across financial assets at different moments and at different quantiles. Overall， the findings of this paper contribute to further understanding of the multidimensional spillover relationships and risk contagion paths of domestic and foreign financial assets， and have important implications for investors in constructing diversified asset portfolios and policymakers in strengthening macroprudential regulation. Figures and Tables | References | Related Articles | Metrics

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Pyramid Structure and Labor Income Share Jinli Xiao, Jialing Li, Hao Gao, Fenghua Wen 2025, 33 (8):  50-60.  doi: 10.16381/j.cnki.issn1003-207x.2023.2218 Abstract ( 17 )   HTML ( 0 )   PDF (1998KB) ( 4 )   Save Pyramid ownership structure is common in China. The pyramid ownership structure has its uniqueness， which is manifested as the separation of cash flow rights and control rights. This separation distorts the decision-making motivation of the controlling shareholders under the pyramid ownership structure and guides them to make high-risk investment behaviors， which may include decisions about labor income share. Labor income share is an important part of a firm's operating decision， which can reflect the initial distribution status of labor factors within the firm. In the context of pursuing common prosperity， it is of great theoretical and practical significance to discuss the impact of pyramid ownership structure on labor income share and its potential mechanism. Based on the sample of A-share listed companies in Shanghai and Shenzhen from 2008 to 2020， the impact of pyramid ownership structure on the labor income share of enterprises is examined， the reasons for controlling shareholders to increase the labor income share from the perspectives of returns and risks are analyzed， and it is proposed that optimizing the labor structure is the main influencing mechanism. The benchmark regression results of this paper show that the more the pyramid levels are， the higher the labor income share of enterprises is， which verifies the main hypothesis of this paper. Secondly， the reliability of the results is examined by replacing the measurement methods of explanatory variables and explained variables， adding observable control variables， omitting unobservable variable analysis， propensity score matching and entropy balance matching， and instrumental variable method， and it is found that the regression results are robust. When external uncertainty is high or labor can create higher value and shareholders can obtain more retained earnings， the positive relationship between pyramid hierarchy and labor income share is more significant. Moreover， as the pyramid hierarchy increases， firms are able to achieve and upgrade the labor structure. Further studies show that the impact of pyramid hierarchy on labor income share is more obvious when firms have better external and internal governance， more adequate human capital and lower minimum wage standard. The literature on the influencing factors of labor income share is expanded， the research conclusions on the economic consequences of pyramid hierarchy are enriched， and new evidence for in-depth understanding of the relationship between labor and capital is provided. Figures and Tables | References | Related Articles | Metrics

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Reduction in Social Insurance Contributions and the Upgrade of Corporate Labor Demand Structure: Evidence from Internet Recruitment of Listed Companies Peng Jing, Chengyi Ren, Gang Kou 2025, 33 (8):  61-74.  doi: 10.16381/j.cnki.issn1003-207x.2024.0109 Abstract ( 13 )   HTML ( 0 )   PDF (2401KB) ( 3 )   Save For a long time， high social insurance contributions have imposed significant liquidity constraints on enterprises， resulting in notable negative impacts on their operations. To alleviate this burden， China has repeatedly reduced social insurance contributions since 2015， which has played a positive role in stimulating corporate vitality and promoting enterprise transformation. Meanwhile， as key participants in market economic activities， the high-quality development of enterprises forms the microeconomic foundation for high-quality economic development， with the upgrading of human capital serving as the endogenous driving force. Therefore， whether reducing social insurance contributions can improve the labor demand structure of enterprises and subsequently promote the upgrading of human capital is a topic worthy of research. Based on the internet recruitment data of listed companies between 2016 and 2021， the impact of social insurance contribution reductions on corporate labor demand structure is investigated by adopting the difference-in-differences model. The results show that cuts in social insurance contribution significantly promote the upgrading of the corporate labor demand structure. The effect is more pronounced in non-state-owned enterprises， enterprises with high financial constraints， and regions with lower employment targets and talent settling thresholds. Mechanism tests show that reductions in social insurance contribution expand corporate relative demand for high-skilled labor by capital-skill complementary effect and liquidity constraint easing effect. Further discussion reveals that social insurance contribution reductions not only raise the academic qualifications threshold when hiring but also increase the actual employment ratio of high-skilled labor， which confirms that there is no strategic hiring behavior in enterprises. The findings have important implications for optimizing the social insurance contribution system and promoting the high-quality development of enterprises. Figures and Tables | References | Related Articles | Metrics

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Competitive Network and Financial Performance: Empirical Evidence Based on Explainable Random Forest Jianxin Zhu, Kexin Liu, Nengmin Zeng, Xiong Wu 2025, 33 (8):  75-89.  doi: 10.16381/j.cnki.issn1003-207x.2022.2294 Abstract ( 12 )   HTML ( 0 )   PDF (3785KB) ( 2 )   Save The significance of the competitive network position as a key factor in firms' strategic decision-making and its impact on financial performance is the focus of this study. First， using China's property insurance industry from 2008 to 2019 as the research subject， a Random Forest model is constructed to investigate the predictability of competitive network characteristics and market structure characteristics. Next， the importance of competitive network and market structure is analyzed using the feature importance evaluation method of the Random Forest model. Finally， the nonlinear functional relationship between these important features and financial performance is revealed using SHAP values and further the causal relationship between key competitive network features and financial performance is verified using traditional econometric models （Propensity Score Matching）. It also explores the differences between nonlinear and linear model results.The research findings show that　（1） Overall， the absence of either competitive network or market structure features can reduce the fitting performance of the enterprise financial performance model， with the impact of missing competitive network features being more pronounced. （2） In terms of the ranking of feature importance， competitive network features account for approximately 30% of the importance to enterprise financial performance， significantly higher than the importance of market structure features. Key features within competitive network include： individual network size， intermediary frequency， and closeness centrality. （3） These three key features follow a power-law distribution with respect to enterprise financial performance， and the existence of a strong causal relationship between important competitive network features and financial performance was verified through propensity score matching. The conclusions of this paper enrich the research outcomes in the field of competitive networks and financial performance， providing support for them， and offer a new solution for causal analysis and verification between high-dimensional， nonlinear factors. Figures and Tables | References | Related Articles | Metrics

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Mega Railway Projects and Regional Economic, Social and Cultural Development: A DID Test Based on Qinghai-Tibet Railway Huiyu Zhou, Ruimin Li, Meng Xu 2025, 33 (8):  90-99.  doi: 10.16381/j.cnki.issn1003-207x.2023.1381 Abstract ( 14 )   HTML ( 0 )   PDF (2474KB) ( 5 )   Save Mega-railway projects hold a crucial position in developing and constructing a strong transportation network in China. Existing methods to quantitatively analyze and evaluate externalities of these mega-railway project are currently inadequate， and quantitative based studies on the externalities of mega-railway projects has important theoretical and practical implications for the sustainable development. Three key questions， i.e. what is the impact of the opening of Qinghai-Tibet Railway on the regional economy， society and culture development？ What is the mechanism of the Qinghai-Tibet Railway effect？ And is there heterogeneity in the impact of the Qinghai-Tibet Railway on different regions？ To answer these questions， a difference-in-differences （DID） model is proposed basing on the quasi-natural experiment of the opening of the Qinghai-Tibet Railway. Using panel data from the Qinghai-Tibet region from 2003 to 2020， the effect and mechanism of the opening of Qinghai-Tibet Railway on the economic， social， and cultural development of the regions along the route are explored. Parallel trend test， random selection of treatment groups and instrumental variable method are used to verify the reliability of the research conclusions. The DID method can overcome biases in modelling estimation caused by variable endogeneity and omitted variables， making it an effective method for evaluating the externality on the opening of the Qinghai-Tibet Railway. Results show that the opening of the Qinghai-Tibet Railway generally increases the GDP per capita of the regions along the railway， reduces the income gap between urban and rural residents， and significantly increases the number of cultural enterprises and the total tourism income. Meanwhile， the effect of Qinghai-Tibet Railway is sustainable. Mechanism studies find that Qinghai-Tibet Railway promotes the economic， social and cultural development along the line by stimulating investment and developing service industry， promoting employment， increasing population mobility and attracting tourists. Heterogeneity analysis shows that the economic， social， and cultural effects of the Qinghai-Tibet Railway have a greater impact on non-central cities along the railway than on central cities. These findings suggest that the Qinghai-Tibet Railway is conducive to structural transformation and shared prosperity in the Qinghai-Tibet region， and its economic， social and cultural effects are continuous. This quantitative analysis framework for the externalities of mega-railway projects provides theoretical support for promoting the high-quality development of similar projects， as well as the sustainable and harmonious regional development. Figures and Tables | References | Related Articles | Metrics

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A Deep Transfer Learning Evaluation Method for Community Resilience Governance Capacity under Major Epidemics Wenhe Chen, Zhipeng Chang, Hanting Zhou, Longsheng Cheng, Buyu Wen 2025, 33 (8):  100-111.  doi: 10.16381/j.cnki.issn1003-207x.2022.2620 Abstract ( 18 )   HTML ( 0 )   PDF (4999KB) ( 19 )   Save The capacity assessment model of community resilience governance is conducive to establishing the long-term mechanism for grassroots prevention and control under major epidemics. However， there are some problems with community governance capacity assessment models. Firstly， the numbers of samples collected are usually hundreds or thousands， which belongs to small samples in machine learning or deep learning， easily leading to underfitting and overfitting of model training. Secondly， the samples are divided into simple and difficult samples， and the traditional assessment models do not have enough ability to recognize difficult samples. Then， feature extraction methods rely on manual experience， which are difficult to learn how experts select features for assessment. Finally， the parameters of the deep learning models rely on expert experience， which are challenging to select the optimal parameters scientifically. The above problems affect the performance of model in evaluating the capacity of community resilience governance.Therefore， the model in the following ways is improved. Firstly， the model is augmented with data using density peak clustering to improve adaptive oversampling （DPAS） to treat the target class as a minority class sample and the remaining other classes as the majority class， for augmenting the sample size of the data. Secondly， Googlenet using transfer learning （TL） is improved to increase the effectiveness of feature extraction under small samples conditions. In addition， multi-class focal loss（MFL） is used instead of multi-class cross-entropy loss（MCL）is used to enhance the focus on difficult samples in model training， for improving the identification of difficult samples. Meanwhile， the hyperparameters of the optimized model using multi-objective slime mould algorithm （MOSMA） are guaranteed to be optimized under multi-objective conditions. Finally， the model performance is validated using the collected community sample dataset， comparing with different baseline models， multi-objective optimization methods and data enhancement models. Experiments demonstrate that the proposed model outperforms the other models. The structure and parameter settings are justified using ablation experiments and sensitivity analysis.A novel idea about community resilience governance capacity is provided， which can be directly applied to community resilience governance capacity under major epidemics. In addition， the results of this study have practical implications that can be extended to other areas of resilient governance assessment. Figures and Tables | References | Related Articles | Metrics

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Research on the Measurement of the Association Fusion between Digital Economy & Manufacturing Industry and Its Regional Heterogeneity Jianxuan Li 2025, 33 (8):  112-122.  doi: 10.16381/j.cnki.issn1003-207x.2022.2202 Abstract ( 19 )   HTML ( 0 )   PDF (2109KB) ( 8 )   Save The digital economy is an important driving force for global economic growth， and is playing an important role in accelerating China's economic development and achieving the transformation and upgrading of traditional industries. Manufacturing industry is an important driving force for achieving a new level of real economy， and its digitalization level urgently needs to be comprehensively improved. Promoting the integration of manufacturing and digital economy， have attracted increasing attention from researchers and policymakers.Based on the “statistical classification of digital economy and its core industries （2021）”， the digital product manufacturing industry and the digital technology application industry are selected as representatives of the digital economy industry. At the same time， the manufacturing industry is divided into three categories： resource intensive， labor intensive， and technology intensive. Then， the demand driven model and supply driven model in the input-output method are used to measure the backward and forward association fusion effects of manufacturing industry and digital economy industries in the eight major economic zones of China in 2012， 2015， 2017， and 2018， as well as the fsion ripple effect. Finally， its regional heterogeneity is further explored.The results show that from a national perspective， there are positive backward and forward association fusion effects between digital economy industry and manufacturing industry， and there are obvious differences among industries and departments in the association fusion effect of digital economy industry and manufacturing industry. From the perspective of the eight economic zones， there are significant regional heterogeneities in the backward and forward association fusion effects of digital economy industry and manufacturing industry. In addition， the demand pulling force of digital economy industry to manufacturing industry is generally stronger than the supply driving force， but there are also obvious regional heterogeneities： Among them， the digital economy industry and manufacturing industry in the three coastal economic zones， the middle reaches of the Yellow River and the southwest economic zone are mainly pulled by demand， while the digital product manufacturing industry in the northwest and northeast economic zones and the digital technology application industry and manufacturing industry in the middle reaches of the Yangtze River economic zone are mainly driven by supply. The results of this paper provides useful policy enlightenment for promoting the deep integration of digital economy and substantial economy， accelerating the digital transformation of manufacturing industry， and facilitating the construction of manufacturing power and digital China. Figures and Tables | References | Related Articles | Metrics

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Partially Sampling Inspection Process Based Online Change Detection Xiong Han, Yang Yang, Xiaochun Deng, Jianjie Gou, Jie Guo, Chen Zhang 2025, 33 (8):  123-130.  doi: 10.16381/j.cnki.issn1003-207x.2022.1292 Abstract ( 13 )   HTML ( 0 )   PDF (2585KB) ( 5 )   Save In manufacturing systems， practitioners rely on sampling inspection to detect real-time changes within the system. However， due to the large number of categories （variables） that need inspection and the limited availability of inspection resources such as human labor or instruments， only a subset of categories can be inspected at each time point. As a result， only partial observations of the defective numbers for each category can be obtained. To enable a prompt system change detection， it requires not only a powerful change detection scheme that can deal with partially observable data， but also an adaptive variable selection strategy to identify which set of variables to be observed for the next time point such that the change information can be reserved maximally. The challenge of online change detection is addressed for high-dimensional data streams following a binomial distribution， based on a partially sampling inspection process. First， high-dimensional data is decomposed into smooth normal signals and sparse abnormal signals. The normal signals are represented as a linear combination of basis functions multiplied by corresponding coefficients， capturing the correlations between variables. The anomalous parameter is modeled using a spike-slab distribution and variational Bayesian inference is employed to estimate the distribution parameters. Next， a likelihood ratio test is constructed as the detection statistic for detecting system changes. Furthermore， combinatorial multi-armed bandit （CMAB） algorithms are leveraged by treating the test statistics as the reward function. Specifically， a variable selection policy based on Thompson sampling is proposed， enabling the selection of the most anomalous categories for inspection at each time point and minimizing change detection delay. Through experimental evaluations， the results highlight its potential to improve the efficiency and accuracy of defect detection in manufacturing systems while considering the constraints of limited inspection resources. Figures and Tables | References | Related Articles | Metrics

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Research on the Distributed Resource-constrained Multi-project Reactive Scheduling Problem Considering Multi-skilled Staff Leave Yining Yu, Zhe Xu, Song Zhao, Feifei Li 2025, 33 (8):  131-143.  doi: 10.16381/j.cnki.issn1003-207x.2022.2456 Abstract ( 14 )   HTML ( 0 )   PDF (2671KB) ( 7 )   Save In the distributed decision-making environment， the shared resources are considered multi-skilled staff， independently dispatched by the respective project decision-makers from their interests. The shared multi-skilled staff， as the only link between multiple projects， are uniformly coordinated and distributed by the coordination manager. An effective coordination mechanism based on staff characteristics is designed to allocate shared multi-skilled staff for multi-project activities and determine the activity’s start time. This problem is called Multi-Skilled Distributed Resource Constrained Multi-Project Scheduling Problem （MS-DRCMPSP）. In these staff-oriented projects， the staff leave will reduce the available resources during project execution， destroying the pre-established baseline scheduling plan. How to repair the damaged baseline scheduling plan with a reactive scheduling method is studied.To solve the distributed resource-constrained multi-project scheduling problem with the multi-staff leave， a three-stage decision model including initial local scheduling， global coordination decision-making， and repaired schedule is established. The first two models are used to formulate the initial baseline scheduling plan. The initial local scheduling stage takes minimizing the project makespan as the optimization goal， and the global coordination decision-making model takes minimizing the average project delay （APD） as the optimization goal. When the baseline scheduling plan is damaged due to staff leave， the manager designs the repaired objective according to the decision preference of activities and staff to repair the damaged baseline scheduling plan. A “dynamic” strategy， including “waiting” and “adjusting” strategies is designed to develop the repaired scheduling plan， and a softmax scoring mechanism based on conflict activities is designed to apply in the repaired scheduling plan. The softmax scoring mechanism includes a series of evaluation factors： activity duration， slack time， resource demand， and several immediate activities.An example of a multi-skilled scheduling problem is generated based on MPSPLIB and experimental research is carried out on this example. The research shows that when considering the repaired objectives of staff working time deviation before and after the repairing， the “waiting” or “dynamic” strategy can be applied； When considering the minimizing repaired objectives of the APD and the deviation of activities start time before and after repairing， the “adjusting” or “dynamic” strategy can be applied； When considering the minimizing repaired objectives of both activities and staff， the “dynamic” strategy can be directly used to complete the repaired scheduling plan； When solving large-scale examples， the three-stage algorithm of softmax scoring mechanism based on conflicting activities is superior to sequential game algorithm and other heuristic algorithms. The optimization results can be improved by 14.48% at most.The vacancy of the multi-skilled scheduling problem in the distributed decision-making environment is made up for， especially in the case of uncertain resource availability. In addition， it provides a suitable repaired strategy for managers' different preferences and the related reference for subsequent relevant researches. Figures and Tables | References | Related Articles | Metrics

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Integrated Resource-constrained Project Scheduling and Material Ordering Problem with Limited Storage Space Baofeng Tian, Jingwen Zhang, Lubo Li, Junjie Chen 2025, 33 (8):  144-155.  doi: 10.16381/j.cnki.issn1003-207x.2022.1584 Abstract ( 11 )   HTML ( 0 )   PDF (2602KB) ( 3 )   Save Due to the large quantity and high strength of material demand， prefabricated building projects require a large amount of material storage space， but the storage space at the construction site is usually extremely limited. The camped storage space greatly deteriorates the contradiction between the large demand for construction materials and the small storage space of materials. Meanwhile， the limited storage space not only sharply restricts the parallel execution of activities， but also greatly increases the order times of materials， thus worsening the project performance. In order to solve this practical dilemma， the integrated resource constrained project scheduling and material ordering problem with limited storage space （RCPSMOP-LSS） is studied.Considering the renewable resources， precedence constraints， non-renewable resource constraints， limited storage constraints and dynamic inventory updating formula， an integrated optimization model of resource-constrained project scheduling and material ordering with limited storage space is proposed to minimize the total project cost consisting of the material ordering cost， inventory cost and the cost associated with the completion time. In order to obtain a project scheduling plan and a material ordering plan， the integrated model contains two types of decision variables： finish time of each activity for project scheduling part and quantity of each material at each time period for material ordering part， which greatly increased the complexity of the model.In order to solve the model， the properties of the model are firstly analyzed. It is found that the model is strongly NP-hard and needs to be solved in two stages. Then， a double-layer heuristic algorithm is developed. To be more specific， an improved genetic algorithm is firstly designed on the outer layer to obtain the project scheduling plan. Besides， a novel chromosome representation， and mutation operator is developed according to characteristics of solution space. Then， the activity schedule obtained in outer layer is considered as an input of the inner algorithm. By analyzing the nature of the problem， the material ordering subproblem is modeled as shortest path model， which considerably reduces solving difficulty. Furthermore， an exact algorithm is presented on inner layer to obtain the optimal materials ordering plan under the specific activity schedule.To evaluate the effectiveness of the proposed model and algorithm， large scale numerical experiments are carried out based on the instances generated by ProGen and selected from PSPLIB. The orthogonal experiment method is designed to determine the appropriate parameter sets for the proposed algorithm. Besides， to prove the validity of the integrated problem， a comparative experiment of decentralized decision-making and the proposed integrated model is set up， and the experimental result shows that integrated model can reduce the project cost by 12%. Furthermore， the comparisons with the Cplex software show that the proposed algorithm has better computational efficiency.From the perspective of limited storage space， the overall cost of project is optimized by integrating project scheduling and material ordering， which provides project managers with more comprehensive decision support on construction projects with congested site space. Moreover， reference value is provided for the research on project scheduling with limited space. Figures and Tables | References | Related Articles | Metrics

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Effects Analyses of Delivery Fleet Renewal Policies Considering Electric Vehicle Deployment and Routing Optimization Yongling Gao, Yuan Qiao, Meng Xu 2025, 33 (8):  156-165.  doi: 10.16381/j.cnki.issn1003-207x.2022.1252 Abstract ( 12 )   HTML ( 0 )   PDF (3260KB) ( 4 )   Save The promotion of electric vehicles （EVs） has become an important way for green development of urban delivery fleets. In recent years， some cities in China have implemented policies for delivery fleet expansion and renewal， which include without vehicle energy types restriction （referred to as “hybrid policy”） and designated new energy vehicles such as electric vehicles （referred to as “electric policy”）. To study both policies， a hybrid fleet composed of fuel vehicles （FVs） and EVs is analyzed and mathematical programming models for fleet renewal and routing optimization are presented， which consider access restrictions for FVs and the driving range limitation for EVs. On this basis， the social welfare under the hybrid and electric policies is compared. An improved genetic algorithm to solve the proposed optimization models is designed. The solutions are compared with them solved by the Gurobi. Using the customer demand data of company A， it is found that in comparison to the hybrid policy， the electric policy can make the proportion of EVs in the fleet the same or higher， often increase the total EV mileage， and result in higher total fleet costs. When the operating cost of EVs （FVs） decreases （increases）， the total mileage of EVs can rise. In the presence of the higher driving range of EVs or more access restrictions for FVs， the total mileage of EVs does not necessarily increase. The government should push for the electric policy if the used number of EVs under the electric policy is higher （lower） than that under the hybrid policy and the environmental impact of EV production is sufficiently small （large）； Otherwise， the government should not implement the electric policy blindly. Figures and Tables | References | Related Articles | Metrics

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Joint Optimization Model and Algorithm of Cold Chain Product Production-inventory-transportation Considering Freshness-keeping Effort in the Physical Internet Shoufeng Ji, Hongyu Liu, Lijie Wang, Yuanyuan Ji 2025, 33 (8):  166-176.  doi: 10.16381/j.cnki.issn1003-207x.2022.1949 Abstract ( 21 )   HTML ( 0 )   PDF (2831KB) ( 6 )   Save Due to the lack of collaboration and interconnections between firms， the cost and wastage are usually quite high in traditional cold chain logistics. The advent of Physical Internet has motivated us to explore the potential value of the integrated production-inventory-transportation optimization problem for cold chain products. Consequently， the production-inventory-transportation joint optimization problem considering cold chain products in Physical Internet is proposed. Considering the characteristics of perishable cold chain products and high logistics costs， the freshness-keeping efforts and the decay of freshness are integrated to the production-inventory-transportation model in Physical Internet. The relevant mixed integer linear programming model is constructed to quantify the advantage of Physical Internet. Improved particle swarm optimization is designed to solve the model. Moreover， the validity and accuracy of model and algorithm are verified by computational experiments about production-inventory-transportation system of Shenyang Northeast Cold Fresh Port. The influence of different parameters on preservation effort cost and freshness attenuation is obtained via sensitivity analysis， which can provide scientific decision-making reference for enterprise operation and management. Figures and Tables | References | Related Articles | Metrics

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System Dynamics Analysis of Emergency Materials Mobilization Chain Resilience under the Coupling of Multiple Disruptions Wenqiang Shi, Zhaojun Kong, Mingyue Wang, Jie He 2025, 33 (8):  177-188.  doi: 10.16381/j.cnki.issn1003-207x.2022.2637 Abstract ( 9 )   HTML ( 0 )   PDF (4567KB) ( 6 )   Save To reduce disaster losses and save more lives， government departments and mobilization agencies need to provide emergency supplies to disaster areas through the emergency material mobilization chain. The emergency material mobilization chain may be affected by emergencies and derivative crises， resulting in significant disturbances or even disruptions in different segments. Multiple disruptions have complex and nonlinear coupling relationships， making it difficult to accurately quantify the risks and effectiveness of the mobilization chain system. Meanwhile， China’s 14th Five-Year Plan for National Emergency System proposes enhancing resistance to damage and rapid recovery under extreme conditions. Therefore， clarifying the internal coupling mechanism of multiple disruptions and finding a recovery mechanism for the emergency material mobilization chain has become a practical problem that needs to be urgently solved by all sectors of society. The existing literature on disruptions management is mostly based on the enterprise level and static perspective， with a gap of research from the government level and overall control over the dynamic perspective. In addition， current research on emergency material mobilization has not taken into account the degree of damage caused by multiple disruptions. At the same time， huge emergencies not only cause demand or supply disruptions， but may also affect the normal operation of the mobilization chain， disrupting communication between some management departments， and ultimately causing organizational disruption. How to provide recovery strategies for emergency material mobilization chains in such disruption events is still rarely discussed. The optimization mechanism of emergency material mobilization chain resilience under the coupling of multiple disruptions is even rarer. Aiming to address this problem， the evolution and coupling mechanisms of various disruption events are sorted out， when demand disruption， supply disruption and organizational disruption happen simultaneously. Then， the system dynamics model of the emergency materials mobilization chain is established under the coupling of multiple disruption events， considering the restoration process of each subsystem and the mobilization magnitude of the supply agents. The relief tents case in Zhejiang Province during 2008 Sichuan earthquake is used to simulate the resilience value of the emergency materials mobilization chain in continuous time. How to adjust the key variables to enhance the emergency materials mobilization chain resilience is explored. Several key findings are proposed. When multiple disruption events are coupled， restoring the damaged subsystem can improve the mobilization chain resilience， but the resilience is still lower than the ideal state. Improving the level of restoring mobilization of each subsystem can speed up the recovery of mobilization chain efficiency at the beginning of the task， but its influence will decrease at the end of the task. The impact of disruptions on the emergency management department is measured and restorative measures for buildings， roads， and information systems are introduced to explore the effect of various recovery strategies on the mobilization chain resilience. These works have not been discussed by previous researchers， which can help the practitioners to put forward improvement measures for the bottlenecks in time， so as to provide scientific reference for the practical work. Figures and Tables | References | Related Articles | Metrics

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The Impact of Emergency Supplies Quantitative Differences and Social Factors on the Fair Perceptions of Victims: An Empirical Study Jianfang Shao, Yu Fan, Xihui Wang, Liang Liang 2025, 33 (8):  189-197.  doi: 10.16381/j.cnki.issn1003-207x.2022.2670 Abstract ( 15 )   HTML ( 0 )   PDF (1447KB) ( 7 )   Save The fair allocation of the emergency supplies is one of the most important concerns of victims after the occurrence of disasters. A fair allocation not only helps calm down the victims and keep the order in disaster-affected areas， but also avoids the waste of emergency resources. However， a fair allocation does not mean giving the exact same amount of relief supplies to each victim. Instead， decision-makers should consider the demand and preferences of victims and make allocations based on them. Hence， in this paper， the authors adopt inequity aversion as the measurement of fairness from the perspective of ‘people-centered’， which avoids the situation that decisions made are not in line with the reality. Inequity aversion is a widely studied concept in economics and social psychology， which mainly measures the degree that people dislike inequal allocation. Through online investigation， the factors that might have impacts on the fair perceptions of victims are analyzed. It is found： （1） Quantitative differences in the allocation of emergency supplies， the income， gender， education and age of victims have significant impacts on the inequity aversion of victims. （2） When the quantitative differences are small， the social factors （the gender， income， age and education of victims） do not have significant impacts on the inequity aversion of victims. However， with the increase of the quantitative differences， the impacts will gradually become significant. Based on the result of analysis， suggestions are provided to decision-making of resource allocation in emergency logistics， which helps mitigate the impact of negative emotions of victims on the relief operations. Figures and Tables | References | Related Articles | Metrics

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Research on Emergency Logistics Path Optimization Based on Hybrid Artificial Fish Swarm Algorithm Yanqiu Liu, Jihui Hu 2025, 33 (8):  198-208.  doi: 10.16381/j.cnki.issn1003-207x.2022.1672 Abstract ( 12 )   HTML ( 0 )   PDF (2152KB) ( 9 )   Save In recent years， emergencies have occurred frequently， causing a certain threat to the stable and prosperous development of the country and the happy and healthy life of the people. After the occurrence of emergencies， effective emergency material distribution should be taken in time to avoid more serious consequences. At the beginning of the corresponding stage， there is a shortage of supplies as well as road damage. When the supply of materials is insufficient， the dissatisfaction of the affected points is reduced as much as possible according to different needs， and the materials are distributed fairly. After the emergency， the road network will be damaged to different degrees， which in turn will affect the transportation time of supplies and aggravate the losses of the affected sites. The emergency logistics path optimization problem is addressed， taking into account the fair distribution of materials and road damage. A two-stage model of emergency logistics path optimization under the situation of road damage and lack of emergency supplies is established. And the adaptive hybrid artificial fish swarm algorithm is designed to solve the model， and the model and algorithm are validated by some data in Solomon's calculation case， revealing the influence of maximum vehicle load and driving speed. The results show that （1） compared with the artificial fish swarm algorithm， the AH-AFSA algorithm is able to obtain better results in solving the emergency logistics vehicle path optimization problem considering the fair distribution of materials and road damage， with a 12.9% improvement in total vehicle transportation time and a 29.0% improvement in algorithm running time. （2） Compared with AFSA， AH-AFSA algorithm can converge faster in the early stage of the algorithm and converge faster； it still has stronger local search capability in the late iteration of the algorithm and can find better results. （3） Finally， the changes of the maximum load and range of the vehicle affect the total vehicle transportation time. In general， the total vehicle transportation time decreases with the increase of the maximum load weight， and produces some volatility with the change of the vehicle range distance. These results provide good insights for the solution of the emergency vehicle path optimization problem under the situation of road damage and material shortage. Figures and Tables | References | Related Articles | Metrics

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Research on Route Selection for Emergency Supply Distribution Based on Dual Losses of Shortage and Delay Bing Su, Xiangwen Chen, Meng Zhang, Hao Ji, Lulu Sun, Yang Xu, Qing’e Guo, Guohui Lin 2025, 33 (8):  209-217.  doi: 10.16381/j.cnki.issn1003-207x.2023.0781 Abstract ( 12 )   HTML ( 0 )   PDF (2160KB) ( 3 )   Save Efficient emergency supply distribution after sudden disasters stands as a critical factor in mitigating overall losses. The arrangement of emergency supply distribution encounters two challenges. First， it is difficult to mobilize sufficient emergency supply in a short time after the disaster， resulting in unmet needs of victims and subsequent losses. Second， there exists a pressing demand for timely supply arrivals， and any delays in emergency supply distribution cause further losses. Ensuing equitable access to disaster-stricken individuals necessitates vigilant monitoring of shortages of supply and impediments of distribution at each demand point. It is of great significance to formulate the routing strategy for scarce emergency supply distribution with time windows to reduce dual losses of storage and delay at each demand point as much as possible.In this paper， the problem of determining the optimal routes for emergency supply distribution is studied with the objective of minimizing the maximum dual losses of shortage and delay of each demand point. The premise acknowledges that the emergency supply is scarce and the available vehicles at the distribution center are potentially inadequate. The definition of shortage loss is given based on the shortage ratio， and the definition of delay loss is given based on piecewise penalty function.Further comprehensive consideration of supply shortage and distribution delay of each demand point servers as the foundation of developing a route selection model for emergency supply distribution. The objective of this proposed model is to minimize the maximum dual losses of shortage and delay of each demand point， aiming to avoid critical shortage at any single demand point and ensure adherence to stringent distribution time windows.An in-depth analysis of scenarios regarding supply shortage and distribution delay at each demand point is conducted. It delineates solutions in two distinct cases： sufficient vehicles and insufficient vehicles. In the case with sufficient vehicles， all amounts of supply in the distribution center can be sent to demand points. The routes for emergency supply distribution is initially determined based on the shortest paths from the distribution center to each demand point. Then， the quantities of supply distributed to each demand point is determined by equating the shortage ratio at each demand point， and an algorithm A^{\mathrm{*}} is developed accordingly. In the case with insufficient vehicles， it is not possible to send all amounts of supply in the distribution center， and an approximate algorithm G{A}^{\mathrm{*}} is developed to solve the problem in this case.By analyzing the performances of the proposed algorithms， the results demonstrate that algorithm A^{\mathrm{*}} is an exact algorithm in the case with sufficient vehicles. Its time complexity is O(n^{\mathrm{3}})， where n represents the number of demand points. The results also evaluate the time complexity of the approximate algorithm G{A}^{\mathrm{*}} in the case with insufficient vehicles. Its time complexity is O(L{n}^{\mathrm{2}})， where L denotes the number of vehicles and n represents the number of demand points. Additionally， the approximate ratio of algorithm G{A}^{\mathrm{*}} and the upper and lower bounds of the approximate ratio are also proved.A case study is conducted focusing on the rescue in Luding County， Sichuan Province in September 2022 with a magnitude 6.8 earthquake. The dataset utilized for analysis is based on actual road conditions and related reports from CHINANEWS.COM. The algorithms developed in this paper are used to solve the route selection model for emergency supply distribution with the objective of minimizing the maximum dual losses of shortage and delay of each single demand point. The routing strategy is obtained， and an approximation ratio of 1.32 is yielded when comparing the maximum dual losses of shortage and delay at each single demand point to the result of the optimal solution.By investigating the route selection for emergency supply distribution based on dual losses of shortage and delay， the model developed in this paper aims to prevent the simultaneous occurrence of maximum shortage and delay at a single demand point， thereby enhancing the effectiveness of emergency supply distribution after sudden disasters. The proposed algorithms also provide reference for similar research problems. Future research endeavor will likely focus on building a model that closely mirrors real-world scenarios or refining the algorithm to achieve improved efficacy. Figures and Tables | References | Related Articles | Metrics

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Optimization of Reactive Project Scheduling with Stochastic Resource Requirements Considering Information Handling Input Xiao Cui, Zhengwen He, Nengmin Wang 2025, 33 (8):  218-229.  doi: 10.16381/j.cnki.issn1003-207x.2022.1629 Abstract ( 13 )   HTML ( 0 )   PDF (4320KB) ( 6 )   Save As one of the important uncertainty factors of the project， stochastic resource requirements are usually highly variable. And resource conflict caused by highly variable stochastic resource requirements often leads to disruptions in project implementation. Therefore， it is crucial to take measures to reduce the variability of stochastic resource requirements. Besides， information handling input is an effective measure， which can be used to accurately estimate the resource requirements of project activities， thereby ultimately reducing the variability of the resource requirements of project activities.Based on the above facts， the reactive project scheduling optimization problem with stochastic resource requirements is investigated considering information handling input. In this study， the quantitative relationship between the information handling input and the reduced variability of activity resource requirements is established first. After defining the problem， the optimization model is constructed to minimize the total implementation cost of the project， where， the decision variable of the model is the information handling input scheme， and the total implementation cost of the project is defined as the sum of the cost incurred by the information handling input scheme and the reactive adjustment cost caused by the execution of baseline schedule under the scheme. Especially， the reactive adjustment cost caused by the execution of baseline schedule under the scheme can only be obtained by calculating the weighted sum of the deviations between the activity start time in the realized reactive schedule and those in the baseline schedule， whereas the corresponding reactive schedule when the baseline schedule is interrupted needs to be determined by the reactive project scheduling model. Furthermore， according to the NP-hard property of the studied problem， a variable neighborhood search algorithm is developed to solve the models.At last， a typical practical case is utilized to illustrate the research problem， demonstrating the effectiveness of reasonable investment in information handling to control the total implementation cost of the project. And then， by analyzing the sensitivity of several key parameters， it is concluded that the total implementation cost of the project rises with increase in the per unit cost of information handling input and the activity weight， and drops with the increase in the renewable resource availability.A new idea is provided for project managers to manage and control the project cost， and quantitative decision support is also provided for reactive project scheduling with stochastic resource requirements considering information processing input. Besides， it also supplies some references for further research. Figures and Tables | References | Related Articles | Metrics

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Robust Guaranteed Delivery and Allocation Optimization for Online Display Advertising with Covariate-based Information Wenqiang Dai, Danyang Li 2025, 33 (8):  230-237.  doi: 10.16381/j.cnki.issn1003-207x.2022.1769 Abstract ( 8 )   HTML ( 0 )   PDF (1928KB) ( 4 )   Save An online advertising contract inventory allocation model is proposed， in which covariate information is incorporated and the impression supply of targeted viewers is treated as uncertain. Owing to the fact that the joint probability distribution of viewer impression supply and covariate information is difficult to be precisely known， the model is constructed by means of a distributionally robust optimization approach. Building upon existing research， an ambiguity set based on covariate information is first formulated； subsequently， an exact algorithm is derived through the utilization of dual cone techniques. The validity of the proposed model is ultimately demonstrated via simulation experiments. Furthermore， managerial implications are provided. Figures and Tables | References | Related Articles | Metrics

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The Optimal Free Trial Strategy of the Online Course Platform Considering Bundling Zhaofang Mao, Ruiying Yuan, Qingran Zhang 2025, 33 (8):  238-249.  doi: 10.16381/j.cnki.issn1003-207x.2022.2352 Abstract ( 17 )   HTML ( 0 )   PDF (2627KB) ( 6 )   Save Online courses have broken the limitations of time and space， providing a convenient tool for quality education and lifelong learning， which has led to the rapid development of online course platforms. However， it has also brought new management challenges. For example， when selling new course products， start-up online education platforms usually spend a lot of resources to provide consumers with a free trial of a certain length in order to reduce the uncertainty of the quality of the course. How to improve the effectiveness of this free trial strategy remains to be studied， especially when the platforms face different courses. Based on this， a game-theory model is built to characterize the relationship between consumers' uncertainty about the quality of online courses and the duration of free trials， and the optimal free trial strategy for a monopolistic online course platform that offers both premium and regular courses is analyzed， ultimately the mathematical formula is provided for the optimal free trial duration. Furthermore， the research problem is extended to bundled sales， analyzing the impact of bundled sales on the optimal free trial duration and pricing decision of the online course platform. It is shown that although free trials can alleviate consumers' uncertainty about course quality， when the quality difference between the two courses is large， the platform prefers to offer free trials only for premium courses rather than both types. In addition， compared with the non-bundled scenario， bundled sales benefit both the online course platform and consumers. Figures and Tables | References | Related Articles | Metrics

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Research on the Network Maintenance Scheduling Problem with Flexible Arc Outages and Its Algorithm Shuang Jin, Jing Zhou, Qian Hu 2025, 33 (8):  250-259.  doi: 10.16381/j.cnki.issn1003-207x.2022.2617 Abstract ( 9 )   HTML ( 0 )   PDF (2250KB) ( 3 )   Save Maintenance scheduling is to make a scheduling plan for a series of maintenance activities within a certain period before an accident occurs. Through preventive maintenance， the service life of equipment or facilities can be effectively improved to avoid losses caused by unexpected faults. In practice， there are many network-based services， such as communication networks， transportation networks， supply chain networks， etc. Different maintenance activities are distributed in different locations of the network， and maintenance downtime will cause flow losses in the network.A network maintenance scheduling problem where maintenance activities are carried out on the arcs of the network with flexible start time window constraints. An arc is closed if a maintenance activity is being carried out on it and thus no flow can pass through it. The problem is to arrange the start time of each maintenance activity in the maintenance period， to maximize the total flow of the network with flexible arc outages （MaxTFFAO）， that is minimizing the flow reduction caused by maintenance. The problem has been proposed initially from the actual demand of a coal supply chain and there exist some studies of greedy heuristics and Benders decomposition algorithm on it.Considering large-scale networks and different time limits in practical applications， it expects to further explore the characteristics of this problem and design more effective heuristic algorithms. Firstly， the problem is formulated as a mixed integer programming model， and some properties which are useful for algorithm design are analyzed. Secondly， effective neighborhood operators are designed and embedded into heuristic algorithms. A variable neighborhood search heuristic algorithm （VNS） and an optimization-based heuristic algorithm （OBH） are proposed for the problem. Specifically， VNS is enhanced with the above operators， fast solution evaluation， polynomial algorithms for network flow subproblems， hash tables， etc. OBH decomposes the problem by slicing the planning horizon and makes use of the advantage of a solver which can solve small subproblems optimally and efficiently. Next， computational experiments are carried out on a set of simulated test instances with various sizes of network and maintenance activities， and a set of real-world test instances from the literature. The comparison results show that both two proposed heuristic algorithms are effective in finding feasible solutions of high quality， and the results further verify the effectiveness of algorithm components. Finally， the algorithms introduced in this paper can be well applied to the related variants， so as to provide a good foundation for problem-solving. Figures and Tables | References | Related Articles | Metrics

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Customer Behavior and Pricing Analysis Based on “Service Intelligence Queueing System” Rui Wang, Xiuli Xu 2025, 33 (8):  260-270.  doi: 10.16381/j.cnki.issn1003-207x.2022.2463 Abstract ( 9 )   HTML ( 0 )   PDF (2925KB) ( 3 )   Save With the application and development of mobile internet technology， promoting intelligent services is an inevitable path. Intelligent services have brought convenience to our daily life， but intelligent devices cannot be accepted by all social groups， and people still have doubts about their security， so the guidance of professional staff is indispensable in the use process. In addition， the function of intelligent devices is not yet perfect， and the frequency of failures is high. Once a failure occurs， it cannot be repaired in a short time， causing a large number of customers to be stranded at the service point， resulting in an unpleasant experience for customers.The customers’ equilibrium and pricing strategy in delayed repair queue with working vacation and vacation interruption combined with intelligent service mechanisms are considered. Firstly， the utility functions are established based on a linear reward-cost structure and customers’ equilibrium and socially optimal strategies are derived under different information levels. Secondly， the socially optimal strategy is calculated by using the genetic algorithm and computer simulation. By comparing with the equilibrium strategy， it concluded that selfish behavior of customers can paralyses the system， which leads to the loss of customers and is unfavorable to the overall healthy development of society. Therefore， it seeks to social welfare optimization by setting the optimal service price to regulate the benefit relationship between customers and society. Finally， taking the failure rate as an example， the impact of system parameters on performance indicators is analyzed and some suggestions are proposed for service industry managers to make decisions： （1） improve the staff’s ability to handle failures； （2） strengthen the training of employees in enterprises； （3） a reasonable pricing strategy can effectively alleviate congestion situations. Figures and Tables | References | Related Articles | Metrics

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Research on Ordering Strategy Optimization of LNG Maritime Trade Jun Zhou, Sizhou Peng, Kai Wang, Yunxiang Zhao, Guancheng Wu, Bixian Zeng 2025, 33 (8):  271-277.  doi: 10.16381/j.cnki.issn1003-207x.2022.2567 Abstract ( 10 )   HTML ( 0 )   PDF (2309KB) ( 5 )   Save Within a long-term non-negotiable purchase and sale model with inflexible contract prices of China’s LNG import trade model， large losses and intensified contradiction between domestic natural gas supply and demand occur. The optimization of LNG seaborne trade ordering strategy for reasonableness on contract execution time and order quantity is explored， as well as mitigation of domestic natural gas supply-demand contradiction. In this paper， LNG supply chain operation cost minimization is taken as the objective function， while contract execution time and ordering quantity as the decision variables. Applying consider constraints such as terminal safety storage capacity， berth quantity， demand satisfaction， quality satisfaction， etc.， LNG maritime trade ordering strategy optimization model and the CPLEX solver are implemented， so as to achieve economical LNG ordering optimization. Five major LNG import routes and a domestic LNG receiving station configuration are investigated， while the stable and fluctuating demand scenarios are set up respectively for simulation.Results indicate that not only reasonableness of contract execution time and ordering quantity decision， but also the LNG demand and client’s gas quality requirements can be fulfilled by the optimal ordering plan obtained from the optimization model of LNG maritime trade ordering strategy. Implications of effective solution and decision support for the LNG maritime trade ordering strategy optimization are put forward for good prospects for practical application. Figures and Tables | References | Related Articles | Metrics

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Competitive Pricing Strategy of Platform E-commerce Enterprises Considering Reference Point Effect and Price Commitment Xuwang Liu, Junjia Wang, Wei Qi, Xinggang Luo 2025, 33 (8):  278-288.  doi: 10.16381/j.cnki.issn1003-207x.2022.2088 Abstract ( 17 )   HTML ( 0 )   PDF (3598KB) ( 10 )   Save Under the background of platform economy， price and sales volume are the most concerned and easily available information for consumers. Based on these information， consumers form their own reference points for product-related attributes and further make purchase decisions. The research on consumer decision-making behavior considering the influence of price and sales double reference point effect is of great significance to the product pricing and marketing strategy formulation of platform enterprises. In this paper， the Hotelling model is used to construct a two-period product pricing model for competition among platform enterprises. The influence of double reference point effect on product pricing， profit and strategy choice of competitive enterprises under price commitment or non-commitment is studied. The influence mechanism of product quality difference， reference point effect intensity， unit product preference cost and other factors on enterprise profit and competition situation change is analyzed， and the two-period optimal pricing strategy of product sales is further proposed. It is found that whether the disadvantaged enterprises choose the price commitment strategy is related to the degree of influence of the reference point effect and the unit preference cost of the product. Platform e-commerce enterprises can improve consumers ' preset reference points for products by optimizing product experience and improving service quality， so as to increase the competitive advantage of enterprise products. With the goal of maximizing profits， the optimal choice of advantageous enterprises is price commitment， and the optimal choice of disadvantaged enterprises is price non-commitment. The research results can provide theoretical basis and decision support for product pricing and operation management of platform enterprises. Figures and Tables | References | Related Articles | Metrics

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Research on the Cooperation Strategy of “Trade-in” between E-commerce Shopping Platform and Recycling Platform Yi Jing, Wenqiu Zhang, Liu Cao 2025, 33 (8):  289-297.  doi: 10.16381/j.cnki.issn1003-207x.2022.1318 Abstract ( 13 )   HTML ( 0 )   PDF (2252KB) ( 7 )   Save The growing scale of waste products but the low effective recycling rate has become a major constraint to sustainable economic and social development. Driven by national policies， circular economy and information technology， recycling platforms are gradually emerging and signing “trade-in” cooperation agreements with e-commerce shopping platforms to achieve the combination of waste product recycling and new product sales. However， at present， the “trade-in” cooperation between the two platforms is still in the exploration stage， and there are still many problems that need to be clarified. Based on this， two game models are constructed for the supply chain system consisting of an e-commerce shopping platform and a recycling platform， where both parties do not implement the “trade-in” cooperation strategy or implement the “trade-in” cooperation strategy， respectively. On the basis of solving the optimal equilibrium solution， comparative analysis and parameter sensitivity analysis are conducted to study the impact of the “trade-in” cooperation strategy on the market demand structure， the recycling of waste products and the economic profit of the platform， and to explore the impact of the recycling price on the economic profit of the platform and the total profit of the system under the premise of cooperation between the two sides.The results of the study show that　when the cost of refurbishment is low， “trade-in” cooperation will expand the market demand for refurbished products， but is not conducive to direct sales of new products； “trade-in” cooperation will not affect the recycling platform's own channels and will always help expand the total amount of recycling. However， “trade-in” cooperation is not necessarily beneficial to the economic benefits of both platforms， only when the cost of acquiring new products is low can the e-commerce shopping platform profit from it， and only when the size of the used product market stock and the cost of acquiring new products are both larger or both smaller will the profits of the recycling platform increase. If both platforms decide to cooperate on “trade-in”， the economic benefits of the e-commerce shopping platform will grow as the recycling price increases， but the economic benefits of the recycling platform will tend to grow and then decrease. Figures and Tables | References | Related Articles | Metrics

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Study on Logistics Equilibrium of Automobile Parts Considering Circulating Container Mingshu Zhang, Xianhao Xu, Ruiting Yue, Yuerong Chen 2025, 33 (8):  298-307.  doi: 10.16381/j.cnki.issn1003-207x.2022.1328 Abstract ( 8 )   HTML ( 0 )   PDF (2112KB) ( 3 )   Save Returnable Transport Items （RTI） are widely used in the automobile manufacturing industry. The management of RTI plays an important role in the normal operation of the automobile parts production logistics system. Once the management of RTI is not good， such as the return of empty RTI is not timely， it may lead to the interruption of the production line and cause great economic losses. Therefore， the matching between orders and empty RTIs， the selection of parts and the return process of RTIs are considered， a semi-closed queuing network is conotructed， two indexes of order queue length and RTI utilization rate are selected to measure the influence of RTI quantity and return lead time on the balance of the system. And the AMVA algorithm is used to solve the problem. In this paper， the actual data of enterprises are used to carry out experiments. The results show that the number of RTI and the return lead time have a great influence on the system balance. Different storage policies also affect the results. The data results of sorted storage policies are better than those of random storage. Through this model， the specific RTI number and return lead time of the optimal solution can be obtained. This makes the external order of the system almost need not wait for pickup， and the utilization rate of RTI can be close to 100%， avoiding the waste of resources. Finally， the balance of parts logistics system is improved. The main contribution of this paper is to consider the influence of RTI on the balance of the system， and obtain the index of the stability of the direct reaction system through the queuing network， so as to ensure the smooth logistics operation and ensure the normal operation of the production line. Figures and Tables | References | Related Articles | Metrics

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Remanufacturing Closed-loop Supply Chain Decision Considering Retailer’s Dual Behavior Preference under Carbon Tax Regulation Yeming Dai, Shuang Yu 2025, 33 (8):  308-320.  doi: 10.16381/j.cnki.issn1003-207x.2022.1639 Abstract ( 14 )   HTML ( 0 )   PDF (3554KB) ( 6 )   Save With the development of behavioral economics， decision-makers pay more attention to the application of behavioral preference in supply chain management. However， the existing researches on supply chain management considering behavioral preferences usually only consider a single behavioral preference， without considering the superimposed effect of multiple behavioral preferences. But many behavioral experiments and researches have confirmed that decision-making models based on rational assumptions deviate from reality. And the complexity of human behavior means that people may have multiple behavioral preferences. In particular， the disadvantaged decision-makers will pay more attention to fairness and be more sensitive to their own losses. Therefore， the dual behavioral preference of the retailers with the inferior position of supply chain is introduced into the multi-echelon closed-loop supply chain composed of the manufacturers， the remanufacturers， the distributors and the retailers. Furthermore， considering that with the maturity of remanufacturing technology， the advantages of the remanufacturers are increasingly prominent， but due to the lack of market advantages， the remanufacturers often choose to cooperate with other enterprises to maximize profits when carrying out remanufacturing business. However， there is a gap in the research on the cooperation between the manufacturers and remanufacturers under the consideration of behavioral preferences. Based on this， under the supervision of carbon tax， the multi-echelon closed-loop supply chain game models of cooperation and non-cooperation between the manufacturers and remanufacturers are established， namely Model CD and Model MT. Then the influence of the retailers' dual behavioral preferences on pricing decisions of each member in the supply chain is discussed， and equilibrium analysis and comparison are conducted. The main discussions are as follows： （1） The influence of the retailers' dual behavioral preferences on the pricing of new and remanufactured products when the fair gain and loss coefficients meet different conditions； （2） The influence of carbon emission saved by remanufactured products on the pricing of new and remanufactured products； （3） The impact of carbon tax imposed by the government on the manufacturers and remanufacturers on the pricing of new and remanufactured products. The results are showed as follows： （1） the cooperation between the manufacturers and remanufacturers reduces the price of remanufactured products， but does not affect the price of new products. （2） When the retailer's profit is higher than the manufacturer's profit at the reference point， the retailer's negative inequality and loss aversion preference decrease， but positive inequality preference increases， so the upstream enterprises raise the wholesale price of products. On the contrary， the upstream enterprises reduce wholesale prices. （3） The carbon tax levied by the government increases the price of new products， reduces new products’ production and the carbon emission of the supply chain system. In addition， when the carbon emission saved by remanufacturing is large， the remanufacturer can increase the production of remanufactured products and the manufacturer can reduce the production of new products. In this case， appropriate carbon tax will encourage enterprises to actively remanufacture. The dual behavioral preferences are considered to make the research more practical. In addition， the carbon tax policy and remanufacturing considered in this paper are in line with the current green development policy， and the conclusions drawn can provide theoretical basis for the government to formulate a reasonable carbon tax policy to reduce carbon emissions and enterprises to make targeted decisions. Figures and Tables | References | Related Articles | Metrics

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Differential Game of Dynamic Closed-Loop Supply Chain Considering Consumers' Purchasing Regret Mei Duan, Zhenwei Liu, Lang Liu 2025, 33 (8):  321-330.  doi: 10.16381/j.cnki.issn1003-207x.2022.2756 Abstract ( 14 )   HTML ( 0 )   PDF (2242KB) ( 6 )   Save With the shortening of the product life cycle， the number of discarded electrical and electronic products （such as mobile phones， TV sets， etc.） has grown sharply， which has caused serious energy waste and environmental pollution. Hence， the research of closed-loop supply chain have been paid more attention by the academic circles. However， existed studies have not considered the heterogeneity between new products and reproducts， and the effect of consumer regret. In this paper， the change path of demand is depicted for refurbished innovative products when considering the purchasing regret of consumers based on the closed-loop supply chain differential game system. To deal with homogeneity between new product and remanufactured product， purchasing regret coefficient is introduced to Bass diffusion model， and then a modified Bass diffusion model is built to depict the change path of innovative products’ demand after entering the market. And based on this， a closed-loop supply chain system is considered is considered which is dominated by a manufacturer and followed by a retailer. In this system， the manufacturer recycles used products at a given ratio. Then a differential game model is built under the Stackelberg game taking the sales of product as the state variable. The optimal path of wholesale price， retail price and the maximum profit of supply chain members is obtained by using the differential game theory and the optimal control theory. By numerical examples， the optimal decisions in different market conditions are compared. The results show that： （1） The higher the consumer regret factor， the smaller the sales volume. When new products account for a large proportion of sales， more energy should be devoted to improve the satisfaction of new product users； Conversely， when remanufactured products account for a large proportion of sales， remanufactured sale should be paid more attention. （2） manufacturer’s optimal wholesale price is fixed over time horizon and will not be changed by purchasing regret of consumers. This is because the manufacturer is the leader of the supply chain. Fixed wholesale prices can reduce uncertainty for retailers. Then retailers can adjust the sales volume by adjusting the price； （3） The seller adjusts the price of the product according to the sales volume of each stage. During the whole process， retail price first rises then descends. （4） the higher regret coefficient the lower retail price and the profit of manufacturer and retailer. Hence， both manufacturer and retailer should try best to improve customer satisfaction. our research shows that both manufacturers and retailers should strive to improve consumers' satisfaction with their products. Figures and Tables | References | Related Articles | Metrics

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Analysis of a Manufacturer's Encroachment Strategy and Pricing Timing Decision Considering Power Structure Lihao Zhang, Luyu Chang, Cheng Zhang 2025, 33 (8):  331-339.  doi: 10.16381/j.cnki.issn1003-207x.2022.2736 Abstract ( 12 )   HTML ( 0 )   PDF (3069KB) ( 5 )   Save With the increased development of the Internet and e-commerce， manufacturer encroachment has become more popular， yet few literature focuses on the interaction of power structure and pricing timing decisions on manufacturer encroachment. Three power structures are considered， namely， MS power structure （i.e.， Manufacturer -Stackelberg game）， RS power structure （i.e.， Retailer-Stackelberg game） and VN power structure （i.e.， Vertical-Nash game）. And supply chain members have three pricing timing strategies （the manufacturer decides the direct channel’s retail price earlier than/simultaneously with/ later than the retailer determines the retail channel’s retail price）. Further， the profit models of supply chain members considering three power structures and three pricing timing strategies under two scenarios of encroachment and non-encroachment are constructed respectively. By using backward induction， the optimal price and revenue of supply chain members are solved， and further， the equilibrium pricing timing strategy of supply chain members and the optimal encroachment decision of the manufacturer can be obtained. It is found that under the MS or VN structure， the optimal pricing timing strategies of supply chain members are always in conflict， but Pareto improvement can achieve the equilibrium pricing timing strategy； under the MS （VN） structure， the manufacturer should decide the retail price simultaneously with （earlier than or simultaneously with） the retailer. However， under the RS structure， the optimal pricing timing strategies of members are always consistent and all three pricing timing strategies are the optimal strategy. In addition， encroachment is not always detrimental to the retailer. When the basic market demand of direct channels is small， encroachment is beneficial to the retailer. The reason is that with price competition， encroachment triggers a negative wholesale price effect and a positive channel competition effect， which makes the encroachment has a positive and negative impact on the retailer respectively. Surprisingly， a larger market power does not always provide members with greater advantages. With the increase of basic market demand for the direct channel， the manufacturer is increasingly willing to encroach under the RS structure， while the retailer gains higher profit with encroachment under the MS structure. Figures and Tables | References | Related Articles | Metrics

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Decision-making Analysis of Power Battery Recycling under Carbon Cap-and-trade Mechanism and Subsidy Policy Wenqi Wu, Ming Zhang 2025, 33 (8):  340-354.  doi: 10.16381/j.cnki.issn1003-207x.2023.1828 Abstract ( 19 )   HTML ( 0 )   PDF (7599KB) ( 5 )   Save To promote the power battery recycling work and promote the carbon emission reduction of enterprises， the government launched carbon cap-and-trade mechanism and subsidy policy， to explore the impact of the government subsidy strategy choice on the decision-making of the power battery recycling supply chain under cap-and-trade mechanism， a closed-loop supply chain led by power battery manufacturer and composed of the vehicle manufacturer and third-party recycler is constructed， and the optimal decision-making of the power battery recycling supply chain under the three strategies of subsidized power battery manufacturer is studied. In addition， three recycling models are constructed： single-channel monopoly recycling， mixed recycling and alliance recycling， and the supply chain decision-making， profit and recycling rate under different recycling models are compared. Results show that （1） The forward supply chain decision of power battery recycling is affected by the carbon cap-and-trade mechanism and recycling models， while the subsidy policy only affects the reverse supply chain decision； （2） The carbon cap-and-trade mechanism improves the profits of the power battery recycling supply chain， showing that the increase of carbon quota and carbon price is conducive to the increase of the profit of the power battery recycling supply chain， while the increase of carbon price leads to poor recovery performance； （3） Subsidy policies improve supply chain profits and recycling rates， and the recycling rate of subsidized power battery manufacturers or recyclers is always better than that of subsidized consumers； （4） The impact of carbon cap-and-trade mechanism and subsidy policy on the supply chain is not affected by the recycling mode， but the supply chain profit and recovery rate of the alliance recycling mode are better than that of single-channel recycling and mixed recycling. Figures and Tables | References | Related Articles | Metrics

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Research on Multi-Agent Decision-Making in Carbon Data Quality Supervision under MRV Mechanism: A Blockchain Technology Empowerment Perspective Xichen Lyu, Yinfeng Tian, Shihai Tian, Jiayuan Liu, Qingwei Kong 2025, 33 (8):  355-368.  doi: 10.16381/j.cnki.issn1003-207x.2024.0953 Abstract ( 13 )   HTML ( 0 )   PDF (4899KB) ( 6 )   Save Carbon data quality is the lifeline of carbon trading and holds significant implications in realizing the “dual-carbon” strategy objectives. Consequently， in light of the existing risks associated with regulating carbon data quality under the MRV mechanism， the optimal behavior decision-making empowered by blockchain technology for multiple agents is explored. Initially， the enabling mechanism of blockchain technology on the behavior decisions of multiple agents in the context of carbon data quality supervision is dissected. Subsequently， an evolutionary game theory and method are utilized to construct a mixed-strategy game model involving government regulatory authorities， emission-capping enterprises， and third-party carbon verification agencies. By analyzing the stability of the strategies of the various agents and the equilibrium of the system， the effective conditions for collaborative carbon data quality management with the participation of multiple agents empowered by blockchain are solved. Furthermore， referring to policy documents and typical cases， the impact of key parameters on the behavioral evolution of participants is further explored using numerical simulation methods. The research findings indicate that： （1） The construction of an on-chain supervision system with multiple interactions of different nodes supported by blockchain can effectively alleviate the regulatory problems of carbon data quality. With the widespread adoption of blockchain technology and the strengthening of public environmental awareness， the implementation of blockchain regulatory models by the government is a prerequisite and trend for promoting joint governance by multiple parties. （2） Emission-capping enterprises have a subjective tendency to engage in collusion and speculation with the verification agencies. It is necessary to significantly increase the cost of fraud and the threshold for collusion on the basis of fiscal and tax incentives and on-chain subsidies， in order to effectively regulate the integrity of emission-capping enterprises in reducing emissions. （3） The focus of strategy selection for third-party carbon verification agencies is credit cost， but there exists a "failure interval" for credit cost parameters， which may lead to a game deadlock in system strategy evolution. A robust mechanism of multiple reviews and credit penalties is conducive to the verification institutions actively fulfilling their responsibilities and overcoming this situation. This research can help mitigate the risk of carbon data quality supervision under the MRV mechanism， and provide a valuable reference for the effective guidance of behavioral decision-making among carbon trading-related subjects and the regulation of data quality management within the carbon market. Figures and Tables | References | Related Articles | Metrics