Table of Content

25 December 2025, Volume 34 Issue 12

Previous Issue   

Theory Analysis and Methodology Study

Consensus Decision-making Model Based on Social Link Probability and Group Selection

LI Yueyuan, WU Zhibin

2025, 34(12):  1-8.  DOI: 10.12005/orms.2025.0368

Asbtract ( )   PDF (1259KB) ( )  

References | Related Articles | Metrics

In social network group decision-making, decision makers rely on social relationships and interactions to achieve collective decisions. Social relationships are crucial in this process as they not only serve as channels for information exchange but also influence the opinions and behaviors of the members. Furthermore, strong social relationships foster trust and cooperation among decision makers, thereby enhancing the efficiency of the decision-making process and increasing the acceptability of the outcomes. Therefore, many studies have focused on the exploration of the unknown social relationships to obtain a relatively complete social network, aiming at providing a powerful tool for analyzing individual interactions. But they have failed to adequately capture the randomness and uncertainty of decision makers’ social behaviors as they believe that a connection can be established between any two decision makers as long as there is an accessible path. Additionally, in the social network group decision-making, decision makers finally reach a consensus, or have opinions of polarization or divide after discussion and consultation. This is a dynamic process accompanied by the emergence of new opinions and the establishment of new social relationships. Differences in opinion among decision makers are inevitable due to the potential conflicts of interests. Consequently, some consensus decision-making models based on social relationships have been proposed, but they do not effectively model the potential interactions between decision makers. They also ignore the fact that the establishment of social relationships and opinion adjustments are a parallel process in actual decision-making. Given the above, it is necessary to propose a new social network group decision-making model. It includes a new social network completeness analysis method and opinion adjustment process to fully leverage the role of social relationships and potential individual interactions in promoting consensus.
Existing research on social network group decision-making lacks the analysis of decision-makers’ social paradigms. Additionally, current consensus adjustment processes fail to capture potential interactions between decision-makers and strangers. To address both issues, this paper proposes a consensus decision-making model based on social link probability and group selection. It guides decision makers with conflict opinions to communicate effectively based on analyzing the mutual influence between social relationship establishment and opinion adjustment, so as to eliminate opinion difference and promote consensus. The main contributions are as follows. First, the social link probability is used to quantify the probability of decision makers establishing social relationship through direct or indirect interactions, capturing the uncertainty and dynamics of social network. The concept of social link probability is derived from the link prediction in network science, which aims to predict the probability of forming connections between unlinked nodes based on known node and network structure. Then an integrated rule for social link probability is proposed, considering the choice of social paradigms to estimate the probability of forming new social relationships between decision makers. Second, a multi-objective consensus feedback model based on social link probability is developed for maximizing social link probability and prioritizing the resolution of opinion differences. The results are used to update the social network and adjust the opinions of conflicting individuals, thereby enhancing group consensus.
The feasibility and applicability of the model are demonstrated through a numerical example. This paper compares the model with existing consensus adjustment models, providing a detailed analysis of its performance. The comparison highlights the rationality and advantages of the proposed model from the range of final opinions, required adjustment rounds, the number of individuals with modified opinions, total adjustment degree and the process of the social network analysis. A series of simulation experiments are conducted to verify the stability and superiority of the model. The results indicate that the model not only achieves good consensus convergence but also has a superior ability to adapt to social networks with varying densities. This adaptability is crucial as it results in a higher rate for consensus achievement, making the model highly effective in diverse scenarios.

Economic and Statistical Optimization Design of VSI-EWMA-NPS Chart for Monitoring Process Scale Parameter

WANG Haiyu, GAO Xiaoying

2025, 34(12):  9-16.  DOI: 10.12005/orms.2025.0369

Asbtract ( )   PDF (1541KB) ( )  

References | Related Articles | Metrics

As an important tool of statistical process control, control chart has been widely used in the monitoring process of production and service. In the actual production process, for systematic reasons, the scale parameters often change but the position remains unchanged, which will lead to the overall decline in process quality, so it is necessary to monitor the scale parameters. In many production processes, some simple and rapid detection methods are often used to judge the process quality, such as measuring the diameter of cigarettes with a cigarette measuring board and measuring the concentration of alumina with a test paper. These methods are simple and rapid. In this paper, this kind of method is called rapid detection, and it is applied to the control chart.
In the continuous production process of monitoring, if the sampling detection cost is high or the detection time is long, the counting control chart can be considered for monitoring. EWMA chart uses both the data information of the current sample and the data information of the historical sample, which has high monitoring efficiency for small and medium-sized deviations, but the cost of sampling detection is high; NPS diagram can effectively reduce the detection cost, but it is not sensitive to process fluctuation. Dynamic control chart can improve monitoring efficiency and save costs by adjusting sampling strategy, but the degree of improvement and saving is limited. Therefore, through the application of rapid detection method, this paper constructs a VSI-EWMA-NPS scale parameter control chart which can effectively monitor the scale parameters of continuous production process. In order to consider the monitoring efficiency and quality cost of VSI-EWMA-NPS control chart, the economic statistical optimization model of VSI-EWMA-NPS scale parameter control chart is constructed with the Average Product Length (APL) as the statistical evaluation index and the average quality cost per unit product as the economic evaluation index. The optimization model of this paper is illustrated by a case of graphite gasket production. Through the combination and analysis of non-inferior solutions, the relationship among sample size, sampling interval, quality cost per unit time (C) and average product length (APL₁) in out-of-control state is studied. Through sensitivity analysis, the influence of model parameters on economic indicators and statistical indicators is studied. Finally, the optimization method of economic statistics proposed in this paper is compared with the existing models, and the results show that the model in this paper is advanced in economy and statistics.
In this study, it is assumed that the product quality characteristic values obey the normal distribution, but with the development of intelligent manufacturing, the product production mode becomes more flexible and refined. In the process of product production, the process quality characteristic value will follow some non-normal distribution, and even sometimes it is difficult to judge the specific distribution type it obeys. In view of this kind of production process, if the quality monitoring method based on the assumption of normal distribution is still used to monitor the product production in real time, it will lead to large errors and misjudge the production process. Therefore, it is necessary to consider the monitoring of non-normal data by VSI-EWMA-NPS control chart in the subsequent research.

Robust Two-stage Minimum Cost Consensus Model Based on Asymmetric Costs

LI Huanhuan, JI Ying, QU Shaojian

2025, 34(12):  17-24.  DOI: 10.12005/orms.2025.0370

Asbtract ( )   PDF (1664KB) ( )  

References | Related Articles | Metrics

Consensus plays a crucial role in group decision-making.For decision-making problems that need to be solved in reality, the opinions of decision-makers may differ greatly from each other, and it is difficult to obtain an acceptable solution directly. For possible future situations, decision-makers will prepare multiple decision options in advance, but cannot accurately predict the actual situation. In addition, due to the interference of external factors, it is difficult to obtain the exact value of the adjustment cost in each case. This suggests that it is challenging for decision-makers to obtain real data, and the obtained data are often inaccurate. In the application of real-world decision problems, the decision-makers often don’t know the exact values of the parameters in the optimization model before giving the initial opinions. In order to effectively address the individual characteristics of uncertainty and complexity, two-stage stochastic programming and robust optimization are applied to deal with uncertainty in decision-making problems, and both techniques excel in solving such uncertainty problems. The former considers stochastic scenarios during the decision-making process, and it uses expectation as a preference criterion to minimize the expected total cost of obtaining the preferred solution. The latter considers parameter uptake throughout the decision-making process, where uncertainty can be modeled by the worst-case scenario in the cost uncertainty set, and finds a stable solution that satisfies all the optimization constraints in the worst-case scenario.
Firstly, the key influencing factors of the uncertain decision-making environment are analyzed. We generate several stochastic scenarios and introduce perturbations to unit costs in each scenario. This is used to improve a situation where it is difficult to accurately carry out modeling based on model-driven approaches. Secondly, robust optimization and stochastic programming are combined to focus on the constraints of asymmetric adjustment costs and uncertain decision environments. By introducing box set, polyhedral set and intersection set, a robust two-stage minimum cost consensus model based on asymmetric adjustment costs is constructed. The specific robust counterpart model under each uncertainty set is given, which provides the corresponding strategies in terms of cost compensation and optimal opinion adjustment. Finally, by combining the survey data of carbon emission reduction governance under uncertain background, the effectiveness of the robust two-stage minimum cost consensus model in the carbon emission reduction governance problem is verified. Based on this, the optimal opinion adjustment strategy of the experts representing each company in the manufacturing industry in uncertain environments is proposed. In order to better illustrate the effectiveness of the proposed model, it is compared with the previous models in the numerical experiment section, and it is found that the novel consensus model under the intersection set can cost less to reach consensus. However, if the decision-makers are more conservative, they can refer to the model under the box set.
This paper is concerned with the impact of uncertainty influences and stochastic scenarios on consensus costs and consensus in the context of asymmetric adjustment costs. A robust two-stage minimum cost consensus model that considers asymmetric cost uncertainty is constructed. The experimental results show that the novel model is more suitable for uncertain decision-making environments and can help decision-makers obtain more reliable choices. This paper does not take into account other factors that influence uncertainty in real-world decision-making situations. Future research can further investigate the impact of other uncertainty factors on total cost and consensus by considering extended models with uncertainty and information asymmetry, such as initial opinions, total adjustment cost thresholds and experts’ tolerance levels.

Alternating Offers Bargaining Games with k-period Backward Bounded Rational Players

YANG Guangjing, HOU Dongshuang, SUN Panfei

2025, 34(12):  25-30.  DOI: 10.12005/orms.2025.0371

Asbtract ( )   PDF (1121KB) ( )  

References | Related Articles | Metrics

Bargaining games have long been a central focus in game theory research due to their wide applicability in representing various economic, political and social interactions. One prominent example is the alternating offers bargaining game, initially proposed by Ariel Rubinstein in 1982. In Rubinstein’s model, two players take turns to offer terms of agreement to each other indefinitely until an agreement is reached. The players discount future payoffs, which provides an incentive to reach an agreement sooner. The analysis of subgame perfect equilibrium in Rubinstein’s model of alternating offers utilizes backward induction, which is a form of reasoning used extensively in game theory, especially in finite extensive form games. To find the subgame perfect equilibrium of the game, it is assumed that the players make decisions that maximize their own payoff, taking into account the anticipated decisions of other players. This method assumes that each player anticipates that all players will act rationally in the future and employs this anticipation to determine their current optimal action. Hence, players are assumed to be fully rational, while also assuming that they believe all other players are fully rational.
However, these assumptions may not hold in many real-world situations. Players might not be entirely rational due to cognitive limitations, lack of information, limited memory capacity, or other factors. They may also doubt that other players are fully rational. Following Herbert Simon’s introduction of bounded rationality, scholars began to explore how such cognitive limitations affect decision-making processes. Most literature to date abstracts bounded rationality as a limitation in participants’ forward-looking capabilities. Although they provide important perspectives on how bounded rationality influences decision-making processes, these theories show certain limitations when studying finite-horizon alternating bargaining games. In such games, when the number of bargaining periods is fixed, rational participants typically use backward induction to devise strategies, rather than merely observe future outcomes. Typically, participants with bounded rationality, after being able to deduce the optimal strategies for several periods through backward reasoning, may deviate from rational behavior due to changes in circumstances, computational limitations, or memory constraints, thus leading to irrational behaviors. In practical game scenarios, this manifestation of bounded rationality is usually observed when participants are able to make fully rational decisions only in smaller-scale subgames.
In light of these complexities and the limitations, our research aims to explore the dynamics of a finite-period alternating offers bargaining game, where players exhibit this specific form of bounded rationality, namely k-period backward bounded rationality. This refers to players who can only perform backward induction reasoning up to k-periods. We construct a model that captures the essence of k-period backward bounded rationality within a finite alternating offers bargaining game. A comprehensive formalization and characterization of k-period backward bounded rationality is then presented. Moreover, we turn our attention to a bargaining game that pairs a fully rational player with a player who is only rational for the final k periods of the game, where k is less than the total number of periods m. The fully rational participant is designated as player 1 and the k-period backward bounded rational player is designated as player 2. For player 2, we posit that their behavior in the initial m-k periods is characterized by unpredictability, with both proposals and acceptances distributed uniformly at random.
Our main results depend on which player is the initial proposer. Specifically, when player 2 is the initial proposer, we find that the expected utility of player 1 is always above 1/2 and increases with the discount factor. Player 2 has an expected utility that remains below 1/2. This indicates an inherent disadvantage for the player with bounded rationality when making the initial offer. On the flip side, when player 1 is the initial proposer, the expected utilities of both players trend towards 1/4 as the discount factor approaches 0. In the opposing limit, as the discount factor approaches 1 and the number of periods of irrationality m-k for the player 2 is sufficiently large, the expected utility of player 1 asymptotically approaches 1, while that of player 2 trends towards 0. Finally, the main results are validated through computer numerical simulations to ensure the accuracy of the theoretical analysis. Surprisingly, numerical simulations reveal that, in certain scenarios, the expected utility of the k-period backward bounded rational player surpasses that of the fully rational player, a phenomenon that needs further investigation.

Research on Supply Chain CSR Undertaking Strategies Based on Corporate Donation and Consumer Quality Preferences

SHANG Wenfang, REN Yanqiu, LI Tao

2025, 34(12):  31-38.  DOI: 10.12005/orms.2025.0372

Asbtract ( )   PDF (1659KB) ( )  

References | Related Articles | Metrics

Corporate donations are a manifestation of their social responsibility, which helps to enhance brand image and stimulate market demand. Many companies use them as a marketing strategy. However, rational consumers may not overlook their preference for product quality due to corporate donation events. So, it is worth conducting in-depth research on how consumers’ attention and quality preferences towards donations affect market demand, how high the quality and donation settings are for the benefit of enterprises, and whether the dominant position of donation subjects affects quality and donation decisions.
This paper combs through the literature on the three dimensions of product quality decision-making in the context of supply chain, the interaction between CSR and quality and the impact of power structure on CSR and quality, and finds that few studies have examined the impact of CSR and quality level on market demand from the perspective of donation at the same time, and even fewer have considered the change in the CSR bearer body and the rational adjustment of the consumers’ attention to corporate donation. Based on this, the quality level and donation intensity are regarded as binary attributes of the product, the Hotelling model is introduced to portray the two-dimensional preference characteristics of consumers, the rational adjustment of consumers’ attention to donations is introduced into the market demand function, and the impacts of changes in the CSR bearer are investigated by integrating the three kinds of power structures: manufacturer-dominated, retailer-dominated and power-balanced. The results are compared with those of centralized decision-making to analyze the advantages and disadvantages of centralized decision-making, and the main conclusions and management insights of this paper are summarized with the help of numerical experiments.
The results show that: (1)The more sensitive consumers are, the greater the positive effect of donations, and the more it can improve the enthusiasm of enterprises for fulfilling social responsibility and the overall performance level of the chain. (2)When the power structure is asymmetric, it will be more favorable for follower firms to take on CSR; when the power structure is symmetric, each member will want the other to take on CSR. (3)When consumers are more sensitive to donations, an increase in quality sensitivity will lead to a decrease in CSR sensitivity, a weakening of the positive effect of CSR and a decrease in the supply chain’s performance level; when consumers are less sensitive to donation, an increase in quality sensitivity will lead to a decrease in the level of donations. However, the quality positive effect is more obvious, prompting the supply chain performance level to increase.
In addition, this paper can provide the following management insights: (1)In special circumstances such as natural disasters, donations provide psychological care and economic assistance to disaster-stricken areas, and have a CSR demonstration effect on other enterprises, which is beneficial for enhancing corporate image and brand promotion. (2)Donations have a positive effect on stimulating market demand and increasing corporate profits, but this effect will gradually weaken, and quality first should become the strategic goal of enterprises. But if consumers are more concerned about donations, companies can also win through donation marketing. (3)Enterprises should enhance their discourse power and influence in the supply chain by improving their own strength and management level, while promoting subordinate enterprises to fulfill CSR through incentive and cooperation mechanisms to optimize their own and overall profits. Enterprises can also establish strategic partnerships with companies with equivalent power, striving to achieve a “matching door” and flexibly deciding who will undertake CSR based on actual circumstances.

Equilibrium Investment Strategy for DC Pension Funds with a Return of Premiums Clause under Inflation Risk

BIAN Lihua

2025, 34(12):  39-46.  DOI: 10.12005/orms.2025.0373

Asbtract ( )   PDF (1193KB) ( )  

References | Related Articles | Metrics

Nowadays, the pension gap is growing large with the deepening of the global aging. Hence, it is quite urgent to investigate the investment management of pension funds. As the defined contribution (DC) pension plan has an advantage in easing the pressure of the social security system by transferring the investment risk and longevity risk from sponsors to pension members, it has become the direction of the global pension system reform. Therefore, the asset allocation for DC pension plans has attracted much attention in recent years. Mean-variance model (MARKOWITZ,1952) can balance the returns and risks of investment, which makes it an important criterion to study the asset allocation for DC pension funds. However, because the “variance” term in the mean-variance objective function is nonlinear, the mean-variance criterion lacks the iterated expected property. Thus, the optimal investment strategy under the mean-variance criterion (known as the pre-commitment investment strategy) is not time-consistent. Nevertheless, rational investors want to obtain an optimal strategy at any time point, i.e., the time-consistent strategy. It is gratifying that some scholars used game theoretical approach to deal with the time-inconsistent optimization problem and obtained a so-called equilibrium investment strategy, which is the desired time-consistent investment strategy. Drawing on this method, we study the equilibrium investment strategy for DC pension funds in our work.
To protect the rights of pension members who die before retirement, we introduce a return of premiums clause: all of the premiums contributed by dead members can be withdrawn by their heirs. We assume that the financial market consists of one risk-free asset and multiple risky assets. Since the time horizon of the pension plan is generally very long, it is also necessary to consider the impact of the inflation risk. Firstly, in the non-cooperative game framework, the equilibrium strategy is defined. Secondly, using the extended Bellman equation and matrix representation techniques, we obtain the analytical expressions for the equilibrium strategy, the equilibrium value function and the equilibrium efficient frontier. Thirdly, two special cases of our model are discussed. Finally, using numerical examples based on real data from the Chinese market, we analyze the impact of inflation risk and return of premiums clauses on the equilibrium investment strategy and the corresponding efficient frontier.
The theoretical results show that: (1)at any time, the pension manager will invest less wealth in the risky assets if his risk aversion level becomes larger; (2)at any time, the well-known two funds separation theorem holds; (3)the equilibrium investment strategy at any time depends on the current wealth and the return of premiums clause. Compared with previous studies (e.g. BIAN et al., 2018), this result is more practical.
The numerical results show that: (1)considering the return of premiums clause will make the pension manger more cautious about investing in risky assets; (2)if the inflation risk is ignored, the purchasing power of the normal wealth will be overestimated, which is adverse to the financial planning of the pension plan; (3)to obtain the same expected terminal wealth, the pension manager faces more risk when the return of premiums clause and the inflation risk are considered.

Multi-compartment Multi-type Vehicle Routing Problem of Fresh Commodity Logistics Distribution with Temperature Control

WANG Yong, XIE Hongxia, LUO Siyu

2025, 34(12):  47-55.  DOI: 10.12005/orms.2025.0374

Asbtract ( )   PDF (1714KB) ( )  

References | Related Articles | Metrics

The demand for fresh commodities has been growing continuously in recent years, with the improvement of life quality and high demands for a healthy diet. Accordingly, the fresh commodity logistics distribution business is constantly expanding due to the requirements of fresh commodities. However, due to the characteristics of perishability, timeliness and temperature control heterogeneity of fresh commodities, the fresh commodity logistics delivery demand is notably more than those of other commodities. As different types of fresh commodities are different in temperature control conditions suitable for storage and the traditional single compartment, a single-type refrigerated vehicle distribution mode fails to meet the distribution demand of multi-type fresh commodities. In the fresh commodity logistics delivery process, multi-compartment multi-type refrigerated vehicles can effectively fulfill the temperature control conditions of multi-type fresh commodities and flexibly respond to the diverse distribution demand, contributing to the shared scheduling of refrigerated vehicles in different service periods and reducing the fresh logistics operating cost. Therefore, in view of the deficiencies of the fresh commodity logistics distribution vehicle routing problem research in the combination of temperature control of fresh commodity and multi-compartment multi-type vehicle distribution, a fresh commodity logistics distribution vehicle routing problem integrating temperature control and multi-compartment multi-type vehicle distribution optimization is proposed.
In the first part, considering the perishability of fresh commodity and the timeliness characteristic of the delivery process, a bi-objective optimization model with the minimum fresh logistics operating cost, including distribution cost, vehicle rental cost, temperature control cost, value loss of fresh commodity, penalty cost for violating the time window and the minimum number of refrigerated distribution vehicles, is proposed.
In the second part, a NI-MOACO algorithm is designed to solve the developed model. The NI algorithm is applied to generate the initial solution by combining the geographic locations of the fresh distribution center and the customers. In MOACO, integrating the convergence and diversity evaluation values of optimized solutions, the adaptive pheromone updating mechanism strengthens the global search and optimization performance of the solution space. Besides, based on the customer traversal sequence, the number of compartments and loading restrictions, a vehicle-type-matching strategy segments the routes and matches the vehicle types according to the loading rates. In addition, a vehicle-sharing strategy is designed to achieve the sharing of refrigerated distribution vehicles among multiple service periods. Then, the NI-MOACO algorithm is compared with multi-objective particle swarm algorithm, non-dominated sorting genetic algorithm and multi-objective variable neighborhood search algorithm in 10 groups of case data, and then the fresh logistics operating cost, the number of refrigerated distribution vehicles and computation time are compared and analyzed to verify the effectiveness and stability of the proposed model and algorithm.
In summary, the proposed model and algorithm are demonstrated with the real-world data of a fresh commodity logistics and distribution enterprise in Chongqing, China. The changes in value loss, temperature control cost, fresh logistics operating cost, and the number of refrigerated distribution vehicles before and after the optimization are explored. The results show that flexible scheduling of multi-compartment multi-type refrigerated distribution vehicles and consideration of shared vehicles among multiple service periods can effectively respond to the distribution demand for fresh commodities, reduce fresh logistics operating costs and the number of refrigerated distribution vehicles, and promote the efficiency of the fresh logistics delivery network. Furthermore, this study compares and analyzes the optimization results for four different compartment capacities, and the results suggest that 500kg is the optimal compartment capacity, which is preferable to the other compartment capacities in fresh logistics operating costs and the number of vehicles. This study provides methodological references and decision support for fresh commodity logistics distribution enterprises in multi-compartment multi-type vehicle distribution scheduling.

New Exact Algorithms for Minimum Total Tardiness on Single Machine Scheduling

SU Zhixiong, YUAN Mengdi, WEI Hanying

2025, 34(12):  56-62.  DOI: 10.12005/orms.2025.0375

Asbtract ( )   PDF (1130KB) ( )  

References | Related Articles | Metrics

Scheduling is motivated by questions that arise generally in all situations in which scarce resources have to be allocated to activities over time, such as production planning, balancing processes sent to compute nodes, and telecommunication. One of the most representatives is the machine scheduling. Some simplest and most studied scheduling problems involve due date-based objectives on a single machine. These problems deal with scheduling multiple tasks that compete for service on a single resource, or with a machine to meet some objective concerning due dates of tasks. A frequently encountered due date-based objective is to minimize the total tardiness of all the tasks where total tardiness scheduling models have received considerable and increasing attention from the scheduling community, due to their practical importance and relevance. The total tardiness problem with release times on a single machine is NP-hard in the strong sense. This paper focuses on single machine scheduling problem with release times and total tardiness.
In order to obtain the optimal solution of the considered single machine scheduling with release times and total tardiness, this paper develops approaches of mixed integer programming formulations and optimization algorithms. The mixed integer programming formulations for scheduling problems are often classified based on the choice of the decision variables. The different decision variables used to distinguish different scheduling formulations are: (i) completion time variables, (ii) time index variables, (iii) linear ordering variables, and (iv) assignment and positional date variables. The Formulation (i) often appears in textbooks and other literature that simply formulate/describe the problem and it clearly does not generally perform well in practice. The Formulation (iv) creates a potential to use recent advancements found in the integer programming literature. This paper analyzes the relationships between the sequence position of each task on the single machine and the tardiness of the task. Based on the relationships, this paper formulates a new improved Formulation (iv) for the considered single machine scheduling based on binary variable to decide the sequence processing position of each job on the machine.
This paper explores potential to use recent advancements through analyzing the structure characteristics of the above improved Formulation (iv), and then applies integer optimization theories and methods, such as the Dantzig-Wolfe decomposition, to optimize the formulations. The decomposition-based approaches have great convergence. This paper further proves the strong duality of both of the muster-and sub-models generated by the Dantzig-Wolfe decomposition for the improved Formulation (iv), which means that the linear relaxations of the two models still keep the optimality. Based on the above findings, this paper presents simpler exact pseudo-polynomial time algorithms to compute the optimal solution of the improved Formulation (iv) much more efficiently. Through computational experiments, this paper verifies that the proposed algorithms can be applied to compute the optimal solutions of instances even containing more than 1000 tasks in 2000 seconds. The experiments evaluate the accuracy and much more competitive efficiency of the new algorithms.
The relationships between the sequence position of each mask on the single machine and the tardiness of the mask are the foundations to formulate the improved Formulation (iv) with more potential optimization. Furthermore, for the improved Formulation (iv), the Dantzig-Wolfe decomposition even can be regarded as the appropriative method: it not only achieves the best computational convergence but also keeps the optimality of the primal formulation. Inspired by these conclusions, the above relationships and the methods of formulating the above Formulation (iv) may also create better potentials for optimizing the parallel machine scheduling with release times and total tardiness. The future research of this paper will focus on the parallel machine scheduling.

A Method for Identifying Important Nodes Based on Hyperedge Influence

LIU Guo, DAI Shijie, LI Lingyu, ZHU Jie

2025, 34(12):  63-69.  DOI: 10.12005/orms.2025.0376

Asbtract ( )   PDF (1184KB) ( )  

References | Related Articles | Metrics

In the real world, many systems can be abstracted as ordinary networks, which consist of nodes and edges. Nodes in a network represent the elements of a system, and edges connecting nodes represent the relationships between elements. The importance identification of nodes is a primary branch of network research aimed at recognizing nodes that play crucial roles in network structure and resources transfer processes. This is essential for a deeper understanding and optimization of networks, enabling effective management with significant value.
In ordinary networks, “edges” typically connect only two nodes. When faced with complex interactions among multiple elements, crucial higher-order information may be lost. This loss makes it difficult to effectively characterize relationships among internal nodes within the structure. Consequently, this difficulty leads to distortions in mapping to real-world scenarios. In such scenarios, hypernetworks have emerged as vital tools in the study of complex networks. Compared to ordinary networks, “hyperedges” in hypernetworks can include any number of nodes, reflecting high-order complex relationships among multiple nodes.
Generally, there are five metrics to evaluate the importance of nodes in a network: degree centrality, closeness centrality, betweenness centrality, K-shell index and eigenvector centrality. Specifically, in the context of identifying important nodes in hypernetworks, a common metric is node hyperdegree, which measures node importance based on the number of hyperedges to which the node is connected. Findings from research on identifying important nodes in hypernetworks and ordinary networks indicate that identifying important nodes in hypernetworks draws inspiration from the principles of node identification in ordinary networks, primarily starting from node attributes to identify important nodes. However, considering the changing nature of hyperedges in hypernetworks compared to ordinary networks, the identification of important nodes in hypernetworks must comprehensively consider the influence of hyperedges.
While some studies have considered the quantity/differences of hyperedges in identifying important nodes, they have overlooked the influence of hyperedges. For instance, variations in the topological structure of hyperedges in a network lead to differences in their influence, consequently affecting the importance of nodes within them. Failure to consider this scenario in identifying important nodes may result in distorted outcomes.
To address this issue, this study proposes a method for identifying important nodes in hypernetworks based on the influence of hyperedges. The method initially evaluates the influence of hyperedges in the network through external dominance and internal control. External dominance measures the ability of a hyperedge to establish connections with hyperedges that easily connect to others considered to have higher influence. This includes global dominance based on the distance between hyperedges and local dominance where nodes within the hyperedge act as bridges. Internal control measures the network control capability brought by the number of nodes within a hyperedge, with hyperedges containing more nodes considered to have greater influence. Subsequently, based on the set of hyperedge influence vectors obtained, the method calculates node importance using an adjacency matrix and identifies important nodes accordingly. Its advantage lies in considering not only the impact of the quantity of hyperedges on node importance but also the comprehensive influence of hyperedges on node importance. Finally, to validate the effectiveness of this research method, it is applied to sample hypernetworks and real hypernetworks and compared with other existing methods such as K-shell decomposition, hyperdegree value and core degree centrality value. Comparative results indicate that the proposed method can more accurately identify important nodes with higher distinctiveness, confirming the effectiveness of this research method. This study provides crucial references for a deeper understanding of hypernetwork structures, optimizing network layouts and resources allocation.

A Two-layer Model and Algorithm for Emergency Medical Facility Siting-Casualty Transfer Considering Casualty Psychology

ZHAGN Yipeng, LIU Yong, MA Liang

2025, 34(12):  70-77.  DOI: 10.12005/orms.2025.0377

Asbtract ( )   PDF (1081KB) ( )  

References | Related Articles | Metrics

Earthquake disasters were frequent in recent years, causing significant economic losses and casualties. The outbreak of massive earthquakes has brought about a large number of casualties, resulting in a high demand for emergency relief in a short period of time. Therefore, in the face of sudden earthquake disaster, how to carry out timely emergency rescue in a short period of time, rational arrangement of emergency medical facilities, and efficient and orderly organization of casualty transfer is particularly important. Existing studies are mostly from the perspective of vehicle scheduling to the total mileage and time of casualty transfer, the survival of casualties, and the total cost of the program as the objective function of the problem modeling and solving, but less consider the psychological state of the casualty’s impact on the effectiveness of the rescue; at the same time, the current research focuses on the location of separate medical facilities or casualty transfer problems, but rarely on the integration of the two problems research.
Based on this, this paper comprehensively considers the deterioration of injuries, convoys and helicopters involved in casualty transfer, road damage and other factors, to construct a two-layer multi-objective optimization model. The upper-level objective is to minimize the total time for transporting medical supplies and transferring casualties, while the lower-level objective is to maximize the cumulative survival rate of casualties and minimize the cost of psychological suffering of casualties. By adopting the trauma index scoring method, the model classifies the injured waiting for rescue at each rescue point after the earthquake into two categories, light and heavy, and assigns the corresponding rescue priority levels. The injury deterioration function and the casualty survival function are also proposed to portray the survival rate of casualties who are treated at different time. We consider that in the process of casualty transfer, while waiting for the vehicle to rescue, the injured have anxiety, despair and other negative feelings, which will negatively affect the earthquake rescue and deteriorate their physical conditions. This paper portrays the psychological costs of casualties through the casualty psychological ordeal cost function.
Given that the model is an NP-hard problem, an improved human learning algorithm is proposed based on the basic human learning algorithm. The algorithm extends the coding method from binary coding to integer coding by introducing the Split algorithm with the crossover operator. Meanwhile, in combination with the theory of contrastive cognition, the hash table-based solution memorization mechanism and community learning operator are introduced to improve the diversity of the solution set by avoiding repeated searches of the solution and increasing the learning approach; the contrastive selection strategy is introduced in order to improve the convergence of the solution set. In this paper, the Solomon dataset and related studies are used to generate small, medium and large scale of examples and select multi-objective genetic algorithms and simple human learning algorithms to compare with the algorithms in this paper. The results show that the algorithm in this paper can effectively solve the small, medium and large scale of cases and outperforms the remaining two comparative algorithms. Compared to the remaining two algorithms, the average cumulative survival rate of the casualty is large, and the average total transit time and the cost of psychological suffering of the casualty are small on average. Finally, taking the Jishishan earthquake as an example, an optimization scheme for the siting of emergency medical facilities and the transfer of the injured is proposed.
The effect of aftershocks and uncertainty in the number of casualties on the casualty transfer scheme can be considered subsequently to establish a multi-stage casualty number uncertainty model for emergency medical facility siting-casualty transfer.

Research on Multiple Due-window Assignment Scheduling Problems with Learning Effects

LIU Zheng, LYU Danyang, FENG Wei, WANG Jibo

2025, 34(12):  78-84.  DOI: 10.12005/orms.2025.0378

Asbtract ( )   PDF (967KB) ( )  

References | Related Articles | Metrics

This paper provides insights into a specific and complex class of single-machine scheduling problems in the framework of two classical due-window settings i.e., common due-window and slack due-window. These problems involve jobs that are given multiple common due-windows or slack due-windows, where the jobs are divided into different groups based on their characteristics, and the jobs within the same group share the common due-window or slack due-window. In particular, the processing times of these jobs are not fixed, but are closely related to the learning effect, i.e., along with processing, the improvement of the worker’s skill or the optimization of the machine’s state leads to a reduction in the processing time of the subsequent jobs.
The core objective of the study is to find an optimal job processing sequence and determine multiple configurations of the common due-window or slack due-window, where the goal is to minimize the combined effect of several key performance metrics: earliness-tardiness costs for jobs, the cost of adjustments to the due-window start time, the efficiency of utilizing the common/slack due-window and the optimization of the size of the due-window. These metrics are considered together in the form of a linear weighted sum, aiming to balance the complex relationship among productivity, resource allocation and cost control.
Through theoretical analysis and model construction, it is found that the scheduling problem can be skillfully transformed into an assignment problem when the number of groupings of jobs is given, and then the problem can be solved in polynomial time. In this transformation process, we construct the corresponding mathematical model by exploiting the properties of the learning effect, the flexibility of the due-window and the regulating ability of the slack due-window.
Further, the Hungarian algorithm is used to solve the assignment problem where the time complexity is O(n³), where n is the number of jobs. This result not only provides a feasible path for solving this kind of complex single-machine scheduling problems, but also reveals the trend of the algorithm efficiency when the problem size (i.e., the number of jobs) increases, which provides a solid theoretical foundation for decision support in practical applications.
In summary, this study not only deepens the understanding of single-machine scheduling problems with learning effects and complex common/slack due-window constraints, but also provides effective solutions for efficiency improvement and cost optimization in real production scheduling through theoretical analysis and algorithms design.

Application Research

Research on Black-Litterman Portfolio Model Considering Information Disclosure in Annual Reports of Listed Companies

XU Weijun, ZENG Jiawei, LIU Guifang, ZHOU Qi

2025, 34(12):  85-92.  DOI: 10.12005/orms.2025.0379

Asbtract ( )   PDF (1246KB) ( )  

References | Related Articles | Metrics

The annual reports of listed companies, which are characterized by reliability, equality and rich content, increase the information supply of listed companies to the market, meet the information needs of investors, and also affect investors’ investment decisions and perspectives. Previous studies have found that annual report information disclosure has a significant impact on investors’ investment decisions and viewpoints. However, currently there is little research in China that uses annual report text information to improve investor viewpoint parameters in the Black-Litterman model (BL model). The BL portfolio model is proposed based on the Markowitz mean variance model, which introduces investor perspectives and modifies expected returns on the basis of equilibrium returns. This innovative method that combines prior returns and investor perspectives makes the calculation of expected returns in investment portfolios more reasonable. Investors can combine their personal expected returns, risk preferences and other perspectives to make more accurate asset allocation decisions.
The annual reports of listed companies contain rich and complex information, including a large number of textual descriptions, financial data and other related information. Traditional reading and comprehension methods often have low efficiency in processing these large-scale data. With the development of computer technology, various text analysis and mining tools have emerged one after another, making it possible to efficiently interpret and analyze the text content disclosed in annual reports. Furthermore, it can better assist investors in making investment decisions and market analysis. This article first combines computer technologies such as machine learning and deep learning to construct an annual report information disclosure attribute indicator that includes three dimensions: readability, similarity and risk factors. These indicators can measure the disclosure attributes of annual reports of listed companies. Then, this article uses a random forest regression model to predict the rise and fall of stock prices by adding annual report information disclosure attribute indicators as input features on the basis of traditional indicator prediction. Finally, we apply the predicted stock price fluctuations as an investor perspective to the traditional BL investment portfolio model and construct a new BL investment portfolio model that considers the disclosure of annual report information by listed companies. Our paper optimizes the traditional BL investment portfolio model and improves the measurement level of annual report information disclosure.
This article conducts an empirical analysis based on real data from the domestic A-share market. The results indicate that the BL investment portfolio model constructed in this article, which considers the disclosure of annual report information of listed companies, performs well in indicators such as Sharpe ratio. And our model can achieve returns exceeding market indices under market conditions. The research results can provide certain standard references for listed companies, relevant regulatory departments, and investors in the formulation, supervision, and analysis of annual report information disclosure content.
In future research, further exploration can be conducted from the following two aspects. One is to broaden the scope of information disclosure content collection, including disclosure information from self-media platforms (such as financial reports, Weibo, and WeChat) to achieve more dimensional measurement of information disclosure attribute indicators. The second is to optimize and improve more types of investment portfolio models based on the annual report information disclosure attribute indicators, further expanding the application scenarios of information disclosure attribute indicators.

Optimized Strategy for Dynamic Patient Admission to Complex Outpatient Departments

ZHUANG Zian, SU Qiang, DONG Haiyan, ZHUANG Siliang

2025, 34(12):  93-99.  DOI: 10.12005/orms.2025.0380

Asbtract ( )   PDF (993KB) ( )  

References | Related Articles | Metrics

Outpatient services are a vital interface between hospitals and patients, serving as the frontline of hospital operations. The quality of these services significantly influences patients’ perceptions, evaluations and choices of hospitals. Currently, outpatient services in China face considerable challenges, including high patient demand and the persistent issue of “three kinds of long wait and one short visit.” Addressing these challenges and improving the quality of outpatient services are an urgent societal concern. This study focuses on the patient admission decision-making process in complex outpatient departments.
Complex outpatient services are characterized by extended treatment durations, multiple visits and numerous treatment activities. In these departments, patients undergo long-term treatment cycles, with varying combinations of medical service needs across different treatment stages. A key challenge for such outpatient services lies in balancing long-term patient demand with the department’s service capacity. This imbalance often results in excessive overtime or an inability to meet all patient needs. To mitigate this, outpatient managers commonly regulate the number of patients entering the system. Given that admitted patients require continuous care, managers may sometimes need to decline new patients and suggest referrals. Thus, the key management problem for complex outpatient services is how to design a reasonable admission strategy that maximizes the number of patients served while ensuring high-quality care and smooth hospital operations. Another challenge lies in the need for managers to frequently adjust admission decisions based on daily applications and the status of existing patients. As a result, this is an online scheduling problem with distinct multi-stage and dynamic characteristics.
Taking obstetric outpatient services as an example, this study develops an infinite-horizon discounted Markov Decision Process (MDP) model to address these challenges. The model accounts for several real-world uncertainties, including variability in patients’ gestational weeks at the time of application, individual preferences for specific doctors and unpredictable dropout behavior. Additionally, it captures the diverse medical service needs of heterogeneous patients at different stages of treatment and considers the varying costs associated with service provision. The objective is to optimize long-term mutual benefits for both hospitals and patients.
To tackle the “curse of dimensionality” posed by the model’s expansive state and decision space, the study proposes a solution approach based on Approximate Dynamic Programming (ADP). This approach employs basis functions to linearly approximate the value function and utilizes a dual-column generation algorithm to efficiently derive near-optimal strategies. The proposed MDP model and its solution method effectively encapsulate the complex management characteristics of outpatient services. The numerical experiments reveal significant improvements, with the model and algorithm enhancing performance by approximately 41% compared to the current practices in tertiary hospitals and by around 14% relative to heuristic threshold strategies.
The proposed complex outpatient admission model is readily applicable to the daily management of obstetric outpatient services and can be adopted with minor modifications for other complex outpatient departments with similar characteristics. Future research could explore more dynamic elements, such as demand fluctuations and seasonal effects, which may further complicate the admission decision-making process.

Optimization of Project Scheduling with Limited Construction Site in a Carbon Trading Scheme Based on Deep Reinforcement Learning

LIU Hao, ZHANG Jingwen, CHEN Zhi, LI Heng

2025, 34(12):  100-106.  DOI: 10.12005/orms.2025.0381

Asbtract ( )   PDF (1114KB) ( )  

References | Related Articles | Metrics

Heavy machinery used in construction projects generates significant carbon emissions. The carbon trading scheme aims to reduce these emissions through market mechanism. This paper proposes a Project Scheduling Problem with a Limited Construction Site in a Carbon Trading Scheme (PSPLCS-CTS). The objective is to minimize the total project cost, including the carbon trading cost. We assume construction machinery can operate at different speeds, leading to varying carbon emissions and activity durations. Upon project completion, if actual carbon emissions exceed the allocated quota, the excess emissions must be purchased additionally; conversely, any surplus quota can be sold.
Based on the above analysis, we construct an integer programming model for PSPLCS-CTS. Then, the integer programming model is transformed into a Markov Decision Process (MDP) model. We design five key components of the MDP model according to the problem’s characteristics: decision points, states, actions, state transition equations and reward function.
We develop a two-stage algorithm (Double DQN-LS) that combines Double Deep Q-Network and local search to solve the MDP model. In the first stage, the agent interacts with the environment to generate experiences, which are stored in a replay buffer and then randomly sampled for training. The state and action information are converted into a matrix and input to the network, where convolutional layers automatically extract features, and the Q-value of the state-action pair is estimated. In addition, to reduce the overestimation of the target value, the evaluation network is used to select the action during the learning process, and the target network is used to estimate its Q-value to improve the stability and performance of the algorithm. In the second stage, two local search algorithms are employed to enhance the quality of the schedule produced by the Double DQN.
Finally, extensive computational experiments are conducted to verify the effectiveness of the algorithm. For each set of instances, a sample is randomly selected for training at the level of each characteristic parameter. The trained Double DQN is then used to solve other new instances, and the two local search algorithms are used to refine the schedules generated by the Double DQN. The experimental results show the proposed Double DQN-LS algorithm outperforms the Genetic Algorithm (GA) and Estimation of Distribution Algorithm (EDA) on instances with larger sizes. Furthermore, the Double DQN-LS algorithm demonstrates a significant advantage in solving efficiency on all instances, with an average solving time of only about 6% of that of GA and 12% of that of EDA.

A Learning-based Simulated Annealing Algorithm for Vehicle Routing-Loading Problem in Reverse Logistics

ZHENG Yonghong, WU Peng, LU Yongliang

2025, 34(12):  107-114.  DOI: 10.12005/orms.2025.0382

Asbtract ( )   PDF (1615KB) ( )  

References | Related Articles | Metrics

Reverse logistics refers to the process of collecting products from consumers and returning them to retailers or manufacturers. In this process, each returned product has its own specific value and weight. Since truck drivers have limited working hours, the challenge lies in how to efficiently plan the vehicle route within the specified time and simultaneously optimize the loading strategy to maximize profits. This has become a critical issue that logistics companies need to address. Therefore, the Vehicle Routing-Loading Problem (VRLP) is an important optimization problem in reverse logistics.
In VRLP, multiple customer sites are involved, with each site containing several items of known profit and weight. The optimization goal of the problem is to plan vehicle route efficiently within a specified time, visit multiple sites to collect items, and at the same time, maximize the total value of collected items without exceeding the vehicle’s loading capacity. VRLP is a complex NP-hard problem. Its complexity primarily arises from the need to consider multiple interrelated factors, such as vehicle travel time constraints, loading capacity limitations, and the value and weight of the goods. These factors are intertwined, making the problem extremely challenging to solve. Traditional exact solution methods often cannot find the optimal solution within a reasonable time frame. In contrast, heuristic algorithms can provide satisfactory feasible solutions in a shorter amount of time, making them well-suited for solving such problems. VRLP originates from real-world applications in reverse logistics and can solve many practical issues in logistics operations. Therefore, developing and researching efficient algorithms to solve VRLP can not only improve the operational efficiency of logistics companies but also provide important theoretical support and practical references for the academic community.
To address the NP-hard nature of VRLP, this paper proposes an efficient learning-based simulated annealing algorithm. The algorithm consists of three important components: a learning-based random greedy initialization method, a simulated annealing optimization procedure and a learning probability update mechanism. The algorithm first initializes a probability learning matrix and then executes a series of iterations. In each iteration, the algorithm first generates a high-quality initial solution using a learning-based random greedy method, and then updates the initial solution using the simulated annealing procedure to obtain an optimized solution. Finally, the algorithm dynamically updates the probability learning matrix by comparing the initial and optimized solutions, and the probability learning matrix, in turn, guides the creation of high-quality initial solutions. Experimental results show that the proposed algorithm can efficiently solve VRLP. Specifically, the algorithm outperforms comparison algorithms in the literature in terms of solution quality in large and extremely large test cases, offering a new approach to solving the vehicle routing-loading problem in reverse logistics.
Future research can focus on several aspects. First, given the NP-hard nature of VRLP, developing efficient exact algorithms remains an important research direction, aiming to provide optimal solutions for medium- and small-scale problems. Second, future studies should consider more real-world factors, such as customer time windows, customer priorities and satisfaction and multi-vehicle coordinated scheduling, to enhance the practicality and adaptability of the problem. Additionally, integrating machine learning techniques, especially deep reinforcement learning, to solve VRLP is also an exciting direction for future research.

Waste Collection Routing Optimization Considering Charging Strategies in Two-echelon Mode

SUN Zhuo, YANG Huirong, WU Longjie, Han Peixiu

2025, 34(12):  115-122.  DOI: 10.12005/orms.2025.0383

Asbtract ( )   PDF (1390KB) ( )  

References | Related Articles | Metrics

With the acceleration of urbanization and rapid economic development, the quantity of municipal solid waste has shown a trend of rapid growth, and an efficient collection of municipal solid waste has become an urgent task for the sanitation industry. At the same time, in order to effectively control and reduce greenhouse gas emissions from transportation, countries around the world have formulated and implemented sustainable energy saving and emission reduction policies. This puts strict requirements on municipal solid waste collection, and also brings pressure to reduce emissions. On the other hand, high collection costs are also a major obstacle limiting an efficient management of municipal solid waste. With the growth of waste volume, an increase in collection costs has become more and more prominent, and waste disposal companies have to invest more resources in coping with the huge demand for waste collection. Faced with the double pressure of cost reduction and emission control, municipal solid waste management departments have tried to save energy and increase efficiency by adopting various optimization methods, such as the Municipal Solid Waste Vehicle Route Problem (MSWVRP). As the public attaches more importance to environmental protection, scholars’ research on MSWVRP has expanded from focusing only on economic costs to taking environmental benefits into account, and then the green MSWVRP problem has been proposed.
With the enhanced promotion and application of new energy vehicles in the sanitation field, the green MSWVRP is no longer limited to the pollution problem of fuel vehicles, but the research on the MSWVRP based on electric vehicles, the Municipal Solid Waste Electric Vehicle Routing Problem (MSWEVRP), is gradually carried out. However, the current research on MSWEVRP by scholars at home and abroad considers charging strategies in a simplistic way, mostly focusing on the complete charging behavior, a single charging mode, or even ignoring the charging problem, and most of it focuses on the first-echelon collection process as the scope of the research, and is lack of the exploration for the full collection process in the two-echelon mode. Therefore, this paper adopts the hybrid scheme of using electric vehicles for the first echelon of collection and fuel vehicles for the second echelon for the two-echelon waste collection mode. Meanwhile, the focus is on EV charging strategies, including partial charging behavior and multiple charging modes, which are incorporated into the model construction and algorithm design. Fusing the ideas of adaptive strategies, local search and large neighborhood search algorithms, the hybrid adaptive large neighborhood search algorithm (HALNS) is proposed, and the charging station related operator and charging mode related operator that fit the problem are also designed to accelerate the convergence and thus improve the quality of the solution.
This study takes Chaoyang District of Beijing, China as an example, constructs an arithmetic example and successively carries out model accuracy test, algorithm validity verification and performance comparison, as well as case solving and result analysis. The results show that the two-echelon waste collection mode, partial charging behavior and multiple charging mode can effectively reduce the waste collection cost. Compared with the full charging behavior, the partial charging behavior can significantly save the collection costs, with an improvement of up to 10.36%. Multi-charging reduces collection costs by 1.42% compared to single (slow) charging. A two-echelon mode using both electric vehicle collection and fuel vehicle collection can reduce cost by 67.86% compared to a direct collection mode using only fuel vehicles.
In summary, this paper proposes the optimization problem of municipal solid waste collection routing considering charging strategy in two-echelon mode, in-depth investigation into partial charging behavior and multiple charging modes in charging strategy. It can improve the availability and economy of electric vehicles and provide a routing plan for cost reduction and emission control for the municipal solid waste collection. Our study is of great significance for improving the level of waste management and promoting the sustainable development of the city.

Heterogeneity Path of Impact of Renewable Energy Development on Carbon Dioxide Emissions

WANG Yiqi, ZHEN Wenqing, DUAN Yangzhou

2025, 34(12):  123-129.  DOI: 10.12005/orms.2025.0384

Asbtract ( )   PDF (939KB) ( )  

References | Related Articles | Metrics

The development of renewable energy is beneficial for improving the energy structure and reducing the use of traditional energy sources, which helps mitigate the adverse effects of carbon dioxide emissions and achieve sustainable social and economic development. In-depth research on the impact of renewable energy development on carbon dioxide emissions and its heterogeneous pathways is of great significance for guiding China in adopting differentiated carbon reduction strategies and achieving the “dual carbon” goals.
The article first conducts a theoretical analysis based on the mechanisms through which renewable energy development impacts carbon dioxide emissions. It then examines, from a theoretical perspective, the carbon reduction effects of green technological innovation and institutional quality on renewable energy development. Next, empirical tests are carried out using traditional models and finite mixture models, incorporating accompanying variables to assess their impact on carbon dioxide emission reductions. Furthermore, the study investigates the probability of pathway transitions and uses two-sided T-tests to examine whether there are differences between the pathways with respect to the two accompanying variables-institutional quality and green technological innovation. Finally, robustness and endogeneity tests are performed by replacing the dependent variable, using two-sided trimming, and introducing lagged accompanying variables.
The study finds that: First, after robustness and endogeneity tests, it is found that the development of renewable energy in China can significantly suppress carbon dioxide emissions, and the impact on carbon dioxide emissions follows three distinct pathways. Most provinces belong to the “comprehensive development” pathway, which promotes carbon dioxide reduction by optimizing both energy efficiency and energy structure simultaneously. Second, institutional quality and green technological innovation, as accompanying variables, both effectively enhance the carbon reduction effects of renewable energy development. Moreover, the higher the level of institutional quality and green technological innovation, the greater the likelihood that provinces will belong to the “comprehensive development” pathway. Third, the results of the dynamic pathway transitions show that most eastern regions have been already on the “comprehensive development” pathway at the early stage of the study, while many central and western regions have undergone pathway transitions, shifting from the “energy efficiency improvement” or “energy structure adjustment” pathways to the “comprehensive development” pathway. The improvement in institutional quality and green technological innovation levels are key factors driving these provincial pathway transitions.
Based on the research conclusions, the following recommendations are made: First, we should vigorously develop renewable energy. The government should increase financial investment in renewable energy, and each province in China should develop renewable energy according to its own geographic and resource endowments. Second, we should strengthen green technology research and innovation. Governments should increase investment in renewable energy R&D and promote green technological innovation, while also boosting subsidies for corporate green technology innovation. Third, we should steadily promote institutional innovation. Policymakers should enhance their institutional advantages, implement stricter and more effective environmental policies, and strengthen the carbon reduction effects of renewable energy development.

Two-echelon Vehicle Routing Problem with Drones Considering Three-dimensional Loading Constraints

MA Yunfeng, HU Jian, OUYANG Lijun, HU Yina, LI Jian

2025, 34(12):  130-137.  DOI: 10.12005/orms.2025.0385

Asbtract ( )   PDF (1348KB) ( )  

References | Related Articles | Metrics

As the future development of the express delivery industry requires a greater focus on user experience, the integration of drones into vehicle delivery systems can serve as an effective solution to city last-mile logistics and green logistics. In order to ensure the feasibility of loading before the drone-assisted vehicles distribution and the absence of outbound relocating during the delivery process, and considering the three-dimensional loading constraint and the customer service deadline, the Two-echelon Vehicle Routing Problem with Drones Considering Three-dimensional Loading Constraints (3L-2E-VRP-D) is a new optimization problem, which is a combination of a loading problem and a two-echelon vehicle routing problem with drones. Distribution operations are jointly completed by vehicles carrying drones, each of which departs from the depot with some drones and the goods demanded by customers, and returns to the depot after completing distribution services for each customer on the path in turn before the service deadline. The drone loaded with goods takes off from the vehicle, flies to the customer nodes that drone could service and completes the order, and finally returns to the original vehicle via the original route. During the drone distribution period, the vehicle is needed to stay at the departure point of the drone and wait for the drone to fly back. There are several constraints for 3L-2E-VRP-D: (1)the demand of customers served by a vehicle or drone; (2)a feasible placement of items within the loading space; and (3)each customer’s service deadline. Loading items into trucks and successive routing of vehicles and drones along the road network are the most important problems in distribution management.
This paper addresses an important problem combining three-dimensional loading and a two-echelon vehicle routing problem with drones. A mixed integer programming model is established for 3L-2E-VRP-D. Both a two-echelon vehicle routing problem with drones and a three-dimensional loading problem are NP-hard problems. Thus, the combinatorial problem 3L-2E-VRP-D is clearly also the case. Exact algorithmic methodologies are not expected to solve the real-world problems of large customers and item sets in a reasonable time. Therefore, we solve the problem by using a hybrid algorithm based on the Adaptive LargeNeighborhood Search (ALNS). ALNS as the outer algorithm optimizes the vehicle routing through destroy and repair operations. For the goods of customers along the route, the innerImproved Heuristic Loading Algorithm (IHLA) verifies the feasibility of the optimized routes by attempting to construct loading solutions that satisfy three-dimensional packing constraints.
The algorithm is tested and numerically experimented using the Cardiff dataset, a famous VRPTWDR dataset. Information about customer goods is randomly generated. The accuracy of the model and the effectiveness of the algorithm are verified by solving small-scale arithmetic cases by Gurobi and the algorithm. The results show that for 10 sets of small-scale examples, the hybrid algorithm is able to find the optimal solution for 7 of them, with an average GAP value of-1.11% compared to the exact algorithm solver, Gurobi, and the average solution time is reduced by 66.6%. In addition to this, an improvement of the final solution over the initial solution is consistently above 60% using the hybrid algorithm to solve the 25 to 150 customers’ arithmetic cases. Sensitivity analyses are also carried out for four parameters in the problem: the maximum number of drones that can be carried by the vehicle, the average size of the cargo and the parameters of the different types of drones including the maximum load and maximum flight distance. The results show that as the maximum number of drones that can be carried by the vehicle increases, the optimization rate gradually increases, however, its marginal benefit decreases; an increase in the average size of the cargo has a greater impact on the larger number of customers of the algorithm when the average size of the cargo increases to a certain value. With an increase of maximum load and maximum flight distance of the drones, the total delivery time decreases, but the marginal benefit diminishes.
3L-2E-VRP-D considers the realistic loading and unloading problem based on the drone assisted vehicle distribution problem, which is of great significance in the distribution process because, up to the present time in research, it is the closest to the realistic application of drone assisted vehicle distribution in reality, which ensures that a feasible loading plan is available before the distribution and that there is no outbound relocating of the boxes during the distribution process.
Follow-up studies can be carried out in the following aspects: (1)This paper assumes that the goods are not rotatable, and future studies may consider adding the case of the goods being rotatable. (2)The hybrid algorithm is based on the adaptive large neighborhood search, adding an improved heuristic loading algorithm. Future research can design a hybrid algorithm based on the exact algorithm for solving medium-scale problems with higher quality.

Dynamic 3D Placement of Drone Base Station for Maximal Covering Emergency Communication Demand after Disasters

XIANG Yin

2025, 34(12):  138-144.  DOI: 10.12005/orms.2025.0386

Asbtract ( )   PDF (1174KB) ( )  

References | Related Articles | Metrics

In recent years, natural disasters such as the “7·20 Extraordinarily Heavy Rainstorm in Zhengzhou” and the “9·5 Luding Earthquake” have occurred frequently, causing severe casualties and economic losses. After the disasters, the communication infrastructure was severely damaged, resulting in communication interruptions and affecting emergency rescue operations. In the era of the low-altitude economy, the Unmanned Aerial Vehicle (UAV) equipment technology has developed rapidly. By equipping UAVs with communication devices, temporary communication services can be provided to the disaster-stricken areas, which often have the advantages of strong signals, wide coverage and flexible movement. In this context, how to rationally layout UAV communication base stations after a disaster to maximize the number of demand coverage has become an important decision-making problem faced by emergency command departments.
The layout of UAV communication base stations falls within the category of facility location. The maximum coverage model, as a classic location model, has received widespread attention. The classic maximum coverage model uses a fixed coverage radius to determine the facility’s service area in order to seek the location solution that covers the most demands. The layout of UAV emergency communication base stations can be regarded as an extension of the classic maximum coverage location problem, which expands the problem from a two-dimensional plane to a three-dimensional space and adds the decision of hovering altitude. However, through a literature review, it is found that the existing research on the location of UAV communication base stations mainly focuses on static situations and cannot be applied to the dynamic changes in the emergency rescue environment. Therefore, this paper expands the existing research on the location of UAV communication base stations and further considers the dynamic change characteristics of the location of demand points after a disaster. By constructing a multi-period nonlinear mixed-integer model, an integrated solution to the spatial positioning, movement trajectory, demand allocation and adjustment of the UAV swarm in multiple periods is obtained. To solve this model, an improved genetic algorithm is designed. The improvements of the algorithm are reflected in: (1)improving the initial population generation method based on the distribution of demand points, (2)generating and importing elite individuals in combination with the K-means algorithm and (3)designing a greedy algorithm to achieve demand allocation.
Finally, we compare the solution effects of the genetic algorithm before and after the improvement with the effect of the mathematical software BARON. The results show that: (1)The computing time of the two types of genetic algorithms in solving large-scale problems is significantly less than that of the BARON solver. (2)After the improvement of the genetic algorithm, the error rate decreases from 20% to 4.5%. In addition, in order to verify the feasibility of the model and algorithm in this paper in a real-world scenario, we also conduct a case study based on the “8·8 Jiuzhaigou Earthquake” to obtain the communication base station location and demand allocation plan.

Institutional Investor Attention, Individual Investor Attention and China’s Gold Futures Volatility Prediction

QU Hui, ZHANG Yu

2025, 34(12):  145-151.  DOI: 10.12005/orms.2025.0387

Asbtract ( )   PDF (953KB) ( )  

References | Related Articles | Metrics

China listed gold futures in 2008 and implemented its night trading in 2013. Since then, the trading volume of Shanghai gold futures market has jumped to the third place in the world and ranked first in the Asia Pacific region. The gold futures market has gradually formed a “Chinese price”. With the continuous development and improvement of China’s gold futures market and its increasing influence, it is necessary to conduct a reasonable modeling and accurate prediction of the volatility of China’s gold futures.
As important participants in the gold market, investors’ attention to the market and their trading behavior will greatly affect the fluctuation level of gold futures prices. In view of this, this article starts from behavioral finance theory and adopts four classic heterogeneous autoregressive volatility models of realized volatility to explore the predictive abilities of institutional investors’ attention and individual investors’ attention on the volatility of China’s gold futures prices. Among them, the construction of individual investors’ attention proxy is based on the Baidu search index, and the construction of institutional investors’ attention proxy is based on regression decomposition of China’s gold futures trading volume, considering the time dimension of information. Specifically, based on expected and unexpected trading volumes, expected institutional investor attention and unexpected institutional investor attention are respectively constructed.
This study applies the 5-minute high-frequency prices for the main contracts of China’s gold futures from January 2, 2014 to June 30, 2023 to construct the realized volatility series. For each of the four classic heterogeneous autoregressive models, we consider the extension that only introduces individual investor attention, as well as the extension that simultaneously introduces individual investor attention, expected institutional investor attention and unexpected institutional investor attention, thus altogether twelve models. The analysis of fitting and prediction results for the twelve models shows that:
(1)Individual investors are easily influenced by market information due to cognitive limitations, which can lead to noise trading behavior and affect gold futures prices. Therefore, in the short term, individual investor attention is positively correlated with the volatility of gold futures.
(2)The expected attention of institutional investors based on historical trading volume reflects, to some extent, the trading conducted by institutional investors on the basis of analyzing market historical information. In addition, institutional investors will engage in zero sum games based on the limited attention of individual investors in the short term. Therefore, the expected institutional investor attention is positively correlated with the volatility of gold futures, while the unexpected institutional investor attention is negatively correlated with the volatility of gold futures in the short term.
(3)Introducing individual investor attention, expected institutional investor attention and unexpected institutional investor attention in the HAR class volatility models can significantly improve the fitting and forecasting performance for China’s gold futures, and this improvement is more significant during market turbulence.
The research conclusion of this article has practical significance. On the one hand, investors can more accurately grasp the volatility characteristics of China’s gold futures, and carry out more effective investment portfolio construction and risk management during periods of economic turbulence. On the other hand, the government and policy makers can monitor the market more effectively, formulate corresponding policies, improve risk prevention and control capabilities, and ensure the sustained and healthy development of China’s economy and social stability. As a prospect, the method proposed in this article to introduce investor attention variables can be extended to predict various financial and commodity futures price volatility, which is also the direction we are concerned about.

Study on Online Prediction for Wind Power Ramp Events Adapting to Concept Drift

WANG Jujie, XU Wenjie, SUO Weilan

2025, 34(12):  152-158.  DOI: 10.12005/orms.2025.0388

Asbtract ( )   PDF (1238KB) ( )  

References | Related Articles | Metrics

Developing renewable energy generation is a crucial measure in achieving the strategic goals of “carbon peaking and carbon neutrality.” The construction of a new power system dominated by renewable energy demands higher flexibility from the power grid. As a typical representative of renewable energy, wind power has become an integral component of the global power system, owing to its abundant resources, minimal environmental impact and well-established industrial foundation. Wind power ramp events refer to the sharp fluctuations in wind power output over short time intervals. With the continuous growth in wind power capacity and its increasing share in the energy mix, these ramp events have emerged as a significant risk factor affecting the economic and reliable operation of power grids. The potential impact on grid stability is substantial. However, wind power ramp events exhibit complex characteristics, including transience, uncertainty and non-linearity, which pose significant challenges for accurate prediction. Reliable forecasting of wind power ramp events is essential for facilitating wind power integration, guiding rational grid dispatch, and mitigating the risks of power imbalances due to wind power grid integration.
This paper proposes an online prediction framework for wind power ramp events that adapts to concept drift, which consists of an online prediction module, a trend compression module and a ramp identification module. First, based on the adaptive random forest model, the wind power output online prediction module is constructed, which uses an online ensemble learning method to adaptively update the model parameters, adapt to the concept drift in wind power output, and achieve accurate power prediction. Second, based on the swinging door algorithm optimized by the Grey Wolf Optimization (GWO), the trend compression module is constructed, which calculates the optimal tolerance coefficient and compresses the power prediction data, extracts the significant trend, and reduces the interference of noise data on ramp prediction. Finally, based on the fluctuation trend-based ramp identification algorithm, the ramp identification module is constructed, which avoids the multiple and false detection caused by complex local fluctuations, and identifies the ramp events to obtain the final ramp prediction results. The integration of these modules ensures both the accuracy and robustness of the wind power ramp event predictions.
In the empirical study, this paper utilizes wind power data from two wind farms located in the Fujian and Zhejiang provinces in China to validate the proposed framework. Additionally, three error metrics and three accuracy metrics are employed to evaluate the prediction performance of the framework. The results indicate that the framework outperforms several benchmark models in terms of both prediction accuracy and robustness. Notably, the adaptive random forest model effectively addresses concept drift in the wind power data, enhancing the model’s generalization ability. Moreover, the GWO algorithm optimizes the parameters of the swinging door algorithm, determining the optimal tolerance coefficient for different wind farm data, which aligns the compression results more closely with the fluctuations in wind power. The fluctuation trend-based ramp identification method mitigates multiple and false detections caused by minor local fluctuations in wind power, accurately capturing various types of ramp events.
In summary, the online prediction framework for wind power ramp events, which adapts to concept drift, demonstrates strong predictive performance. This framework holds significant potential for optimizing the operational control and scheduling strategies of wind farms, thereby enhancing the economic efficiency and reliability of wind power. Additionally, its ability to handle real-time data fluctuations makes it suitable for practical applications in diverse wind farm environments. Based on this framework, future research could integrate wind power data into numerical weather prediction data to improve ramp events predictions, further enhancing the model’s predictive capabilities.

Data-driven Time Consumption Prediction and System Optimization for Search and Rescue of Missing Elderly in Social Volunteer Organizations

LI Yiying, QIAN Kun, XIA Wenxin, ZHAN Shuai, LIU Dehai

2025, 34(12):  159-165.  DOI: 10.12005/orms.2025.0389



Asbtract ( )   PDF (1336KB) ( )  

References | Related Articles | Metrics

With the accelerating aging process in China, leveraging social forces to safeguard the safety of missing elderly has become a crucial measure in building an elderly-friendly society. The instability of volunteer mobilization efforts and the extreme time-sensitivity of locating these vulnerable individuals pose a dual challenge that demands innovative solutions. However, the current lag in digital and intelligent intervention measures within social volunteer organizations has led to issues such as inadequate prediction accuracy for time consumption during rescue operations and delayed responses to police alerts. These challenges hinder further improvements in the effectiveness of social rescue systems. Establishing robust, data-driven volunteer rescue capabilities is therefore paramount for effectively addressing these urgent cases in the future. This study tackles these intertwined issues by constructing a data-driven search and rescue system for social volunteer organizations.
This study establishes a data-driven search and rescue system for missing elderly persons in social volunteer organizations through investigations of WZ Emergency Rescue Team and historical data mining. Machine learning algorithms are introduced to conduct search duration prediction based on real case characteristics. Subsequently, a system optimization model is developed with the objective of minimizing full-chain circulation time of police alert information, accompanied by a solution algorithm tailored to model characteristics. The system integrates machine learning and the Ant Colony Algorithm, aiming to achieve highly accurate predictions of the time required for such rescues and significantly accelerate the flow of critical information throughout the entire emergency response chain.
This paper makes three key academic contributions to addressing missing seniors in China’s aging society: first, it identifies critical factors like dementia severity, advanced age, and reporting time that significantly influence volunteer rescue decisions through real-world case data analysis; second, it develops a scalable, time-sensitive machine learning model to predict search duration, enhancing the precision of volunteer mobilization alerts; and third, it creates an algorithm that optimizes multi-stage emergency information flow, providing volunteer organizations with a practical, data-driven framework for improving dementia-related rescue operations.
To achieve precise time-consumption predictions for missing senior rescues, this research rigorously evaluated leading algorithms (Decision Tree, Random Forest, Gradient Boosting, XGBoost), finding Random Forest superior in accuracy. Enriching historical rescue records further enhanced its prediction accuracy and stability. Integrating these predictions into mobilization guidance helps volunteers better balance professional and volunteer commitments, boosting rescue call credibility and encouraging more stable participation. The adaptable prediction module holds significant potential for broader application in assessing rescue workloads and expanding AI use in volunteer emergency response. Building on this predictive foundation, the study developed a data-driven search and rescue system for missing elderly persons in social volunteer organizations optimizing the dispatch of resources and critical case information. Applying the Ant Colony Algorithm demonstrably improved efficiency, reducing average completion time by 4.72%, standard deviation by 2.70%, and coefficient of variation by 4.37%. This faster information flow enables quicker mobilization for time-critical rescues over wider areas. This optimization module is a significant step towards leveraging technology within the collaborative “government-led, society-participated” emergency response mode, with scalability promise for larger or more complex response chains.
This research provides theoretical and methodological references for technical interventions in social rescue operations, offering significant practical application value for other types of social emergency response scenarios. Future research directions can consider the impact of standardizing case record protocols within social volunteer organizations on the robustness and applicability of predictive models. An extended model incorporating the evolving impact of widespread smart anti-wandering devices for seniors, which may enable shorter response networks and alter case information flow dynamics, can also be considered. These need to be further studied and demonstrated to establish highly efficient “early rescue, swift resolution” mode.

Forecasting China’s Industrial Structure Using a Fractional Discrete Grey Breakpoint Model

WANG Huiping, ZHANG Zhun

2025, 34(12):  166-173.  DOI: 10.12005/orms.2025.0390

Asbtract ( )   PDF (1207KB) ( )  

References | Related Articles | Metrics

Since the outbreak of the COVID-19 in early 2020, the epidemic has had a tremendous impact on China’s industrial economy, industrial organization and industrial structure. For example, measures such as mandatory quarantine are taken to avoid large-scale population movements and gatherings, which can severely impact service industries such as transportation, tourism, catering, retail and entertainment. In addition, due to the limited mobility of labor force and logistics transportation, manufacturing enterprises face difficulties in employment and transportation of raw materials in the short term, resulting in delayed order delivery and disruptions in the capital and supply chains. Overall, the COVID-19 pandemic has had the greatest negative impact on the tertiary industry, with a more profound effect on producer services than on consumer ones, and a greater influence on offline physical industrial activities than on online virtual industries. Therefore, quantifying the impact of the COVID-19 epidemic on China’s industrial structure and accurately predicting the change trend of China’s industrial structure in the post epidemic era have important practical significance for promoting the sustainable development of China’s economy.
In this paper, we first introduce a time breakpoint into the traditional GM(1,1) model to construct the Grey Breakpoint Prediction Model (GBPM(1,1)). Furthermore, we optimize this model to propose the Fractional Discrete Grey Breakpoint Model (FDGBM(1,1,t)). Second, drawing on traditional policy evaluation methods, we describe the application of the grey breakpoint model in intervention evaluation based on its characteristics and explain how to analyze the parameters of the grey breakpoint model. Third, we use data on the advancement and rationalization of China’s industrial structure to test the modeling accuracy of FDGBM(1,1,t) and other models. The new model is then employed to assess the impact of the COVID-19 pandemic on China’s industrial structure and that of its four major regions. Additionally, based on the disaggregated data of indicators, we model to identify the primary industries affected by the pandemic. Finally, we utilize FDGBM(1,1,t) to predict the future trends of advancement and rationalization in China’s industrial structure.
The results indicate that: first, by establishing time breakpoints and accurately estimating parameters, the grey breakpoint prediction models can effectively capture system changes caused by external environmental shifts, achieving precise system predictions. When compared to the GBPM(1,1,t), FDGM(1,1), and ARIMA models, the FDGBM(1,1,t) model demonstrates superior modeling accuracy. Second, the COVID-19 pandemic has exerted differential impacts on the advancement and rationalization of China’s industrial structure. Specifically, it has inhibited the current advancement of the industrial structure, primarily through its effects on the secondary and tertiary industries. Conversely, the pandemic has promoted the current rationalization of the industrial structure, mainly by influencing the secondary industry. Third, in the long term, the COVID-19 pandemic has accelerated the trend of industrial structure advancement but significantly hindered its rationalization. The rebound in these indicators is primarily attributed to the secondary industry, while the impact of the pandemic on the tertiary industry has, to some extent, facilitated the rationalization of the industrial structure. Fourth, regionally, the COVID-19 pandemic has exhibited similar effects on the industrial structures of China’s four major regions, with the most pronounced impacts observed in the northeast and western regions. Fifth, in the coming years, China and its four major regions are expected to witness a more rapid upward trend in the advancement of their industrial structures. However, the rationalization of the industrial structure in the eastern, central and western regions may experience stagnation in the later stages, while the northeast region may see a fluctuating trend in its industrial structure rationalization. Overall, the degree of industrial structure rationalization in China is projected to enter a period of stagnation.

Research on Quality Evaluation System of Elderly Care Services Based on Domain Lexicon

TIAN Xiaoli, ZHANG Kun, XU Zeshui

2025, 34(12):  174-181.  DOI: 10.12005/orms.2025.0391

Asbtract ( )   PDF (1111KB) ( )  

References | Related Articles | Metrics

According to the latest data from China’s National Bureau of Statistics in 2024, the population aged 65 and above reached 210 million, accounting for 14.5% of the total population, transitioning into a deeply aging society. The key characteristics of this demographic shift include a large elderly population base, a rapid aging process and an increasing dependency ratio. Among various strategies to address aging, community-based home care services stand out. However, research on the evaluation system for the quality of those services remains a challenge. Currently, studies related to evaluation indicators for the quality of community-based home care in China are still in the phase of theoretical development. Issues exist, such as the incomplete establishment of indicator systems, subjective indicator selection, a narrow research perspective, strong subjectivity in weighting models and limited empirical data.
In response to these challenges, this study explores new approaches to building a quality evaluation system for service. First, a specialized lexicon for community elderly care services is created. This lexicon is built by data mining, natural language processing and search engine algorithms, drawing from policy documents and academic literature. Key terms are identified through methods like extraction of seed word, information entropy and Pointwise Mutual Information (PMI), and categorized by semantic co-occurrence network analysis. The lexicon covers areas such as daily care, domestic services, medical and nursing care, physical and mental health, safety and privacy, community facilities and service regulation. Next, a comprehensive evaluation framework is developed from both the supply and demand perspectives. This framework is built based on models such as Andersen’s behavioral model and needs theory. Using the identified key terms, we expand and refine the lexicon to establish a quality evaluation system with seven dimensions: daily care, rehabilitation, health and well-being, safety, community facilities, service completeness and quality regulation. The framework is validated and refined through expert consultations to ensure content validity. Finally, the reliability of this framework is tested through field research. A measurement scale is developed based on the theoretical framework, and survey data are collected from 1,188 respondents in a newly classified first-tier city in the west of China (referred to as ‘City C’). By SPSS and AMOS, we confirm that the framework meets the standard requirements of reliability (Cronbach’s α, composite reliability) and validity (convergent and discriminant validity). Additionally, the cumulative variances explained by the extracted factors from both supply and demand sides are 72.385% and 70.578%, respectively (both above the 60% threshold), demonstrating robust factor extraction. CRITIC method and logarithmic smoothing Weighted Average Aggregation (WAA) are used to calculate the weights of indicators, allowing us to assess the current state of development of service quality.
On this basis, we enhance the existing ‘5A’ service quality assessment model by proposing a new ‘9-level’ standard, further improving the evaluation framework. The study concludes by highlighting its advantages and significance in terms of research pathways, perspectives and methodologies. It also provides policy recommendations aimed at balancing service resources allocation, aligning expectations and satisfaction levels between supply and demand, enhancing quality supervision and control, and strengthening a continuous feedback mechanism for improvement. This research offers valuable insights for the sustainable development of elder care services and the quantitative evaluation of service quality.


Deep Early Warning for Financial Risk Based on Multifractal Spectral Clustering and ResNet-SMOTE-SVM

HUANG Xun, WANG Peng, XU Kai

2025, 34(12):  182-187.  DOI: 10.12005/orms.2025.0392

Asbtract ( )   PDF (1350KB) ( )  

References | Related Articles | Metrics

In recent years, the frequent occurrence of major international economic and political events has intensified the turbulence of the global financial system and increased the risks. How to establish a scientific and effective financial risk early warning system to identify and prevent potential risk crises is of great significance for maintaining financial security and national security.
With the rapid growth of data, significant improvement in computing power and the rapid development of financial technology, the deep learning models represented by deep convolutional neural networks are gradually becoming a research focus in the academic community. Among them, the residual network(ResNet)deep learning model is good at preserving the original features of data through residual connections, which can effectively avoid feature layer by layer disappearance. Therefore, it is widely used in the research field of image processing. Unfortunately, the ResNet deep learning model has not yet been applied to research in the financial field, especially in the field of financial risks warning. Therefore, how to apply the ResNet deep learning models to financial risk early warning research has important practical significance for government financial management departments to scientifically respond to financial risk crises, and maintain financial security and national security.
Based on SSEC, this paper firstly uses the multifractal method to calculate the multifractal volatility, and introduces spectral clustering method to adaptively mine normal state samples and attentional state samples, which measures the financial risk sates. Further, this paper combines Synthetic Minority Oversampling Technique(SMOTE) of the imbalanced sample processing technology, ResNet of deep learning technology and Support Vector Machine (SVM) of the machine learning technology to propose a deep warning model of ResNet-SMOTE-SVM, and conducts an empirical research on financial risk deep warning.
The empirical results show that the financial risk states measured by the multifractal spectral clustering method not only has significant statistical significance, but also is highly consistent with the actual operation of Chinese financial market. Meanwhile, compared with other models, the deep early warning model of ResNet-SMOTE-SVM can effectively extract deep features of the financial market, overcome imbalanced sample problems, and accurately predict financial risks, which has excellent capability of financial risk early warning.
Based on the empirical results, the following conclusions can be drawn: (1)The financial market is a typical multifractal market, and the use of multifractal spectral clustering method can accurately measure the financial risk state. (2)The deep warning model of ResNet-SMOTE-SVM combines the advantages of ResNet network, SMOTE technology and SVM model. It can extract high-level and complex deep-seated features of the financial market, overcome imbalanced sample problems, and accurately predict financial risks. It has strong financial risk warning capabilities. (3)The research on deep waring of financial risks in this paper has innovated research methods in the field of financial risk early warning, provided reference value for risk management of financial systems with complex volatility characteristics, and also offered operable application tools for government financial regulatory and stability.

Research on Strategy of Interworking Membership Levels of Newly Entered Duty-free Retailers

HE Yi, LIU Xiaoyu, XU Qingyun

2025, 34(12):  188-195.  DOI: 10.12005/orms.2025.0393

Asbtract ( )   PDF (1121KB) ( )  

References | Related Articles | Metrics

With the steady development of Hainan’s offshore duty-free retail sector, there has been a surge in businesses entering the duty-free retail market, leading to intensifying competition within the industry. Leading duty-free market retailers in Hainan have established membership point systems through which they award points to consumers based on their membership tiers. The first-mover advantage of incumbent duty-free retailers enables them to accumulate a certain proportion of high-level member consumers. They use multiple points rebates to attract high-level member consumers to make repeat purchases, thereby creating formidable market entry barriers for new entrants. A number of new entrants to the duty-free retail industry have begun to challenge this advantage of established retailers by offering competitive membership levels strategy, thereby facilitating their entry into the duty-free retail market. In practice, Global Premium Duty-Free Plaza (Haikou) does not interwork the membership level strategy of China Duty Free Group, while Hainan Tourism Duty Free interworks the membership level strategy of China Duty Free Group, allowing consumers to enjoy the same level of point benefits. After the implementation of the “interworking membership level” strategy by the new entrants, consumers with high-level memberships from the incumbent retailers to be upgraded to high-level memberships with the newcomers, and the consumers can receive multiple rebates on the points they earn when purchasing duty-free products from the newcomers. The implementation of the “interworking membership level” strategy by new entrant retailers breaks the incumbent retailers’ customer barrier advantage and brings new opportunities for new entrant retailers, but newcomers also face new challenges. For example, when providing this service to consumers, they need to go through a series of cumbersome processes to review consumer membership upgrade application information, such as verification, validation and registration, which increases the trouble costs of new entrants. In addition, newcomers must cover the cost of providing points promotions involved in this strategy for consumers. Our research questions focus specifically on the following questions: (1) What conditions prompt new entrants to offer “interworking membership level” service in the context of incumbent retailers’ customer barrier advantage? What factors or scenarios drive newcomers to adopt this strategy? (2) What are the implications of new entrant retailers offering “interworking membership level” service on product pricing and profitability for both retailers?
This paper considers the market consisting of two types of consumers, i.e., premium membership consumers and regular membership consumers, and develops a retail system consisting of an incumbent duty-free retailer and a new duty-free retailer to study the market conditions under which the new duty-free retailer adopts the “interworking membership level” strategy in the context of the incumbent duty-free retailer’s customer advantage. We analyze the impacts of the strategy on equilibrium prices, profits and consumer welfare. First, using consumer utility theory, we build game-theoretical models and obtain the optimal strategies under two different promotional strategies: the new duty-free retailer does not provide “interworking membership level” service (NI strategy); it provides “interworking membership level” service (OI strategy). Second, we explore the impacts of providing “interworking membership level” service on the two retailers by comparing the profits of the new duty-free retailers and the incumbent duty-free retailers before and after providing this service. Finally, we analyze the impact of the strategy on consumer surplus.
Firstly, the implementation of the “interworking membership level” strategy by the new entrant retailers improves the optimal pricing of both retailers to some extent, thus weakening price competition. Secondly, new entrant retailers do not consider offering the ‘interworking membership level’ strategy when consumer acceptance of the new entrant retailer is small or large; new entrant retailers choose to offer the ‘interworking membership level’ strategy to consumers only when consumer acceptance of the new entrant retailer is moderate and the hassle cost is small. However, it is always advantageous to the incumbent retailer for new entrant retailers to offer the ‘interworking membership level’ strategy, which well explains why in practice incumbent retailers have not objected to the implementation of this strategy by new entrant retailers. The implementation of the “interworking membership level” service by new retailers can achieve a win-win situation for both new entrant retailers and incumbent retailers. Thirdly, interestingly, when the proportion of high-ranking member consumers is small, the incumbent retailer’s retail price will increase as the proportion of high-ranking member consumers increases and aggregate demand increases, but profits will decrease. This is because the losses from the increased cost of points-based promotions and reduced demand from regular member consumers outweigh the profit growth from higher prices and increased demand from higher-tier member consumers, so retailers’ profits decrease despite higher retail prices and increased aggregate demand. Finally, the analysis of the numerical example shows that the provision of an interworking membership level service by new entrant retailers harms consumer surplus.

Research on Competitive Relationship in China-Europe International Multimodal Transport under the Belt and Road Initiative

ZHOU Yutao, LI Zhenfu

2025, 34(12):  196-203.  DOI: 10.12005/orms.2025.0394

Asbtract ( )   PDF (2695KB) ( )  

References | Related Articles | Metrics

In the context of the Belt and Road Initiative, traditional maritime transportation, China Railway Express (CR Express) and inland transportation services, together constitute the China-Europe international multimodal transportation network, providing customers with more diversified transportation routes and service options. However, it should be emphasized that there is still a serious structural contradiction between the development demands of Chinese foreign trade and the capacity of international freight transportation corridors. On the one hand, although CR Express offers faster delivery times for Chinese customers, due to its high transportation costs and the withdrawal of the subsidy mechanism, most customers continue to prefer the maritime option with higher volumes and lower freight rates. On the other hand, the blockage of the Suez Canal, the Red Sea crisis and the frequent piracy risks have highlighted the shortcomings of traditional maritime transportation in terms of transportation safety and reliability, which can’t effectively guarantee the long-term stability of China-Europe trade transportation. Therefore, how to effectively assess the competitive relationship between CR Express and traditional sea transportation is an urgent research problem. This is crucial for the selection and adjustment of the customer’s transportation scheme, and is the key involving the customer’s ability to maintain profitability and competitiveness in the fast-developing and fiercely competitive market.
China-Europe multimodal transportation involves a wide range of logistics services and cargo types, and is a chain multimodal transportation problem from source to destination. It is not only the economic or time utility of each transport chain that affects the decision-making behavior of customers, but also the comprehensive transport utility of the entire transport chain under multimodal transport services. Based on this, this article regards the international multimodal transportation routing between China and Europe as a multi-criteria decision-making problem involving several conflicting decision criteria, and constructs a hybrid multi-criteria decision-making framework. The framework first establishes a route criteria utility function that considers transportation cost, transportation time, reliability, safety, convenience and environmental impact. Then, a Bayesian Best Worst Method (BBWM) is applied to determine customer preferences for the decision criteria. Finally, Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) is utilized to rank the multimodal routes. This enables a comprehensive assessment of the competitive relationship in China-Europe international multimodal transport in heterogeneous regions and heterogeneous customer markets, and provides decision-making references for the international multimodal transport organization optimization, logistics corridor construction and industrial layout planning of the China-Europe trade.
The results of the research find that in descending order of customer’s concern for decision-making criteria when choosing transportation services are transportation cost, transportation reliability, transportation time, transportation safety, transportation convenience and environmental impact. The choice of the optimal transport service is strongly related to geographical factors, with China-Europe maritime transport being more competitive in coastal and sub-coastal regions close to ports. The further western and deep land region is more inclined to choose CR Express service, the advantageous service areas of China-European maritime transportation and CR Express account for 64.5 percent and 35.5 percent, respectively. Meanwhile, most customers tend to choose more convenient CR Express departure stations or sea departure ports, breaking the principle of “geospatial proximity”. CR Express is absolutely attractive for high-value time-sensitive products such as electronics and medical devices, and has a competitive advantage in the transportation market for low-value time-sensitive products such as clothing and apparel. In contrast, China-Europe maritime transport is absolutely attractive to customers with low-value time-insensitive products such as ore fuels and competitively attractive to customers with high-value time-insensitive products such as machinery and transportation equipment. The sensitivity analysis shows that transportation time, cost and reliability attributes are the key factors affecting the competition between CR Express and China-Europe maritime transport, and the reliability test results also prove the robustness of the model proposed in this paper. Finally, this paper provides targeted policy recommendations for a range of stakeholders, including logistics operators, government departments and China-Europe customers.

Research on Network Derived Public Opinion Prediction Based on Uncertain Differential Equation with Jump

LIU Jiang, PENG Gang, SUN Xiaojun, LI Hongli

2025, 34(12):  204-209.  DOI: 10.12005/orms.2025.0395

Asbtract ( )   PDF (1227KB) ( )  

References | Related Articles | Metrics

With the exponential growth of internet energy, according to the China Internet Network Information Center (CNNIC), as of June 2021, China’s internet user base stood at 1.011 billion, including 1.007 billion mobile internet users. With the exponential growth of the internet,the spread of online public opinion has an increasing impact on people's lives and social stability. Network-derived public opinion refers to the characteristic of network public opinion evolving into new topics based on the original topic, which will replace the original topic and bring a “secondary impact” to network public opinion events. Currently, studying and analyzing the future trend of network derived public opinion is of great scientific significance not only for guiding and controlling public opinion,but also for maintaining social stability. And it helps government departments at all levels to develop scientific and reasonable public opinion guidance and control measures, and this can further create a healthy and harmonious online public opinion ecological environment.
The dynamic process of online public opinion dissemination can be seen as an uncertain process that changes over time. During the dissemination process, online public opinion is intertwined with rumors, revelations and other information. The active release and forwarding of these messages by netizens can easily cause the original online public opinion to deviate from the evolution law, leading to information alienation, sudden changes in the original online public opinion and a jumping uncertain process, so that the network generates public opinion in this way. Jumping uncertain differential equations are a new mathematical tool for describing dynamic systems in uncertain environments. Based on the analysis and research of potential derivative topics caused by network public opinion, this article fully considers some uncertain factors in the propagation process of network public opinion derivative events. According to the different stages of network public opinion propagation, a corresponding network public opinion propagation model stochastic jump-based uncertain differential equation is proposed.
Finally, based on the actual case of the “Snow Township Car Accident Case”, the actual data on its public opinion development is obtained through the Baidu Index. From the perspective of responding to derivative public opinion,the current focus is mainly on the embryonic and active stages of the development of primary public opinion. During the recession, due to the shift of attention from netizens, the government pays less attention to the derivative public opinion generated by it. However, when the original public opinion during the recession is replaced by new derivative topics, the lack of attention to derivative public opinion may have a worse impact on society. Therefore, the article focuses on exploring the numerical calculation and prediction analysis of the network derived public opinion decline stage model. The forecast outcomes demonstrate a strong alignment with the actual case propagation situation, thereby validating the efficacy of the proposed model.
The network public opinion with derivative public opinion, as a complex evolution form of public opinion development, is intertwined with the original public opinion. The three types of uncertain differential equation models with jumps proposed in this article reflect, to some extent, the propagation laws of network public opinion with derivative public opinion at different stages. The next step of work will attempt to introduce more parameters in these three types of models, making the prediction effect of the model more accurate compared to the actual situation.

Management Science

Research on Service Cooperation Modes and Mechanism of “Internet of Vehicles+Aftermarket”

ZHAI Yue, LUO Kainan, YANG Yefei

2025, 34(12):  210-217.  DOI: 10.12005/orms.2025.0396

Asbtract ( )   PDF (1443KB) ( )  

References | Related Articles | Metrics

The automotive aftermarket shows growth potential due to rising vehicle ownership and aging vehicles. However, demand uncertainty challenges aftermarket stores in attracting customers. Some aftermarket stores have been integrated into Internet of Vehicles (IoV) platforms to monitor vehicle operations, provide alerts, and recommend services, attracting customers to aftermarket stores. Companies like AliOS and SAIC are implementing this model. To expand the user base of IoV service platforms, three cooperation modes exist within the “IoV+aftermarket” service supply chain: (1)Promotion mode, where the IoV platform promotes itself through sending car repair and maintenance advertisements accurately, increasing user numbers. (2)Subsidy mode, where the platform offers subsidies to owners and users to enhance their utility and encourage platform usage. (3)The “promotion+subsidy” model, a combination of the first two, which simultaneously provides precision marketing and offers subsidies to attract more car owners and users. However, the current “IoV+aftermarket” cooperation model faces several issues: (1)The supply chain cooperation between IoV platforms and aftermarket manufacturers lacks clarity and standardization, leading to the inefficiency and potential conflict. (2)The long-term sustainability of these cooperation models is uncertain due to factors such as changing market demands, technological advancements and evolving customer preferences. (3)There is a need for more robust model analysis and insights to inform us of decision-making and optimize the effectiveness of these cooperation models.
Therefore, this paper researches the service supply chain involving the IoV platform and aftermarket stores, and finds optimal supply chain strategies in the context of Stackelberg games led by aftermarket stores and Stackelberg games led by IoV platforms by building mathematical models in the promotion mode, subsidy mode and “promotion+subsidy” mode. The study is also based on revenue sharing contracts, which is the current mode used by IoV platforms and aftermarket stores. In order to study the influence of external factors such as network externalities, promotion and service effort costs, and subsidy on supply chain decisions, this paper explores them through theoretical and numerical analysis. The findings are as follows:
(1)When the Stackelberg game is led by the IoV platform, if the unit cost of promotion is below a certain threshold, the subsidy mode should be adopted for the supply chain; whereas, if the unit cost of promotion exceeds the threshold, the promotion mode or the “promotion+subsidy” mode should be chosen. However, when the aftermarket store leads the Stackelberg game, the cooperation mode selection strategies among supply chain members may diverge, necessitating negotiation between the parties.
(2)The modes with subsidies or changing subsidy amount have a relatively weak impact on decision variables and profits. Except in scenarios where the aftermarket store leads the Stackelberg game, whether subsidies are combined with promotional activities only affects the service price of the IoV platform, but not profits. Additionally, when managers plan to adjust the service price of the IoV platform, they can achieve their goals by adjusting the subsidy amount. However, this approach cannot be used to increase profits.
(3)Regardless of the game-playing method employed, the simultaneous increase in service sensitivity and network externality effects significantly boosts the supply chain profits, but may potentially harm the profits of supply chain members. When the IoV platform leads Stackelberg game, as service sensitivity and network externality effects increase simultaneously, the “IoV+Aftermarket” supply chain can only increase profits of both the IoV platform and the aftermarket store by choosing a lower profit-sharing ratio. Otherwise, the aftermarket store’s profits may suffer significant losses, which will hinder the “IoV+aftermarket” supply chain cooperation.

Agricultural Subsidy Mechanisms Considering Heterogeneous Farmers and Production Capacity Constraints under Random Yield

NI Shuchen, FENG Chun, XIAO Yu

2025, 34(12):  218-225.  DOI: 10.12005/orms.2025.0397

Asbtract ( )   PDF (1240KB) ( )  

References | Related Articles | Metrics

Agricultural production is characterized by uncertain yields, which not only cause serious losses in farmers’ incomes, but also pose a threat to the supply of grain. In addition, it would discourage farmers from planting, resulting in the abandonment of land and a reduction in production. As a large agricultural country, China’s government has been providing a variety of subsidy policies for decades to protect farmers’ incomes from fluctuations in output and market prices, the most common of which are cost and target price subsidies. Cost subsidies can help farmers to reduce production costs and alleviate financial shortages in planting. However, it may lead to phenomena such as farmers receiving subsidies but not planting, or blindly expanding their planting area. Price subsidies are less disruptive to market prices and can be used directly to protect farmers’ incomes. The selection of appropriate and effective subsidies is important for agricultural development. Thus, the mechanism of each subsidy policy and how to choose an appropriate subsidy strategy are the subjects of this paper.
In China, agricultural production is still dominated by a smallholder economy. Farming households are characterized by large numbers, small scales and low levels of mechanization. Therefore, a game model of agricultural production decision-making that is consistent with the conditions of China’s smallholder economy has been innovatively constructed. We study a single-level agricultural supply chain of n small farmers facing the market directly, in which farmers have capacity constraints and heterogeneous production costs. First, considering the random yields of agriculture, we develop a Cournot game model for n farmers, where farmers make decisions on production with the optimization objective of maximizing their own profit, which is constrained by the production capacity A. The KKT condition is used to solve for the optimal decision input of each farmer, whereby the farmers are divided into three clusters. Supply chain performances such as total output, farmers’ surplus and consumers’ surplus are also calculated to analyze the impact of output uncertainty on agricultural supply chains. It is shown that, under capacity constraints, heterogeneous farmers with different unit production costs will make different cultivation decisions: high-cost farmers will choose to abandon cultivation; lower-cost farmers will participate in cultivation, and the lower the cost, the more they will put into production; and those with very low costs will produce in full capacity.
Then, a cost subsidy policy (reimbursing farmers for a portion of their costs) and a target price subsidy policy (compensating for the difference between the current market price and the target price, which is the historical average market price) are introduced in separate chapters, and their impact on supply chain performance is explored mathematically. Finally, the differences between cost subsidy policy and target price subsidy policy are compared. The result shows that, compared with the situation before the introduction of subsidies, (1)both cost subsidies and target price subsidies can increase the quantity of participating farmers, total production and consumer surplus. Moreover, cost subsidies are effective in achieving better results with less expenditure; (2)cost subsidies only increase the profits of high-cost farmers and reduce farmers’ total surplus. This is because subsidy policy incentivizes too many farmers to produce a particular crop, thus hurting prices and marginal profitability. In contrast, under the target price subsidy, each farmer’s profit will increase, and the total surplus of farmers will also increase. It can be verified that the target price subsidy policy reduces the impact of price volatility on farmers and has a direct effect on safeguarding their income.
These conclusions can provide a reference for the design of government subsidy mechanisms, and the government should choose appropriate strategies based on fiscal budget, purpose and the characteristics of different policies. If the government’s subsidy objective is to increase the cultivation rate of arable land, reduce land abandonment, or increase total production and consumer welfare, it should choose the cost subsidy, which is more economical and efficient. If the subsidy objective is to improve the total welfare of farmers, it should choose the target price subsidy. And if the subsidy objective is to reduce the income gap among heterogeneous farmers, a cost subsidy should be chosen.


Mechanism of Subsidy for Agricultural Product Supply Chain Considering Public Welfare in Adverse Weather

QIN Yanhong, ZHANG Yang, ZHANG Bing

2025, 34(12):  226-232.  DOI: 10.12005/orms.2025.0398

Asbtract ( )   PDF (960KB) ( )  

References | Related Articles | Metrics

In recent years, frequent adverse weather has brought great losses to agricultural production in China, and the randomness of market demand has further intensified the difficulty of agricultural product supply chain operation. To ensure stable production and supply of grain and important agricultural products, and improve the income guarantee mechanism of farmers, the government needs to implement subsidy for the agricultural product supply chain, and the weak nature of agriculture also requires that the government must implement subsidy. At the same time, the government builds a public welfare market for the purpose of “providing public services at a low price or for a low profit”, and members of the agricultural product supply chain actively perform public welfare functions to establish positive corporate image to obtain sustainable profit. Therefore, in adverse weather, it is of great significance to study the influence of public welfare and government subsidy on the optimal decision-making of agricultural product supply chain for proposing the optimal government subsidy strategy so as to reduce weather risks in agricultural production, accelerate the construction of public welfare market, and improve the operational stability of agricultural product supply chain.
In this paper, by describing the random profit function of adverse weather affecting the quality of agricultural products, the public welfare of the agricultural cooperative and company is introduced into the government subsidy mechanism of agricultural product supply chain, and a Stackelberg game model is constructed without subsidy as the benchmark. When the agricultural cooperative performs public welfare, the government implements output subsidy or purchase subsidy to the agricultural cooperative. When the company performs public welfare, thegovernment will implement procurement subsidy or sale subsidy for the company. Finally, through a sensitivity analysis, comparative analysis and numerical simulation, the effects of adverse weather, public welfare and various subsidies on the optimal decision-making of agricultural product supply chain are analyzed, and the optimal subsidy strategy is proposed from four aspects: quality-price ratio of agricultural products, consumer surplus, overall social welfare and the utilization efficiency of government subsidy funds.
The results show that: Firstly, the public welfare of the agricultural cooperative and company can alleviate the negative impact of adverse weather on the agricultural product supply chain, but the implementation of public welfare by the agricultural cooperative reduces their own profit, resulting in no incentive to implement public welfare. However, the stronger public welfare of the company will damage the company’s profit, resulting in the limited extent of the company’s public welfare.
Secondly, when the government subsidizes the agricultural cooperative, the incentive effect of output subsidy will be greater than purchase subsidy in terms of improving the quality-price ratio of agricultural products, consumer surplus and overall social welfare, but the utilization efficiency of government subsidy funds will be lower than purchase subsidy. When the government subsidizes the company, the incentive effect of procurement subsidy will be greater than sale subsidy in terms of improving the quality-price ratio of agricultural products, consumer surplus and overall social welfare, but the utilization efficiency of government subsidy funds will be lower than sale subsidy.
Finally, by comparing the output subsidy with the procurement subsidy, we find that the output subsidy has the best incentive effect, and the government should adopt the output subsidy for agricultural cooperative. By comparing the purchase subsidy with the sale subsidy, the utilization rate of the funds receiving the sale subsidy is the highest, so the government should adopt the sale subsidy.

Private Brand Encroachment Strategies of E-commerce Platform and its Impact on Manufacturer

FENG Zhongwei, LI Fangning, TAN Chunqiao, FU Duanxiang

2025, 34(12):  233-239.  DOI: 10.12005/orms.2025.0399

Asbtract ( )   PDF (1029KB) ( )  

References | Related Articles | Metrics

In the past two decades, private brands have been widely used as an important way to improve the competitiveness of retailers in the market. As a result, many e-commerce platforms have also introduced their private brands. However, the manufacturer brand products have already occupied a certain market share and established customer consistency, which causes e-commerce platforms to introduce a private brand with a lower price and quality compared to a manufacturer brand product (i.e., a low-quality private brand encroachment strategy of e-commerce platform). Interestingly, some e-commerce platforms have introduced high-quality private brands. Compared to low quality private brand encroachment, high-quality private brand encroachment can significantly increase consumer willingness to pay and enhance the bargaining power of e-commerce platforms; on the other hand, high-quality private brands also bring higher production costs. Therefore, when e-commerce platforms choose their private brand encroachment strategies, they need to balance the relationship between the quality and cost of the private brand.
It is worth noting that e-commerce platforms benefit from encroaching on market share of the manufacturer brand after introducing the private brand. However, there is controversy over the impact of e-commerce platforms introducing the private brand on manufacturers. On the one hand, introducing the private brand will encroach on the market demand of the manufacturer brand, which hurts manufacturers’ interests. On the other hand, when e-commerce platforms introduce private brands, e-commerce platforms will implement various advertising, recommendation and promotional activities to promote the sales of their private branded products. As a result, manufacturers can benefit from the private brand encroachment as a free rider. E-commerce platforms may choose different private brand encroachment strategies. It is necessary to explore the impact of different private brand encroachment strategies on manufacturers.
This paper considers three encroachment strategies of e-commerce platform: no encroachment, low-quality private brand encroachment and high-quality private brand encroachment. Then we explore the following questions: Do e-commerce platforms always choose to introduce the private brand? Does e-commerce platform’s private brand encroachment always harm the interests of the manufacturer? If the private brand encroachment strategy is detrimental to the manufacturer, which encroachment strategy causes less damage to it? To solve the above problems, we consider a supply chain composed of a manufacturer and an e-commerce platform, where consumers have quality preference for products and the e-commerce platform may introduce the private brand. Firstly, when the product quality of the private brand is an exogenous variable, we will explore the impact of encroachment decisions on the manufacturer and the conditions for achieving a win-win between the e-commerce platform and manufacturer. Secondly, when the product quality of private brand is an endogenous variable, we will explore the private brand encroachment strategies and product quality decision of e-commerce platform, and then analyze the impact of the private brand encroachment decision on the manufacturer.
The research results are shown as follows: (1)When the quality of the private brand is an exogenous variable, private brand encroachment will be beneficial for the e-commerce platform, and may be beneficial for the manufacturer under certain conditions. (2)When the quality of the private brand is an endogenous variable, the e-commerce platform will benefit from private brand encroachment that is harmful to the manufacturer. When the quality of the manufacturer brand is low (high), the introduction of the high (low) quality private brand will be more beneficial for e-commerce and less harmful for the manufacturer. (3)When the quality of the private brand is an endogenous variable and the quality of the manufacturer brand is high, if the quality cost of the product is low or the product category size is high, compared to the low quality private brand encroachment, it will be more advantageous for the e-commerce platform to introduce the high-quality private brand, which is different from the existing research result, that is, the significant quality difference between two types of the brand product is more advantageous for the e-commerce platform.