The empowerment provided by third-party intelligent information platforms has emerged as a crucial driving force for facilitating the digital transformation of supply chain enterprises. Whether and how intelligent information platforms are engaged is a key to the digital transformation of the supply chain. This essay endeavors to establish theoretical underpinnings for supply chain digital transformation and assist enterprises in resolving transformation-related issues. Based on the tripartite evolutionary game, this essay analyzes the digital transformation of supply chain from the behavioral strategies of the third-party intelligent information platform, manufacturers and retailers, respectively. Combined with numerical simulation analysis, we explored the digital transformation patterns and characteristics of supply chains under different scenarios. The results suggest that joining an intelligent information platform will always be a balanced strategy for supply chain digital transformation, and that an increase in the level of platform enablement will accelerate the process, while potential threats to cybersecurity will slow it down. However, manufacturers may also build their own platforms when their initial digitization level is high or the cost of building their own platforms is not prohibitive, resulting in higher word-of-mouth benefits and digital longevity. This paper provides new perspectives for analyzing the impact of intelligent information platforms on supply chain digital transformation decisions, and ultimately proposes practical operation and management recommendations for related practices.

Marketing strategies developed by merchants on e-commerce platforms are essential tools to increase sales. To attract consumers, merchants often design multiple promotions for their products and allow them to be stacked. However, combining multiple promotions is not always rational, as some may conflict with each other. In these cases, merchants may suffer unnecessary losses, i.e., the combinations of promotions may have loopholes, which need to be urgently addressed. Thus, this paper discusses the risk assessment and optimal design of promotional combinations. A risk assessment model is established by designing heuristic rules and using machine learning. Using this model, we analyze collected marketing data to identify and categorize various types of promotional loopholes. We then construct corresponding mixed-integer linear programming (MILP) optimization models that aim to maximize merchant profits while minimizing risk. Simulation experiments demonstrate the effectiveness of our predictive model in detecting promotional loopholes. Moreover, our optimization framework provides optimal promotional combination strategies that account for both profitability and risk. Compared to optimizing single promotions in isolation, our approach significantly reduces the likelihood of unreasonable promotional strategies. This provides a practical and effective tool for merchants to assess the risks of their marketing strategies and to optimize promotion parameters before implementation.

Recently, online shopping for medical products has gradually normalized, promoting the acceleration of medical e-commerce. This improves the channel layout, subsequently broadening the consumer market and augmenting enterprise performance. Considering a two-stage supply chain composed of a medical manufacturer and a dealer with more accurate demand information, this paper focuses on the online channel expansion strategy with information sharing. Four distinct sales modes are developed: traditional sales, dual-channel, O2O and multi-channel. For each modality, an investigation of the information sharing motivation and impact of online expansion on enterprise profits and patient surplus is conducted. Intriguingly, a dealer may be incentivized to share information exclusively under multi-channel mode, accompanied by either a moderate level of public welfare or high online preference. Moreover, the online entry decision is independent of information sharing strategy. Specifically, O2O and multi-channel are more conducive to dealer, whereas manufacturer prefers the dual-channel. In addition, online expansion can not only prove to be a “win-win” situation for both manufacturer and dealer, but can also increase patient surplus.

To effectively address the dual challenges of insufficient motivation for digital transformation (DT) among small and medium-sized enterprises (SMEs) and low collaboration efficiency in the manufacturing supply chain within the context of the digital economy, this paper focuses on how government subsidy policies can promote the empowerment behavior of core manufacturing enterprises (CMEs) to resolve the DT difficulties of SMEs and drive the overall upgrade of the manufacturing industry. Based on evolutionary game theory, a three-party evolutionary game model involving the government, CMEs, and SMEs is constructed. The evolutionary stability strategies of the three parties under different scenarios are explored, and the evolutionary stability of system strategies under single-factor and two-factor interactions is analyzed through MATLAB simulations. The research results indicate that (1) the intensity of government subsidies shows an inverted U-shaped impact on their effectiveness. (2) The government subsidy to CMEs can not only directly incentivize empowerment but also indirectly promote the DT of SMEs. (3) CMEs converge to the empowerment strategy faster than SMEs, while SMEs are more sensitive to insufficient subsidies. (4) SMEs have weak self-transformation capabilities, and their willingness to undergo DT is significantly more influenced by external factors than by internal factors. The above findings can help to clarify the interactive relationships among the government, CMEs, and SMEs in the DT process and provide valuable suggestions from multiple perspectives to promote the SMEs’ DT.

Subject to pressures from resource exhaustion and environmental pollution, many countries have aimed to replace fossil fuels with renewable energy as part of their decarbonization strategy. In the post-pandemic era, countries are making efforts to explore a sustainable mode of economic development that features low resource consumption and less environmental pollution. Consumers are increasingly concerned about the environmental friendliness of energy products. In this study, we formulated four solutions for energy-saving optimization and control of the waste heat supply chain to conserve energy and compared the impact of a profit-as-incentive energy efficiency strategy and an energy efficiency incentive strategy on energy efficiency in the waste heat supply chain. Government agencies and enterprises can adopt a suitable strategy with the best current social and economic benefits to manage waste heat recovery. The profit-as-incentive energy efficiency strategy is more favorable for enterprises in the early stage of development. Under dual pressures of social attention to green energy and environmental protection, government agencies may adjust energy conservation policy to encourage enterprises to choose an energy efficiency incentive strategy to increase energy conservation.

As the aging society progresses, it is particularly important to strengthen the sharing of silver economy data to promote the development of the silver economy. This paper focuses on analyzing the mechanism by which digital platforms promote silver economy data sharing and constructs an evolutionary game model that includes government departments, digital platforms, enterprises, and elderly people. On this basis, the stability of the strategies of each subject in the system is analyzed, and the influence of key parameters is also discussed. The simulation draws the following conclusions. Firstly, initial strategy proportions significantly influence evolutionary directions. Higher initial proactive participation increases the probability of convergence to the optimal state. Secondly, digital platforms are driven by government regulation intensity, user complaint probabilities, and reputational losses. Increasing fines and user complaint probabilities incentivize platforms to offer high-quality protection. Thirdly, government departments can initially incentivize enterprises and elderly people to participate in data sharing through subsidies and tax incentives and build a long-term driving mechanism by improving regulatory mechanisms and enhancing digital literacy among the elderly people. The research results can serve as a reference for government departments to promote data sharing in the silver economy.

Graph neural networks are a subfield of graph learning within artificial intelligence. Knowledge graphs enhance information interconnectivity and comprehensibility by representing knowledge in a structured and semantic manner. They provide richer semantic information compared to traditional datasets by explicitly modeling relations and entities. Knowledge graphs play a key role in powering search engines, answering questions, and providing recommendations. A major focus of research in this area is link prediction, which aims to uncover missing relationships between entities that have not yet been recorded. Knowledge graph embedding, a highly efficient and scalable approach for link prediction. This paper focuses on distance-based models for KGE, which are lightweight, easy to train, and geometrically interpretable. We propose a new model, TriRH, which uses tri-relation vectors on hyperplanes to enhance predictive accuracy and overall model performance. Experiments on benchmark datasets show that TriRH achieves better performance than existing models across multiple metrics.

在全球大力开展节能减排活动背景下,调整运输结构、大力发展多式联运可以有效降低碳排放量.本文通过对比分析不带低碳政策与碳补偿机制下的多式联运路径选择模型,研究碳补偿政策对于降低二氧化碳排放的效果,并且在此基础上对不同的碳价进行分析,发现不同碳价对于多式联运路径选择与碳排放量具有重要影响,可以为政府制定低碳政策提供一定的参考性意见.通过使用鲸鱼算法对多式联运路径选择模型进行求解,并将遗传算法、模拟退火算法与其进行对比分析,验证鲸鱼算法在求解此类问题的适用性与优缺点,为解决多式联运路径问题提供新的思路方法,往后也可以进行进一步深入研究.

为探究混合碳政策影响下生鲜电商供应链的碳减排策略,基于系统动力学,通过模拟仿真对比了不同参数设置的碳政策对生鲜电商供应链的影响.研究得出:①生产与运输环节的碳排放量占比最大,供应链减排策略的制定应着重关注;②碳税对供应链减排效果的影响呈一个上凸曲线,过高或过低的碳税价格都会使得碳减排水平降低;③碳交易市场价格与供应链减排水平呈负相关,交易价格越高碳减排水平越低.根据研究结果,为政府与供应链企业提出了加大减排投入、动态调整碳政策、搭建政企信息沟通平台的建议.

This study investigates a sustainable three-echelon supply chain structure (supplier-retailer-customer) from the retailer's perspective for a perishable product where: (1) the retailer pays the purchase cost to the supplier in the form of an advance-cash-credit (ACC) payment while granting their customers a partial credit, (2) the supplier offers a price discount to the retailer to facilitate sales, (3) the deterioration rate of items increases over time due to an expiration date, (4) the customer is allowed a partial delay in orders with a fixed market tolerance period, and (5) the supply chain management (SCM) structure takes consideration of government cap-and-trade regulation. This paper intends to establish the retailer's optimum cycle time, selling price, and inventory period simultaneously to maximize his/her total profit. An efficient algorithm has been constructed to find optimal solutions. Two examples have been presented to validate the proposed model. Furthermore, a comparative analysis of the four different payment methods (upstream ACC, advance, cash, credit, and downstream partial credit payments) has been carried out and sensitivity analyses have been performed to gain managerial insights. Our numerical results reveal that with the increase in the discount rate, the selling price reduces significantly, however, the retailer's profit increases tremendously. The reason for this phenomenon is that a higher discount rate can lead to a higher profit for the retailer, hence, the retailer may set a lower price to increase sales. Many retailers desire a high discount rate, which is associated with the upstream advance and downstream partial credit payment; however, the computational results show that this payment method causes significant damage to the environment. Hence, two payment methods (upstream ACC, credit, and downstream partial credit payments) are recommended. The reason is that these two payment types bring more profits to the retailer and result in less damage to the environment.

探讨商品组合在医院医用高值耗材需求预测中的应用。采用Apriori算法对某医院高值医用耗材数据进行分析，挖掘出潜在有用的商品组合；然后基于商品组合进行分析，采用线性回归、BP神经网络等机器学习方法进行数据建模，从而对商品组合中某型号的高值耗材进行需求预测模型构建，以实际算例验证这一模型的精确性。

Population aging and a shortage of medical resources are driving the growth of home health care (HHC). Many HHC studies have focused on developing an optimal routing plan to reduce operation costs; however, few have addressed the attendant long-term sustainability concerns, such as HHC center locations and carbon emissions from transportation. This paper proposes a home health care location-routing problem with a mixed fleet of electric and conventional vehicles that considers battery swapping stations. It aims to simultaneously determine the locations of HHC centers, the scheduling of caregivers with respect to skill requirements, and a routing plan for a mixed fleet under specific time windows, load capacities, synchronized visits, and driving ranges. To address this problem, the paper proposes a novel competitive simulated annealing (CSA) algorithm in which a series of problem-specific effective local search operators expand the solution space of the CSA algorithm, with a competitive mechanism to adaptively adjust these operators to accelerate convergence speed and improve exploration ability. To enhance the exploitation ability, it employs a modified simulated annealing algorithm with a heating strategy and variable neighborhood descent. Comparison experiments are conducted to verify the superior performance of the CSA algorithm in comparison to existing algorithms and the Gurobi solver. Finally, sensitivity analyses are performed on key factors and provide some new managerial insights.

As the aging of Chinese society continues to deepen, it is particularly important for the development of the national elderly care service industry to further strengthen the government’s supervision of private pension institutions and improve their management awareness of standardized operations. The strategic behaviors among the participants of senior care service regulation have not been well studied yet. In the process of senior care service regulation, there is a certain game association among three stakeholders, namely, government departments, private pension institutions, and the elderly. This paper firstly constructs an evolutionary game model including the above three subjects and analyzes the evolutionary path of strategic behaviors of each subject and the evolutionary stabilization strategy of the system. On this basis, the feasibility of the evolutionary stabilization strategy of the system is further verified through simulation experiments, and the effects of different initial conditions and key parameters on the evolutionary process and results are discussed. The research results show that (1) There are four ESSs in the pension service supervision system, and revenue is the decisive factor that affects the evolution of the stakeholders’ strategy. (2) The final evolution result of the system is not necessarily related to the initial strategy value of each agent, but the size of the initial strategy value will affect the rate of each agent’s evolution to a stable state. (3) The increase in the success rate of government regulation, subsidy coefficient and punishment coefficient, or the reduction in the cost of regulation and the fixed subsidy for the elderly can effectively promote the standardized operation of private pension institutions, but the large additional benefits will lead to their tendency to operate in violation of regulations. The research results can provide reference and a basis for government departments to formulate the regulation policy for elderly care institutions.

Waste heat recovery emerges as a promising solution for addressing energy shortages and environmental pollution. However, its viability is hampered by high operational expenses, formidable technical barriers, and substantial capital requirements, rendering it economically daunting without government support. A considerable volume of underutilized low-grade waste heat persists, and viable commercial solutions are wanting. In response, we propose two waste heat recovery modes. One involves entrusting distributors to collectively engage in waste heat recovery alongside suppliers who directly recover waste heat, called Supper-Distributor (SD) mode. The other emphasizes suppliers focusing on their core products while entrusting distributors and specialized third-party waste heat recovery companies to collaboratively manage waste heat recovery, with the aim of fostering waste heat recovery proliferation and expanding its market scope, called Third-party-Distributor (TD) mode. Numerical analysis based on real-world data substantiates the pivotal roles of government subsidies, supply chain decision-making, and the market dimensions of waste heat recovery in the profitability of the waste heat recovery supply chain. Following optimization of both modes, we conclude that the cost of recovered waste heat is lower than that of electricity for end-users. Furthermore, a waste heat recovery landscape spearheaded by third-party recycling companies outperforms others in terms of energy conservation and emissions reduction.

随着中国人口老龄化程度加深,老年群体的门诊服务需求日益增加,对于影响老年人门诊服务利用的因素亟待探究.本文利用2018年中国健康与养老追踪调查数据基于负二项回归模型分析研究影响中国老年群体的服务利用次数的因素,先通过构建影响因素体系提取潜在影响因素并构建数据集,采用负二项回归模型对数据集进行拟合识别显著的影响因素.结果表明,老年群体对门诊服务利用程度不高,有医保、家庭人均收入水平高、医疗机构平均距离远、患有慢性病、自评健康状况差老年人平均利用门诊服务的次数更多.

文章研究了在单配送中心背景下的低碳冷链共同配送路径优化问题.首先提出低碳冷链共同配送的背景,以车辆配送路径为突破口,建立了以包含车辆启动成本、货损成本、制冷成本、碳排放成本和时间惩罚成本在内的总配送成本最小化为目标函数的模型,并设计遗传算法进行求解.而后设计案例,使用模型与算法对问题进行分析.最终得出结论:合理选择车辆配送路径,可以有效降低企业总配送成本,同时减少碳排放.文章通过探讨冷链物流与低碳物流之间的平衡关系,为低碳冷链物流发展提供思路.

Background The needs of outpatient services, a key accessible health resource, are increasing in an increasingly aging population in China. So rational allocation of outpatient service resources to meet older people's health needs is a key to improving their health level. Objective To explore the factors affecting the utilization of outpatient services in the elderly, to provide feasible suggestions for improving older people's health level and the allocation of medical resources. Methods This study was conducted in 2021 using the data of older people (≥60 years old) attending the CHARLS 2018. The number of using outpatient services in these people in the past month prior to the CHARLS 2018 and associated factors were studied using a system developed based on the framework of Anderson's Behavioral Model of Health Services Use, with sex, age, marital status, the type of hukou (rural or urban), education level, smoking and drinking were classified as predisposing factors, health insurance, monthly household income per capita and the average distance from home to medical institutions as enabling factors, and chronic disease prevalence and self-rated health as need factors. The zero-inflated negative binomial regression (ZINB) was used to identify factors associated with the use of outpatient services. Results In all, 9 551 older people were included for analysis. Among them, non-users of outpatient services in the past month numbered 8 038 (84.16%), and one-time, two-time, and three or more-time users numbered 749 (7.84%), 367 (3.84%), and 397 (4.16%), respectively. The number of using outpatient services differed statistically by the type of hukou, education level, smoking, drinking, health insurance prevalence, monthly household income per capita, chronic disease prevalence, and self-rated health status (P<0.05). The results of Logit regression model (one part of the ZINB) in fitting the data indicated that older age, high school education or above, having health insurance, suffering from a chronic disease and poor self-rated health were associated with increased probability of utilizing outpatient services (P<0.05). And the results of negative binomial count model in fitting the data indicated showed that the age of 70-74 years was associated with decreased probability of utilizing outpatient services, while an average distance of greater than 1 km but less than 10 km between home and medical institutions, and three types of self-rated health (good, poor and very poor) were associated with increased probability of utilizing outpatient services (P<0.05) . Conclusion Our study indicates that the use of outpatient services was insufficient in these older people, which was associated with predisposing, enabling and need factors. To improve this, it is suggested to rationally strengthen the provision of outpatient services to vulnerable groups with underuse of such services and individuals with needs of high-quality services. In addition, attention should be given to chronic disease management and self-rated health of the elderly to improve their awareness and level of health management.

本文在低碳经济以及交通路况基础上,鉴于生鲜产品易腐性和冷链过程高排放特性,以载重量、时间窗等为约束条件,综合分析生鲜产品冷链物流配送过程的固定成本、制冷成本、货损成本、惩罚成本、运输成本以及碳排放成本,构建考虑电动配送车在内的总成本最小和客户满意度最大的双目标多车型低碳冷链配送路径优化模型,改进传统遗传算法中的遗传操作,结合邻域搜索和模拟退火算法加快收敛速度、提高算法的全局搜索能力.最后通过实例分析,验证了模型的有效性,取得良好路径优化效果,降低企业冷链配送成本并保证满意度,表明模型同时满足企业与社会效益,能为冷链物流的运输配送过程节能减排提供一定的决策支持.