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.

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.

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.

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.

在缺乏精细化管理下医用耗材的产能过剩和不合理使用了造成资源浪费和环境污染,且当前信息化管理的建设阻碍也使各方无法积极自主地参与数据治理与协同.为了解各方在该问题上的考虑因素并促使合作,通过演化博弈理论深入研究医用耗材制造商、医疗机构和政府监管部门的收益和成本,构建了三方演化博弈模型探讨交互机理并对各方提出了管理建议.结果表明:医用耗材制造商和医疗机构在决策上受到信息化投入产出、成本、信息协同标准、政府奖惩力度的影响.进一步基于结论对各方提出管理建议:政府部门应该在不同投入阶段制定合适的政策促使各方破除技术壁垒,降低信息化建设的边际成本;医用耗材制造商和医疗机构应当展开交流合作,识别技术难点并积极解决.

平台供应链为传统供应链数字化提供新机遇。本文针对平台赋能供应链数字化建设问题，运用演化博弈理论构建制造商、供应商和平台组成的平台供应链系统，对系统各主体的策略稳定性进行分析，运用软件仿真模拟供应链上下游企业参与平台的影响因素。研究发现：企业的策略选择主要受到平台收费水平、赋能能力以及合作成本影响，平台的服务模式选择则与收费水平以及合作成本相关。

本文基于实际配送中存在的配送中心中转货物类型不同的问题,提出共享资源、合并下游订单、中途取货、半开放式回程策略,考虑使用异质车辆,以时间窗、载重限制、需求点容纳能力为约束条件,构建了基于联合配送的多目标路径优化模型.根据模型特征,使用混合自适应遗传算法,设计三阶段解码法和海明距离过滤机制,利用模拟退火算法生成初始解,增强算法的局部与全局搜索能力.通过实例分析,验证了模型即算法的有效性,表明联合配送模式能有效降低配送成本之余提高配送与用车效益.

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.

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.

BACKGROUND:Based on the Chinese model of medical-elderly care integration, this paper aims to explore the impact of different investment levels on the performance of the medical-elderly care integrated institutions.METHODS:Using the method of system dynamics, this paper establishes the performance evaluation model of medical-elderly care integrated institutions, sets the system element input, service level, and policy support as the key factors, and uses Vensim PLE software for simulation.RESULTS:The three key factors have different degrees of positive impact on the performance of medical-elderly care integrated institutions. On the whole, policy support has the most significant impact on the performance of institutions, followed by the level of medical-elderly care integrated services. Institutional input mainly has a great impact on the performance of institutions in the early stage. In addition, the model simulation results also show the emergence effect: the improvement rate of institutional performance under the comprehensive simulation is higher than the sum of the improvement rates under the separate action of single factor.CONCLUSION:Government policies have played an important role in promoting the development of medical-elderly care integrated institutions. The service level and resource input can effectively promote the performance of medical-elderly care integrated institutions. Institutions should formulate development strategies from a systematic perspective, and pay attention to the integration of "medical" and "elderly care" resources.

Abstract Background: With the continuous growth of the elderly population, the pension burden in Guangzhou has increased in recent years, and the aging problem cannot be ignored. This study predicts the relationship between the number of elderly people over 60 years old in Guangzhou and the supply and demand of beds and nursing workers in elderly care institutions in the next ten years and provides a reference for decision-makers to regulate the supply of elderly care resources and formulate policies.Method: Based on the data of the elderly population in Guangzhou from 1990 to 2014, this paper establishes an ARIMA prediction model, uses the data from 2015 to 2020 to verify the validity of the model, and further predicts the number of elderly people, the demand for elderly care facilities and nursing workers from 2021 to 2030 based on specific calculation formulas. Result: In the future, Guangzhou's aging degree will continue to deepen. It is estimated that by 2030, Guangzhou's elderly population will be over 2.9 million. The increase in the number of elderly people puts forward higher requirements for the facilities and human resource supply of elderly care institutions. The prediction results of this study show that the demand for elderly care beds and nursing workers in Guangzhou will exceed 130,000 and 30,000 respectively by 2030.Conclusion: The difficulties in the construction of institutional facilities and the loss of human resources coexist. In the future, the gap between the supply and demand of elderly care facilities and nursing workers will be further expanded. Guangzhou urgently needs to find more effective ways to deal with the increasingly serious aging problem.

In recent years, online shopping of medical device products has gradually normalized, promoting the acceleration of medical e-commerce. This improves the channel layout, thus expanding the consumer market and increasing the enterprise performance. Considering a two-stage supply chain composed of a medical device manufacturer and a dealer with more accurate demand information, this paper focuses on the online channel expansion strategy with information sharing. Four sales modes are developed: traditional mode, dual-channel mode, O2O mode and multi-channel mode. For each mode, the information sharing motivation and influence of online expansion on enterprise profits and patient surplus are explored. Interestingly, only under multi-channel mode, accompanied by a median level of public welfare or extreme consumer valuation, can dealer has information sharing motivation. Moreover, the online entry decision of dealer or manufacturer is independent of information sharing strategy. Specifically, O2O and multi-channel modes are more conducive to dealer, while manufacturer prefers the dual-channel and multi-channel modes. Furthermore, online expansion can not only become a “win-win” situation for manufacturer and dealer, but also increase the patient surplus.

以B2 C电商零售市场为研究主体,探究感知客户服务满意度影响因素及其重要性权重大小的方法,识别客户需求从而提高企业物流服务资源配置效率.通过对家电产品在线评论数据进行高频词的统计,建立语义社会网络,提取与售后服务有关的评论要素,识别评价维度.将评论语言转换为结构化数据,人工对训练样本提及到的评价维度进行识别与权重编码,训练神经网络并运用模型自动预测各评价维度在评论中权重大小.最后,对不同产品客户群体的评论数据进行满意度影响因素权重的分析,得到结论:随着产品属性特征的改变,满意度影响因素的重要性也会有所变化.服务企业应根据不同群体满意度影响因素的权重差别,进行对应服务资源的配置,从而提供有针对性的服务.

为降低农村物流成本,助力"快递下乡"工程的实施,在考虑重量、体积、订单、路径等约束条件下,以总利润最大化为目标构建基于共同配送模式的农村快递车辆调度模型;并提出采用基于随机修复非可行解策略的遗传算法求解该模型,同时在算法中引入多种群机制以提高求解的效率和效果.最后,结合实际算例对模型进行验证;设计不同订单规模的仿真算例对算法性能进行分析.研究结果表明:共同配送策略能从全局优化的角度合理配置区域内物流资源,与独立配送模式相比可降低30％的配送成本.仿真实验表明:相较于标准遗传算法,改进遗传算法能快速地搜索到更优的满意解,可以有效求解共同配送模式下的农村快递车辆调度问题.

针对低碳物流配送中心选址问题,结合需求不确定的现实问题,采用随机规划理论,建立以包含碳排放成本在内的总成本最低为目标、以不确定性需求为随机约束的选址模型,通过实例运用验证模型的有效性,能够为企业在实际市场需求波动下的选址投入以及收益提供一定的参考价值.另外,对不确定性需求的置信水平和标准差进行灵敏度分析,案例分析结果显示:碳排放因素与需求的不确定性对配送中心选址结果存在较为显著的影响;选址总成本与需求的不确定程度密切相关,碳排放成本也会随着需求的变动产生较大的波动.

基于博弈论的思想,针对现实中的制造商委托零售商回收废旧产品以再次加工的逆向供应链定价问题展开研究.首先分别构建了制造商主导的Stackelberg博弈分散式定价模型以及合作博弈的集中定价模型,通过推导得到一些推论,从理论上证明了合作博弈能够给供应链带来更多利润,同时也能让消费者获益.针对合作博弈模型,设计了分配方案来实现合作利润的分配以满足所有供应链成员的要求.最后通过数例计算验证了推论的正确性.

选取2014-2017年河南省26个国家级农村电商示范县作为研究对象,在借鉴前人指标选取标准的基础上,结合农村电商背景,创造性地构建出农村电商物流评价指标体系,并运用DEA的C2R和BC2模型对河南省农村电商示范县的物流效率进行分析评价.结果表明,目前河南省农村电商示范县的物流效率整体偏低,物流资源分配不均和投入产出结构不合理导致示范县投入冗余和产出不足现象较为严重.从2014年到2017年,入围示范县物流效率逐渐上升,说明农村电商物流发展趋势整体向好.建议物流效率较低的示范县通过合理化其物流建设的投入产出结构,优化其电商物流产业的发展模式,以提升示范县的整体物流水平.

针对粤港澳大湾区港口群内部港口间经济腹地、货种竞合关系的复杂性,基于社会网络理论构建粤港澳大湾区港口群多重竞合网络,以港口为社会实体研究港口竞合关系及其变化.结果表明2000年到2016年,粤港澳大湾区港口群各港口竞合关系并存,但恶性竞争不断优化,合作趋势明显,各港口分工日趋清晰,港口群正走向层次化,合作化的道路.