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论文编号：	15315
作者编号：	2120233657
上传时间：	2025/6/5 9:35:43
中文题目：	分散式库存系统中零售商需求信息共享的机制研究
英文题目：	Research on the Mechanism of Retailers'''' Demand Information Sharing in Decentralized Inventory Systems
指导老师：	杨静蕾
中文关键字：	分散式库存系统；需求信息共享；诚信因素；惩罚机制
英文关键字：	Decentralized inventory system; Demand information sharing; Integrity factor; Penalty mechanism
中文摘要：	本研究聚焦分散式库存系统，旨在解决零售商需求信息共享面临的困境，探索有效共享机制，提升供应链协同效率。采用文献研究法梳理相关理论与研究现状，运用数学建模法构建报童模型，结合对比分析法剖析不同需求信息共享情景下零售商的决策与利润变化。 研究构建了无需求信息共享、确定诚信和不确定诚信三种情景的博弈模型。无需求信息共享时，零售商独立决策，依据自身对市场需求的预估确定订货量，以最大化期望利润，但易引发库存积压或缺货问题，导致供应链协同性差。确定诚信情景下，零售商共享真实需求信息并开展库存合作，通过对多种库存情景的分析，计算出不同条件下的最优订货量和最大期望利润。研究证明，此情景下订货决策更接近真实需求，整体利润显著提升，有效降低了需求不确定性和库存成本。在不确定诚信情景中，论文进一步分只有一个零售商存在欺骗动机和两个均有欺骗动机两种情况讨论。当只有一个零售商存在欺骗动机时，发现其一般有分享虚假信息的倾向，仅在特定临界条件下才会分享真实信息；当两个零售商均有欺骗动机时，引入不信任度和惩罚上限机制，得出合适的惩罚上限可诱导零售商分享真实需求信息的结论。 研究表明，需求信息共享能够降低需求不确定性、减少库存成本并提高供应链整体效率和利润。诚信是实现高效协同的关键，而惩罚机制可有效抑制零售商的欺骗行为，保障信息真实共享。本研究的创新点在于拓展了零售商之间需求信息共享的新研究视角，引入诚信因素设定不同情景并深入分析，创新了需求信息共享控制方式。研究成果为企业优化需求信息共享策略提供了理论支持，有助于推动供应链管理实践发展，也为后续研究奠定了基础。未来的研究可考虑将更多的供应链成员纳入模型之中，使模型结构更加贴近复杂的现实供应链场景。
英文摘要：	This study focuses on the decentralized inventory system, aiming to address the dilemmas faced by retailers in sharing demand information, explore effective sharing mechanisms, and enhance the collaborative efficiency of the supply chain. The literature research method is adopted to sort out relevant theories and research status quo. The mathematical modeling method is used to construct the newsvendor model, and the comparative analysis method is combined to analyze the changes in retailers' decisions and profits under different demand information sharing scenarios. The study constructs game models for three scenarios: no demand information sharing, certain integrity with demand information sharing, and uncertain integrity with demand information sharing. When there is no demand information sharing, retailers make independent decisions, determine the order quantity based on their own estimates of market demand to maximize expected profits. However, this is likely to lead to problems such as inventory backlogs or stockouts, resulting in poor supply chain collaboration. In the scenario of certain integrity, retailers share real demand information and carry out inventory cooperation. Through the analysis of various inventory scenarios, the optimal order quantity and maximum expected profit under different conditions are calculated. The study proves that in this scenario, the order decision is closer to the real demand, the overall profit is significantly improved, and the demand uncertainty and inventory costs are effectively reduced. In the scenario of uncertain integrity, considering the possible deception motives of retailers, the analysis is carried out in different situations. When only one retailer has a deception motive, it is found that it generally has a tendency to share false information and will only share real information under specific critical conditions; when both retailers have deception motives, the mechanism of distrust degree and penalty upper limit is introduced, and the conclusion is drawn that an appropriate penalty upper limit can induce retailers to share real demand information. The study shows that demand information sharing can reduce demand uncertainty, decrease inventory costs, and improve the overall efficiency and profits of the supply chain. Integrity is the key to achieving efficient collaboration, and the penalty mechanism can effectively suppress the deceptive behavior of retailers and ensure the real sharing of information. The innovation of this study lies in expanding the new research perspective of demand information sharing among retailers, introducing integrity factors to set different scenarios and in-depth analysis, and innovating the control mode of demand information sharing. The research results provide theoretical support for enterprises to optimize the demand information sharing strategy, which is helpful to promote the development of supply chain management practice and lays a foundation for follow-up research. Future research can consider incorporating more supply chain members into the model to make the model structure closer to the complex real-world supply chain scenarios.
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