Abstract Industrial supply chain management is facing major challenges associated with supplying perishable products, particularly healthy and high-quality foods with a limited shelf-life that require specific inventory holding conditions. Consumers usually prefer product freshness over its price when making buying decision since they will be able to keep it for a longer term. Demand is considered to be a function of the selling price and reference price as well as the freshness of products associated with their expiry dates and safety stock levels. The model is developed taking advantage of Double Interval Grey Numbers (DIGN) to more accurately formulate consumer behavior and enhance quality of the analytical results in practical decision-making. Moreover, the optimal retailer decision provided by the model is discussed when losing the market share and when there is no consistency between product freshness and price. The present study is conducted to develop and propose a game-theoretic model for the joint decisions made on pricing and lot-sizing by retailers of perishable goods. Numerical experiments confirm the consistency of the optimal pricing and inventory strategies and show that the system was uniquely balanced for different demand scenarios. Sensitivity analysis is also presented to identify the structural characteristics of the problem and understand the impact of different parameters on optimal decision-making.

中文翻译：

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易腐货物的最优批量和价格：使用双区间灰色数的新型博弈论模型

摘要 工业供应链管理正面临与供应易腐烂产品相关的重大挑战，尤其是保质期有限、需要特定库存保持条件的健康和优质食品。消费者在做出购买决定时通常更喜欢产品的新鲜度而不是价格，因为他们可以将产品保存更长时间。需求被认为是销售价格和参考价格以及与其保质期和安全库存水平相关的产品新鲜度的函数。该模型是利用双区间灰度数 (DIGN) 开发的，可以更准确地制定消费者行为并提高实际决策中分析结果的质量。而且，当失去市场份额以及产品新鲜度和价格之间没有一致性时，讨论模型提供的最优零售商决策。本研究旨在开发和提出一个博弈论模型，用于易腐货物零售商对定价和批量大小的联合决策。数值实验证实了最优定价和库存策略的一致性，并表明该系统对于不同的需求场景具有独特的平衡性。还提出了敏感性分析，以识别问题的结构特征并了解不同参数对最优决策的影响。本研究旨在开发和提出一个博弈论模型，用于易腐货物零售商对定价和批量大小的联合决策。数值实验证实了最优定价和库存策略的一致性，并表明该系统对于不同的需求场景具有独特的平衡性。还提出了敏感性分析，以识别问题的结构特征并了解不同参数对最优决策的影响。本研究旨在开发和提出一个博弈论模型，用于易腐货物零售商对定价和批量大小的联合决策。数值实验证实了最优定价和库存策略的一致性，并表明该系统对于不同的需求场景具有独特的平衡性。还提出了敏感性分析，以识别问题的结构特征并了解不同参数对最优决策的影响。