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在现实中存在一类温度敏感型产品,其市场需求往往与销售季内的平均温度相关。在针对温度敏感型产品的定价与订货联合决策中,温度的不确定性与零售商的损失规避行为是不可忽视的重要因素,如何构建考虑零售商损失规避的温度敏感型产品定价与订货联合决策模型是需要关注的问题。依据该类产品对温度的敏感类型,本文主要关注高温适用型和低温适用型两类产品。考虑温度变化对两类温度敏感型产品市场需求的影响,给出了两类温度敏感型产品的需求函数;在此基础上,考虑零售商的损失规避行为对零售商效用的影响,构建了以零售商期望效用最大为目标的决策模型;进一步地,依据期望效用最大化理论,求解模型并确定了零售商的最优价格和最优订货量;通过数值实验,分别针对高温适用型和低温适用型两类产品,分析了不同温度敏感系数下销售季内平均温度和损失规避系数对零售商最优决策结果的影响。分析结果表明,销售季内平均温度和零售商的损失规避程度均在不同程度上影响其最优决策结果;相对于不考虑产品温度敏感性的温度敏感型产品零售商的最优决策结果,考虑温度敏感性的该类产品零售商的最优决策结果会更加保守;分析结果还表明,考虑损失规避行为的温度敏感型产品零售商的订货量往往会高于损失中性的该类产品零售商的订货量。
In reality there is a class of temperature-sensitive products whose market demand is often related to the average temperature in the sales season. In the joint pricing and ordering decision for temperature-sensitive products, the temperature uncertainty and the retailer’s loss aversion behavior are important factors that can not be ignored. How to build a joint decision-making of temperature-sensitive product pricing and ordering considering the retailer’s loss aversion The model is a matter of concern. According to the temperature sensitive type of this type of product, this article mainly focuses on high temperature applicable type and low temperature applicable type of two types of products. Considering the effect of temperature changes on the market demand of two types of temperature-sensitive products, the demand functions of two types of temperature-sensitive products are given; on this basis, considering the effect of retailer’s loss aversion behavior on retailers’ utility, a The retailer expects a utility-maximizing decision model; further, based on the expected utility maximization theory, solves the model and determines the retailer’s optimal price and optimal order quantity; through numerical experiments, it is applicable to high-temperature applicable and low-temperature applications, respectively. The two types of products analyze the effect of different temperature sensitivity coefficients on the average decision-making results of the retailers’ average temperature and the loss-avoidance coefficient during the sales season. The analysis results show that the average temperature in the sales season and the retailer’s loss aversion degree influence the optimal decision-making results in different degrees; compared with the optimal decision-making results of the temperature-sensitive product retailers that do not consider the product temperature sensitivity, The optimal decision-making results for temperature-sensitive retailers of this type of product will be more conservative; the analysis also shows that retailers of temperature-sensitive products that consider loss aversion tend to be more likely to place orders than retailers who lose their neutrality. The amount of order.