随机扰动项呈离散分布的库存路径和定价问题的模型与算法

针对产品需求价格函数随机扰动项呈离散分布的库存路径和定价问题(Inventory Routing and Pricing Problem, IRPP)，利用随机扰动项的离散分布率和零售商库存服务水平要求，以供货商期望收益最大化为目标，构建IRPP优化模型，将禁忌搜索算法嵌入改进的粒子群算法中求解模型。3组不同规模的算例分析结果显示，在同样的零售商库存服务水平要求下，考虑需求价格函数随机扰动项与不考虑相比，供货商期望收益分别提高了3.1%、3.5%和3.8%。研究表明，产品需求价格函数随机扰动越剧烈，供货商的期望收益就会愈少，客户的产品价格弹性越高，供货商的期望收益就会愈大。研究结论可为供货商IRPP决策提供参考。

A Model and Algorithm for Inventory Routing and Pricing Problem with Discretely Distributed Random Disturbance Items

This paper studied the inventory routing and pricing problem (IRPP) with discretely distributed random disturbance items in the demand price function. Based on the discrete distribution rate of the random disturbance item and requirements on retailers' inventory service level, an IRPP optimization model was established to maximize the expected revenues of the supplier. The tabu search algorithm was embedded into the improved particle swarm algorithm to solve the model. Three numerical examples with different sizes show that the supplier's expected revenue can be increased by 3.1%, 3.5%, and 3.8% respectively when considering the random disturbance item, under the same retailors' inventory service level. The research indicates that the more severe the random disturbance of the product demand price function, the less the supplier's expected return, and the higher the price elasticity of the customer's product, the greater the expected return of the supplier. This finding can provide a useful reference for the supplier's IRPP decision-making.