随机旅行时间的外卖O2O配送车辆路径问题

Vehicle Routing Problem with O2O Takeout Delivery Based on Stochastic Travel Times

外卖O2O(Online to Offline)是一种典型的移动互联网商业模式。入驻外卖O2O平台的餐饮企业为增强顾客的配送满意度,需要对其配送服务进行规划设计。文章研究外卖O2O平台上饮食类供应商外卖配送中的车辆路径问题(VRP),通过对外卖配送特点的深入分析,采用模拟方法实现了随机旅行时间分布的准确刻画,以最大化顾客满意度为目标,综合考虑配送过程中的约束要求,建立了随机旅行时间的带顾客需求时间窗的VRP问题的数学模型。基于上海市徐汇区某入驻外卖O2O企业配送服务的算例,利用遗传算法完成求解。结果显示本文算法可以有效计算出响应顾客需求的最优车辆路径,分析了顾客完全满意度区间大小、顾客满意度敏感性以及配送车辆数量等因素对配送方案总体满意度水平的影响,提出了提高外卖O2O配送满意度的建议。并针对外卖O2O商户自负配送模式进行了研究,可为外卖O2O平台上饮食类供应商改善配送和提升顾客满意度提供决策支持。

Takeout O2O （Online to Offline） is a typical mobile internet era business model. To promote customer satisfaction degree, takeout suppliers on the O2O platform need to implement proper service delivery planning. In this paper, we focus on the vehicle routing problem （VRP） during the take-out delivery service. We use the simulation method to model the distribution of stochastic travel time, and build mathematical model with considering all the constraints, to maximize customer satisfaction. In this paper, we take a takeout restaurant in Xuhui District in Shanghai which is on a O2O platform as the example, and use genetic algorithm to solve the VRP. The results show that the algorithm fits well. We test factors to assess the influence to the general customer satisfaction. This paper is based on a mainstream pattern of O2O takeout delivery, the self-organizing delivery. The conclusion can provide referential decision support to takeout suppliers and help them promoting customer satisfaction degree.