模糊需求下时间依赖型车辆路径优化

Optimization for time dependent vehicle routing problem with fuzzy demand

中文关键词  车辆路径问题  模糊需求  时间依赖  自适应大规模邻域搜索算法

英文关键词  vehicle routing problem  fuzzy demand  time dependent  adaptive large neighborhood search algorithm

中文摘要

      针对客户需求模糊且有时间窗约束的时间依赖型车辆路径问题(TDVRP), 基于先预优化后重调度的思想 构建模型. 在预优化阶段, 依据可信性理论构建模糊机会约束优化模型处理客户点模糊需求; 针对不同时间段道路 的交通情况, 采用Ichoua速度时间依赖函数表征车辆的行驶速度, 并设计自适应大规模邻域搜索算法(ALNS)对其求 解. 在重调度阶段, 应用随机模拟算法模拟客户点的真实需求, 采用点重调度策略对预优化方案进行调整. 通过改 进的Solomon算例实验验证模型和算法的有效性. 研究成果可丰富TDVRP问题的相关研究, 为现实配送方案的优化 决策提供理论依据.

英文摘要

      The time dependent vehicle routing problem (TDVRP) with fuzzy demand and time window is solved according to the idea of pre-optimization and re-dispatch in this paper. In the pre-optimization stage, the fuzzy chance constrained optimization model was constructed on the basis of fuzzy credibility theory, the model insures that fuzzy demand of customers can participate in optimization. Besides, Ichoua speed time-dependent function was used to represent the driving speed of vehicles in different traffic conditions of roads and time periods in the problem formulation, an adaptive large neighborhood search algorithm (ALNS) is designed to obtain the pre-optimization scheme. In the re-dispatch stage, stochastic simulation algorithm was used to simulate the real demand of customer and re-dispatch strategy was adopted to adjust the pre-optimization scheme. The effectiveness of the model and algorithm is verified by an improved Solomon example. The research results can enrich the related research of TDVRP problem and provide theoretical basis for the optimization decision of realistic distribution scheme.