摘要
车辆路径问题(vehicle routing problem,VRP)是物流配送的核心问题之一,为了提高物流配送的时效性,在传统VRP模型的基础上,同时考虑了路网交通状态的时变性和随机性,基于最小最大准则,提出了一种带硬时间窗的随机时变车辆路径问题(stochastic time-dependent vehicle routing problem,STDVRP)的多目标鲁棒优化模型.设计了一种非支配排序蚁群算法(non-dominated sorting ant colony optimisation,NSACO),求解STDVRP多目标优化模型;通过测试算例,对比分析了NSACO算法与改进型非支配排序遗传算法(non-dominated sorting genetic algorithm II,NSGA-II).研究结果表明:对于车辆数最小的Pareto边界解,NSACO算法的平均车辆数比NSGA-II算法小3.33%;对于最坏行程时间最小的Pareto边界解,NSACO算法的平均最坏行程时间比NSGAII算法小17.49%.
The vehicle routing problem(VRP) is a core issue of distribution logistics. In order to improve the timeliness of deliveries, a multi-objective robust optimisation model based on the minimax criterion was proposed for the stochastic time-dependent vehicle routing problem(STDVRP) with hard time windows,considering both the stochastic and time-varying nature of link travel times. A non-dominated sorting ant colony optimisation(NSACO) algorithm was designed to solve this multi-objective optimisation model for the STDVRP.The NSACO algorithm was compared with the non-dominated sorting genetic algorithm II(NSGA-II) through computational instances. The results show that for the Pareto boundary of the minimised number of vehicles,the average number of vehicles for NSACO is 3.33% less than that of NSGA-II,and for the Pareto boundary of the minimised worst travel time,the average worst travel time for NSACO is 17.49% less than that of NSGA-II.
引文
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