针对物流配送中的不确定性因素,构建车辆路径问题的鲁棒性度量与优化方法,目的是降低不确定性因素对物流配送系统的影响.首先,提出车辆路径问题的鲁棒性度量指标,利用算例对各指标的效果进行分析,选择适用于度量车辆路径方案鲁棒性的指标.在此基础上,设计物流配送车辆路径规划的两阶段优化算法.算法的第一阶段不考虑车辆路径的鲁棒性,以总配送成本最小为目标函数优化配送方案;算法的第二阶段以鲁棒性度量指标最大为目标函数,以第一阶段获得的总成本与车辆数为约束条件,优化鲁棒调度方案.文章为车辆路径问题的鲁棒性度量提供了一种有效方法,同时为如何平衡供应链中的物流配送环节的服务作业成本与调度方案鲁棒性提供了思路.

To tackle the uncertainties that happen in logistics distribution, methods of robustness measures and robust optimization for vehicle routing problem (VRP) are studied. The objective is to decrease the impact of uncertainties on distribution system. Firstly, the robustness measures for VRP are proposed, and each measure is assessed through computational experiments, thus the most suitable measure for schedule robustness is selected. Furthermore, a two-stage optimization algorithm for VRP is designed. The first stage is to minimize the transportation cost without considering the schedule robustness, and the total transportation cost and vehicles obtained are taken as threshold values for the next stage. The second stage is to maximize the robustness for vehicle routing schedule while keeping the total transportation cost at a level which is equal to or smaller than the threshold value obtained by the first stage, and the impact of vehicle quantity on schedule robustness is analyzed. It provides robustness measures for VRP and method to solve the trade-off between distribution cost and robustness of vehicle scheduling plan.