| 随机时变车辆路径问题的多目标鲁棒优化方法 |
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| 引用本文： | 段征宇,雷曾翔,孙硕,杨东援. 随机时变车辆路径问题的多目标鲁棒优化方法[J]. 西南交通大学学报, 2019, 54(3): 565-572. DOI: 10.3969/j.issn.0258-2724.20170617 |
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| 作者姓名： | 段征宇 雷曾翔 孙硕 杨东援 |
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| 作者单位： | 同济大学道路与交通工程教育部重点实验室;上海市城市规划设计研究院 |
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| 基金项目： | 国家自然科学基金项目（71001079） |
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| 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NSGA-II算法小17.49%.
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| 关 键 词： | 车辆路径问题 随机时变路网 鲁棒优化 多目标优化 蚁群算法 |
| 收稿时间： | 2017-08-15 |
Multi-Objective Robust Optimisation Method for Stochastic Time-Dependent Vehicle Routing Problem |
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| DUAN Zhengyu,LEI Zengxiang,SUN Shuo,YANG Dongyuan. Multi-Objective Robust Optimisation Method for Stochastic Time-Dependent Vehicle Routing Problem[J]. Journal of Southwest Jiaotong University, 2019, 54(3): 565-572. DOI: 10.3969/j.issn.0258-2724.20170617 |
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| Authors: | DUAN Zhengyu LEI Zengxiang SUN Shuo YANG Dongyuan |
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| Abstract: | 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. |
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