Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

Yindong Shen;Leqin Yu;Jingpeng Li
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引用次数: 4

Abstract

Vehicle routing problem with time windows (VRPTW) is a core combinatorial optimization problem in distribution tasks. The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW. Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles, the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce. However, practically, it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles (EVs) in actual operation. Hence, we propose the robust optimization model based on a route-related uncertain set to tackle this problem. Moreover, adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem. The effectiveness of the method is verified by experiments, and the influence of uncertain demand and uncertain parameters on the solution is further explored.
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需求不确定性和权重相关能耗下具有时间窗的鲁棒电动汽车路径问题
带时间窗的车辆路径问题是配送任务中的一个核心组合优化问题。需求不确定性和权重相关能耗下带时间窗的电动汽车路径问题是VRPTW的延伸。尽管已有研究人员研究了基于非线性能耗模型的电动VRPTW或不确定的客户需求对传统汽车的影响,但将不确定需求与电动汽车能耗相结合的研究文献仍然很少。然而,在实际操作中,客户需求的不确定性以及电动汽车与重量相关的能耗往往是不可忽视的。因此,我们提出了基于路径相关不确定集的鲁棒优化模型来解决这一问题。此外,由于该问题的NP-hard性质,开发了自适应大邻域搜索启发式算法来解决该问题。通过实验验证了该方法的有效性,并进一步探讨了不确定需求和不确定参数对解的影响。
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