Electric vehicles (EVs) have been highly favoured as a mode of transportation in recent years. EVs offer numerous benefits over traditional fuel-based vehicles, particularly in terms of the environmental impact. Although electric vehicles offer several advantages, there are certain restrictions that limit their usage. One of the significant issues is the uncertainty in their driving range. The driving range of EVs is closely related to their energy consumption, which is highly affected by exogenous and endogenous factors. Since those factors are unpredictable, uncertainty in EVs’ energy consumption should be considered for efficient operation. This paper proposes a two-stage adaptive robust optimization framework for the electric vehicle routing problem. The objective is to minimize the worst-case energy consumption while guaranteeing that services are delivered at the appointed time windows without battery level deficiency. We postulate that EVs can be recharged on route, and the charging amount can be adjusted depending on the circumstances. A column-and-constraint generation based heuristic algorithm, which is coupled with variable neighbourhood search and alternating direction algorithm, is proposed to solve the resulting model. The computational results show the economic efficiency and robustness of the proposed model, and that there is a tradeoff between the total required energy and the risk of failing to satisfy all customers’ demand.

中文翻译：

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能耗不确定下的自适应鲁棒电动汽车路径选择

近年来，电动汽车（EV）作为一种交通方式备受青睐。与传统燃油汽车相比，电动汽车具有许多优势，特别是在环境影响方面。尽管电动汽车具有多种优点，但也存在一些限制其使用的限制。重要的问题之一是其行驶里程的不确定性。电动汽车的续驶里程与其能耗密切相关，受外生和内生因素影响较大。由于这些因素是不可预测的，因此为了高效运行，应考虑电动汽车能耗的不确定性。本文提出了一种针对电动汽车路径问题的两阶段自适应鲁棒优化框架。目标是最大限度地减少最坏情况下的能源消耗，同时保证在指定的时间窗口提供服务而不会出现电池电量不足的情况。我们假设电动汽车可以在途中充电，并且充电量可以根据情况进行调整。提出了一种基于列和约束生成的启发式算法，该算法与可变邻域搜索和交替方向算法相结合来求解结果模型。计算结果显示了所提出模型的经济效率和鲁棒性，并且在总需求能量和无法满足所有客户需求的风险之间存在权衡。