With growing environmental concerns, electric vehicles for logistics have gained significant attention within the computational intelligence community in recent years. This work addresses an emerging and significant extension of the electric vehicle routing problem (EVRP), namely EVRP with time windows, simultaneous pickup-delivery, and partial recharges (EVRP-TW-SPD), which has wide real-world applications. We propose a hybrid memetic algorithm (HMA) for solving EVRP-TW-SPD. HMA incorporates two novel components: a parallel-sequential station insertion procedure for handling partial recharges that can better avoid local optima compared to purely sequential insertion, and a cross-domain neighborhood search that explores solution spaces of both electric and non-electric problem domains simultaneously. These components can also be easily applied to various EVRP variants. To bridge the gap between existing benchmarks and real-world scenarios, we introduce a new, large-scale EVRP-TW-SPD benchmark set derived from real-world applications, containing instances with many more customers and charging stations than existing benchmark instances. Extensive experiments demonstrate the significant performance advantages of HMA over existing algorithms across a wide range of problem instances. Both the benchmark set and HMA will be open-sourced to facilitate further research in this area.

翻译：随着环境问题日益受到关注，电动汽车在物流领域的应用近年来在计算智能学界获得了广泛关注。本研究针对电动汽车路径规划问题（EVRP）的一个重要新兴扩展——具有时间窗、同时取送货与部分充电的电动汽车路径规划问题（EVRP-TW-SPD）展开研究，该问题具有广泛的实际应用价值。我们提出了一种混合模因算法（HMA）用于求解EVRP-TW-SPD。HMA包含两个创新组件：用于处理部分充电的并行-顺序充电站插入策略（相较于纯顺序插入能更好避免局部最优），以及跨领域邻域搜索策略（可同时探索电动与非电动问题领域的解空间）。这些组件也能轻松应用于各类EVRP变体。为弥合现有基准测试集与现实场景的差距，我们基于实际应用构建了全新的大规模EVRP-TW-SPD基准测试集，其包含的实例在客户数量与充电站规模上均远超现有基准。大量实验表明，HMA在各类问题实例上均显著优于现有算法。基准测试集与HMA算法将开源发布，以推动该领域的后续研究。