用变邻域搜索优化电动车配送路线，竞赛夺冠且超越后续算法。
Variable Neighborhood Search for the Electric Vehicle Routing Problem
- 采用变邻域搜索框架，系统探索不同邻域结构提升解质量。
- 在完整竞赛数据集上取得最优结果，显著优于其他方法。
- 适合物流调度与绿色运输研究者参考，尤其关注电动车路径优化。
电动车辆路径问题（EVRP）是经典车辆路径问题（VRP）的扩展，以反映物流中电动及混合动力车辆日益增长的应用。由于文献中考虑的约束各不相同，跨不同问题变体比较方法仍具挑战性。本文聚焦于2020年IEEE计算智能世界大会举办的CEC-12竞赛中的极简型EVRP——容量型绿色车辆路径问题（CGVRP）。该论文提出竞赛获胜方法，基于变邻域搜索（VNS）元启发式算法，在完整竞赛数据集上取得最佳性能，并超越其后发表的更先进算法。
原文摘要 · Abstract (English)
The Electric Vehicle Routing Problem (EVRP) extends the classical Vehicle Routing Problem (VRP) to reflect the growing use of electric and hybrid vehicles in logistics. Due to the variety of constraints considered in the literature, comparing approaches across different problem variants remains challenging. A minimalistic variant of the EVRP, known as the Capacitated Green Vehicle Routing Problem (CGVRP), was the focus of the CEC-12 competition held during the 2020 IEEE World Congress on Computational Intelligence. This paper presents the competition-winning approach, based on the Variable Neighborhood Search (VNS) metaheuristic. The method achieves the best results on the full competition dataset and also outperforms a more recent algorithm published afterward.
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