三层次遗传算法优化电动车路径规划，兼顾电量与充电站布局。
Trilevel Memetic Algorithm for the Electric Vehicle Routing Problem
- 分三层优化客户顺序、路线分配和充电站插入
- 在小规模实例上达到现有最优解水平
- 适合需要低碳配送的物流系统设计
电动车路径问题（EVRP）在经典车辆路径问题基础上引入电池容量限制和充电站约束，带来显著优化挑战。本文提出一种三层次膜算法（TMA），通过分层优化客户序列、路线分配与充电站插入，结合遗传算法与动态规划，实现高效高质量求解。在WCCI2020基准测试集上的实验表明，该方法在小规模实例中达到最优解水平，性能具有竞争力。尽管计算开销限制其扩展性，但展现了在可持续物流规划中的应用潜力。
原文摘要 · Abstract (English)
The Electric Vehicle Routing Problem (EVRP) extends the capacitated vehicle routing problem by incorporating battery constraints and charging stations, posing significant optimization challenges. This paper introduces a Trilevel Memetic Algorithm (TMA) that hierarchically optimizes customer sequences, route assignments, and charging station insertions. The method combines genetic algorithms with dynamic programming, ensuring efficient and high-quality solutions. Benchmark tests on WCCI2020 instances show competitive performance, matching best-known results for small-scale cases. While computational demands limit scalability, TMA demonstrates strong potential for sustainable logistics planning.
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