Trilevel Memetic Algorithm for the Electric Vehicle Routing Problem
This work addresses optimization challenges in sustainable logistics planning for electric vehicle routing, but it is incremental as it builds on existing methods with limited scalability.
The paper tackled the Electric Vehicle Routing Problem (EVRP) by introducing a Trilevel Memetic Algorithm (TMA) that hierarchically optimizes customer sequences, route assignments, and charging station insertions, achieving competitive performance by matching best-known results for small-scale cases in benchmark tests on WCCI2020 instances.
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.