Trilevel Memetic Algorithm for the Electric Vehicle Routing Problem
Ivan Milinovi\'c, Leon Stjepan Uroi\'c, Marko {\DJ}urasevi\'c
TL;DR
This paper presents a hierarchical memetic algorithm for the electric vehicle routing problem, effectively optimizing routes and charging station placements to improve sustainable logistics despite scalability challenges.
Contribution
Introduces a novel trilevel memetic algorithm combining genetic algorithms and dynamic programming for EV routing optimization.
Findings
Matches best-known results on benchmark instances
Demonstrates strong potential for sustainable logistics planning
Shows computational limitations in scalability
Abstract
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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Taxonomy
TopicsVehicle Routing Optimization Methods · Optimization and Packing Problems · Metaheuristic Optimization Algorithms Research