Variable Neighborhood Search for the Electric Vehicle Routing Problem

TL;DR

This paper presents a Variable Neighborhood Search metaheuristic that effectively solves the Capacitated Green Vehicle Routing Problem, a minimalistic EVRP variant, outperforming existing algorithms in a major competition dataset.

Contribution

It introduces a novel VNS-based approach for EVRP, demonstrating superior performance on benchmark datasets and advancing solution methods for electric vehicle routing.

Findings

Achieved best results on the CEC-12 EVRP dataset

Outperformed recent algorithms in the same problem class

Validated effectiveness of VNS in electric vehicle routing

Abstract

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.