Variable Neighborhood Search for the Electric Vehicle Routing Problem

2025-11-11

AI Summary

This paper addresses the Capacitated Green Vehicle Routing Problem (CGVRP)—a benchmark problem from the CEC-12 competition—characterized by electric vehicle energy constraints and fixed charging station infrastructure. We propose a tailored Variable Neighborhood Search (VNS) algorithm incorporating dedicated neighborhood structures, a dynamic neighborhood-switching mechanism, and multi-strategy local search to jointly optimize battery consumption, charging decisions, and route configuration. Evaluated on the complete CEC-12 benchmark suite, our algorithm attains all known optimal solutions, outperforming subsequent state-of-the-art heuristic and metaheuristic approaches. The primary contributions are: (i) the first efficient, problem-specific VNS framework for CGVRP; (ii) empirical validation of its superiority in handling coupled energy-aware routing and charging decisions; and (iii) a scalable, generalizable solution paradigm for green logistics scheduling under realistic operational constraints.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Extends classical Vehicle Routing Problem for electric vehicles
• Addresses challenging constraints in electric vehicle logistics
• Solves Capacitated Green Vehicle Routing Problem variant effectively

Innovation

• Uses Variable Neighborhood Search metaheuristic
• Solves Capacitated Green Vehicle Routing Problem
• Outperforms recent algorithms on competition dataset