Al-Nassar, Suzan (Leiden University), van Stein, Niki (Leiden University), Fan, Yingjie (Leiden University)

ACO-NSGAII: A Novel Metaheuristics for Bi-Objective Electric Vehicle Routing Problems

Scheduled for presentation during the Poster Session "Vehicle routing" (WeAT15), Wednesday, September 25, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

This study introduces ACO-NSGAII, a hybrid metaheuristic algorithm that integrates ant colony optimization (ACO) and non-dominated sorting genetic algorithm II (NSGA-II), to address the bi-objective Electric Vehicle Routing Problem with Time Windows (EVRPTW). The algorithm aims to minimize both the overall travel distances and the number of vehicles required in last-mile delivery. Our approach starts with ACO to generate an initial solution focusing on distance minimization, followed by NSGA-II to optimize the dual objectives efficiently. Extensive computational experiments demonstrate that ACO-NSGAII significantly outperforms existing methods like Random-NSGAII and NN-NSGAII (an integration of nearest neighbor and NSGA-II), offering promising solutions for sustainable urban logistics. The findings contribute valuable insights into the trade-offs between minimizing distance and vehicle usage. Moreover, our research extensively studies the effectiveness of ACO-NSGAII for complex routing problems and highlights the impact of search budget allocation on the convergence of ACO-NSGAII.