Evolutionary-Based Ant System Algorithm to Solve the Dynamic Electric Vehicle Routing Problem

Simon Caillard, Rachida Ben Chabane

2024

Abstract

This article addresses the Dynamic Electric Vehicle Routing Problem with Time Windows (DEVRPTW) using a hybrid approach blending genetic and Ant Colony Optimization (ACO) algorithms. It employs an Ant System algorithm (AS) with an integrated memory system that undergoes mutations for solution diversification. Testing on Schneider instances under static and dynamic conditions, with run time of 10 and 3 minutes respectively, reveals promising results. Compared to static solutions, deviations of 8.55% and 2.38% are observed in vehicle count and total distance. In a dynamic context, the algorithm maintains proximity to static results, with 10.99% and 4.41% deviations in vehicle count and distance. Instances R1 and R2 present challenges, suggesting potential improvements in memory and pheromone transfer during re-optimization.

Paper Citation

in Harvard Style

Caillard S. and Ben Chabane R. (2024). Evolutionary-Based Ant System Algorithm to Solve the Dynamic Electric Vehicle Routing Problem. In Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES; ISBN 978-989-758-681-1, SciTePress, pages 285-293. DOI: 10.5220/0012379200003639

in Bibtex Style

@conference{icores24,
author={Simon Caillard and Rachida Ben Chabane},
title={Evolutionary-Based Ant System Algorithm to Solve the Dynamic Electric Vehicle Routing Problem},
booktitle={Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES},
year={2024},
pages={285-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012379200003639},
isbn={978-989-758-681-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES
TI - Evolutionary-Based Ant System Algorithm to Solve the Dynamic Electric Vehicle Routing Problem
SN - 978-989-758-681-1
AU - Caillard S.
AU - Ben Chabane R.
PY - 2024
SP - 285
EP - 293
DO - 10.5220/0012379200003639
PB - SciTePress