Automated Design of Routing Policies for the Dynamic Electric Vehicle Routing Problem with Genetic Programming

Marko Durasevic, Francisco Javier Gil Gala

2024

Abstract

The dynamic electric vehicle routing problem (EVRP) with time windows (DEVRPTW) is an important combinatorial optimisation problem gaining on importance in today’s world due to environmental concern and the requirement of dealing with dynamic and uncertain environments. This represents a problem when solving such problems, as standard improvement based heuristics cannot be used to solve them, since not all information about the problem is known beforehand. This provides a motivation for applying improvement based heuristics, most notably routing policies (RPs), which determine only the next decision that needs to be performed an execute it. Since these RPs do not construct the schedule in advance, they can easily react to any changes in the problem. However, RPs are difficult to design, which motivated the use of genetic programming (GP) in automatically designing such heuristics for various problems. Unfortunately, in the context of EVRP only static problems were considered. This study investigates the application of GP to automatically design new RPs for DEVRPTW under different levels of dynamism. The results demonstrate that GP performs well for certain levels of dynamism, although as it increases it is more difficult to perform good decisions.

Paper Citation

in Harvard Style

Durasevic M. and Gil Gala F. (2024). Automated Design of Routing Policies for the Dynamic Electric Vehicle Routing Problem with Genetic Programming. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA; ISBN 978-989-758-721-4, SciTePress, pages 346-353. DOI: 10.5220/0013058900003837

in Bibtex Style

@conference{ecta24,
author={Marko Durasevic and Francisco Javier Gil Gala},
title={Automated Design of Routing Policies for the Dynamic Electric Vehicle Routing Problem with Genetic Programming},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA},
year={2024},
pages={346-353},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013058900003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - Automated Design of Routing Policies for the Dynamic Electric Vehicle Routing Problem with Genetic Programming
SN - 978-989-758-721-4
AU - Durasevic M.
AU - Gil Gala F.
PY - 2024
SP - 346
EP - 353
DO - 10.5220/0013058900003837
PB - SciTePress