2023

Conférence

This paper presents an improved genetic algorithm for addressing the multi-objective vehicle routing problem with environmental considerations, published at KES 2023.

In recent years, the negative impacts of neglecting the environment, particularly global warming caused by greenhouse gases, have gained attention. Many countries and organizations are taking steps to reduce their greenhouse gas emissions and promote sustainable practices. In this paper, we aim to address the gap in the classical Vehicle Routing Problem (VRP) by taking into consideration the environmental effects of vehicles. To find a balance between cost-efficiency and environmental impact, we propose a Hybrid Genetic Algorithm (HGA) to address the Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) and a heterogeneous fleet, taking into account new orders that arrive dynamically during the routing process. This approach takes into consideration the environmental effects of the solutions by optimizing the number and type/size of vehicles used to fulfill both static and dynamic orders. The goal is to provide a solution that is both cost-effective and environmentally friendly, addressing the issue of over-exploitation of energy and atmospheric pollution that threaten our ecological environment. Computational results prove that the hybridization of a genetic algorithm with a greedy algorithm can find high-quality solutions in a reasonable run time.

@article{labidi2023improved,
  title={An improved genetic algorithm for solving the multi-objective vehicle routing problem with environmental considerations},
  author={Labidi, Hamida and Azzouna, Nadia Ben and Hassine, Khaled and Gouider, Mohamed Salah},
  journal={Procedia Computer Science},
  volume={225},
  pages={3866--3875},
  year={2023},
  publisher={Elsevier}
}