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A vector evaluated evolutionary algorithm with exploitation reinforcement for the dynamic pollution routing problem

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  • Université de Tunis et Université Polytechnique Hauts-de-France
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In this paper, we investigate the Pollution Routing Problem in dynamic environments (DPRP). It consists in determining the routing plan of a fleet of vehicles supplying a set of customers, while minimizing the traveled distance and CO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CO_2$$\end{document} emissions. The dynamic character of the problem is manifested by the occurrence of new customer demands when the working plan is in progress. Consequently, the planned routes have to be adapted in real time to include the locations of the new customers. In order to efficiently manage the trade-off between the two considered objectives, a new vector evaluated evolutionary algorithm augmented with an exploitation phase and hyper-mutation is proposed. This combination aims to reinforce the refinement of compromised solutions, and to speed up adaptation after the occurrence of a change in the problem inputs. An experimental study is conducted to test the proposed approaches on mono-objective and bi-objective test problems, and against well known approaches from the literature. The obtained results show that our proposal performs well and is highly competitive compared with the competing meta-heuristics.
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Journal of Combinatorial Optimization (2022) 44:1011–1038
https://doi.org/10.1007/s10878-022-00870-1
A vector evaluated evolutionary algorithm
with exploitation reinforcement for the dynamic pollution
routing problem
Nasreddine Ouertani1,2 ·Hajer Ben-Romdhane1·Saoussen Krichen1·
Issam Nouaouri2
Accepted: 24 May 2022 / Published online: 17 June 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
Abstract
In this paper, we investigate the Pollution Routing Problem in dynamic environments
(DPRP). It consists in determining the routing plan of a fleet of vehicles supplying
a set of customers, while minimizing the traveled distance and CO
2emissions. The
dynamic character of the problem is manifested by the occurrence of new customer
demands when the working plan is in progress. Consequently, the planned routes
have to be adapted in real time to include the locations of the new customers. In
order to efficiently manage the trade-off between the two considered objectives, a
new vector evaluated evolutionary algorithm augmented with an exploitation phase
and hyper-mutation is proposed. This combination aims to reinforce the refinement of
compromised solutions, and to speed up adaptation after the occurrence of a change in
the problem inputs. An experimental study is conducted to test the proposed approaches
on mono-objective and bi-objective test problems, and against well known approaches
from the literature. The obtained results show that our proposal performs well and is
highly competitive compared with the competing meta-heuristics.
BNasreddine Ouertani
nasreddine.ouertani@gmail.com
Hajer Ben-Romdhane
hajer.bn.romdhan@gmail.com
Saoussen Krichen
saoussen.krichen@isg.rnu.tn
Issam Nouaouri
issam.nouaouri@univ-artois.fr
1LARODEC Laboratory, Université de Tunis, Institut Supérieur de Gestion de Tunis, Tunis,
Tunisia
2LGI2A Laboratory, Université d’Artois, UR 3926 LGI2A 62400 Béthune, France
123
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