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

Nasreddine Ouertani (), Hajer Ben-Romdhane (), Saoussen Krichen () and Issam Nouaouri ()
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Nasreddine Ouertani: Université de Tunis, Institut Supérieur de Gestion de Tunis
Hajer Ben-Romdhane: Université de Tunis, Institut Supérieur de Gestion de Tunis
Saoussen Krichen: Université de Tunis, Institut Supérieur de Gestion de Tunis
Issam Nouaouri: Université d’Artois

Journal of Combinatorial Optimization, 2022, vol. 44, issue 2, No 7, 1038 pages

Abstract: 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 $$CO_2$$ C O 2 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.

Keywords: Pollution routing problem; Dynamic optimization problem; Vector evaluated GA; Local search; Exploration–Exploitation trade-off; Multi-objective optimization (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10878-022-00870-1

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