Skewed General Variable Neighborhood Search to Solve the Multi-compartment Vehicle Routing Problem

  • Amina Arousse Modeling and Optimization for Decisional, Industrial and Logistic Systems Laboratory, Faculty of Economics and Management, University of Sfax, Tunisia http://orcid.org/0009-0005-2092-0626
  • Ahmed Ben Aouicha Modeling and Optimization for Decisional, Industrial and Logistic Systems Laboratory, Faculty of Economics and Management, University of Sfax, Tunisia http://orcid.org/0009-0005-2092-0626
  • Mohamed Cheikh Modeling and Optimization for Decisional, Industrial and Logistic Systems Laboratory, Faculty of Economics and Management, University of Sfax, Tunisia http://orcid.org/0009-0005-2092-0626
  • Bassem Jarboui Modeling and Optimization for Decisional, Industrial and Logistic Systems Laboratory, Universit´e Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, Valenciennes 59313, France http://orcid.org/0009-0005-2092-0626
DOI: https://doi.org/10.2298/YJOR220615041A

Abstract

Skewed General Variable Neighborhood Search (SGVNS) is shown to be a powerful and robust methodology for solving vehicle routing problems. In this paper we suggest new SGVNS for solving the multi-compartment vehicle routing problem (MCVRP). The problem of multi-compartment vehicle routing is of practical importance in the petrol and food delivery and waste collection industries. A comparison between our algorithm and the memetic algorithm and the tabu search is provided. It was clear that the proposed algorithm is capable of solving the available instances. Skewed General Variable Neighborhood Search was used because it makes it easy to explore the space of realizable solutions for MCVRP. As a result, the SGVNS is much faster and more effective. It is able to solve 50 to 484 customers from the literature.

Published
2024-10-02
How to Cite
AROUSSE, Amina et al. Skewed General Variable Neighborhood Search to Solve the Multi-compartment Vehicle Routing Problem. Yugoslav Journal of Operations Research, [S.l.], v. 34, n. 3, p. 423-438, oct. 2024. ISSN 2334-6043. Available at: <https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/1296>. Date accessed: 04 dec. 2024. doi: https://doi.org/10.2298/YJOR220615041A.
Citation Formats
Issue
Vol 34 No 3 (2024)
Section
Special Issue
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