Title: Hybridised ant colony optimisation for the multi-depot multi-compartment capacitated arc routing problem

Authors: Ali Kansou; Bilal Kanso; Adnan Yassine

Addresses: Department of Computer Science, Lebanese University, Beirut, Lebanon ' Department of Computer Science, Lebanese University, Beirut, Lebanon ' Normandie University, 25 Rue Philippe Lebon, Le Havre, France

Abstract: This paper considers the multi-depot multi-compartment capacitated arc routing problem. It consists to find a set of vehicle routes with minimal travelled distance that satisfy the demands of a set of customers for several products. This problem has some important applications such as in the fields of transportation, distribution and logistics since companies are increasingly using multiple depots to store their products and separate compartments which are necessary since each product has its own specific characteristics and cannot be mixed during transportation. In this paper, a new approach based on the ant colony optimisation that is hybridised with a simulated annealing algorithm is developed. Computational experiments are performed on a benchmark of instances taken from the literature, and a set of real-life instances, and on another new set of random large-scale instances. The proposed metaheuristic generates high-quality solutions compared to the existing algorithms and particularly the results on the new instances seem promising, purposeful and powerful.

Keywords: metaheuristic; arc routing problem; multi-depot; multi-compartment; ant colony optimisation; ACO; simulated annealing algorithm.

DOI: 10.1504/IJOR.2023.133741

International Journal of Operational Research, 2023 Vol.48 No.1, pp.18 - 46

Received: 07 May 2020
Accepted: 21 Jan 2021
Published online: 03 Oct 2023 *

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