Abstract
The vehicle routing problem is one of the most studied combinatorial optimization problems in operations research. The problem deals with a homogenous fleet of capacitated vehicles that operates from a central depot serving a set of customers with known demands. The objective of the problem is to design a set of routes serving customers with minimum cost. The vehicle routing problem is classified as NP-hard problem. Exact and approximate algorithms have been developed in the literature to solve the Capacitated Vehicle Routing Problem (CVRP). However, exact methods can only solve relatively small-size problems while approximate algorithms have been able to reach near-optimum solutions of large problems. The purpose of this paper is to develop a new hybrid search algorithm that combines the evolutionary genetic search with a new local search heuristic to solve the CVRP. The proposed heuristic calculates a resultant objective function based on both the distance travelled and the demand associated with the given customer. A new set of genetic operators suited for the problem was employed. Several computational experiments were conducted. The algorithm was validated and was capable of converging to the optimum solution of the tested benchmark instance.
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Elgharably, N., Nassef, A., Easa, S., El Damatty, A. (2022). New Hybrid Search Algorithm for the Capacitated Vehicle Routing Problem. In: Walbridge, S., et al. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 . CSCE 2021. Lecture Notes in Civil Engineering, vol 250. Springer, Singapore. https://doi.org/10.1007/978-981-19-1065-4_43
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DOI: https://doi.org/10.1007/978-981-19-1065-4_43
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