Combining hybrid genetic search with ruin-and-recreate for solving the capacitated vehicle routing problem

Martin Simensen (), Geir Hasle () and Magnus Stålhane ()
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Martin Simensen: The Norwegian University of Science and Technology
Geir Hasle: SINTEF Digital
Magnus Stålhane: The Norwegian University of Science and Technology

Journal of Heuristics, 2022, vol. 28, issue 5, No 3, 653-697

Abstract: Abstract The Capacitated Vehicle Routing Problem (CVRP) has been subject to intense research efforts for more than sixty years. Yet, significant algorithmic improvements are still being made. The most competitive heuristic solution algorithms of today utilize, and often combine, strategies and elements from evolutionary algorithms, local search, and ruin-and-recreate based large neighborhood search. In this paper we propose a new hybrid metaheuristic for the CVRP, where the education phase of the hybrid genetic search (HGS) algorithm proposed by (Vidal Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood 2020) is extended by applying large neighborhood search (LNS). By performing a series of computational experiments, we attempt to answer the following research questions: 1) Is it possible to gain performance by adding LNS as a component in the education phase of HGS? 2) How does the addition of LNS change the relative importance of the local search neighborhoods of HGS? 3) What is the effect of devoting computational efforts to the creation of an elite solution in the initial population of HGS? Through a set of computational experiments we answer these research questions, while at the same time obtaining a good configuration of global parameter settings for the proposed heuristic. Testing the heuristic on benchmark instances from the literature with limited computing time, it outperforms existing algorithms, both in terms of the final gap and the primal integral.

Keywords: Vehicle Routing Problems; Hybrid Metaheuristics; Evolutionary Algorithms; Local Search; Large Neighborhood Search (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10732-022-09500-9

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