A hybrid adaptive large neighbourhood search algorithm for the capacitated location routing problem

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dc.contributor.authorŞatir Akpunar Ö.
dc.contributor.authorAkpinar
dc.date.accessioned2024-07-22T08:06:17Z
dc.date.available2024-07-22T08:06:17Z
dc.date.issued2021
dc.description.abstractThis paper proposes a new hybrid metaheuristic algorithm that is composed of the adaptive large neighbourhood search (ALNS) and the variable neighbourhood search (VNS) algorithms to tackle the location routing problem (LRP) with capacity constraints. The rationale of the proposed hybrid metaheuristic algorithm is to enhance the performance of the ALNS algorithm by incorporating the VNS algorithm as an elitist local search. Therefore, the diversification and intensification strategies of the proposed hybrid metaheuristic algorithm are realized via the ALNS and VNS algorithms, respectively. The performance evaluation tests of the proposed hybrid metaheuristic algorithm are performed on the three classical LRP benchmark sets taken from the related literature, and the obtained results are compared against some of the formerly proposed and published methods in terms of solution quality. Computational results indicate that the proposed hybrid metaheuristic algorithm has a satisfactory performance in solving the LRP instances and is a competitive algorithm. © 2020 Elsevier Ltd
dc.identifier.DOI-ID10.1016/j.eswa.2020.114304
dc.identifier.issn09574174
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/13465
dc.language.isoEnglish
dc.publisherElsevier Ltd
dc.subjectRouting algorithms
dc.subjectCapacitated location
dc.subjectCompetitive algorithms
dc.subjectComputational results
dc.subjectDiversification and intensification strategies
dc.subjectHybrid metaheuristic algorithms
dc.subjectLarge neighbourhood searches
dc.subjectLocation routing problem
dc.subjectVariable neighbourhood search
dc.subjectBenchmarking
dc.titleA hybrid adaptive large neighbourhood search algorithm for the capacitated location routing problem
dc.typeArticle

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