Solving capacitated location routing problem by variable neighborhood descent and GA–Artificial neural network hybrid method

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dc.contributor.authorPekel, Engin
dc.contributor.authorSoner Kara, Selin
dc.date.accessioned2019-05-13T08:57:15Z
dc.date.available2019-05-13T08:57:15Z
dc.date.issued2018
dc.departmentHitit Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThis paper aims to find the optimal depot locations and vehicle routings for spare parts of an automotive company considering future demands. The capacitated location-routing problem (CLRP), which has been practiced by various methods, is performed to find the optimal depot locations and routings by additionally using the artificial neural network (ANN). A novel multi-stage approach, which is performed to lower transportation cost, is carried out in CLRP. Initially, important factors for customer demand are tested with an univariate analysis and used as inputs in the prediction step. Then, genetic algorithm (GA) and ANN are hybridized and applied to provide future demands. The location of depots and the routings of the vehicles are determined by using the variable neighborhood descent (VND) algorithm. Five neighborhood structures, which are either routing or location type, are implemented in both shaking and local search steps. GA-ANN and VND are applied in the related steps successfully. Thanks to the performed VND algorithm, the company lowers its transportation cost by 2.35% for the current year, and has the opportunity to determine optimal depot locations and vehicle routings by evaluating the best and the worst cases of demand quantity for ten years ahead. © 2018, Faculty of Transport and Traffic Engineering. All rights reserved.
dc.identifier.citationPekel, E., Soner Kara, S. (2018). Solving capacitated location routing problem by variable neighborhood descent and GA–Artificial neural network hybrid method. Promet – Traffic & Transportation, 30(5), 563-578.
dc.identifier.doi10.7307/ptt.v30i5.2640
dc.identifier.endpage578en_US
dc.identifier.issn0353-5320
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage563en_US
dc.identifier.urihttps://doi.org/10.7307/ptt.v30i5.2640
dc.identifier.urihttps://hdl.handle.net/11491/880
dc.identifier.volume30en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherFaculty of Transport and Traffic Engineering
dc.relation.ispartofPromet – Traffic & Transportation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial Neural Networken_US
dc.subjectCapacitated Location-Routing Problemen_US
dc.subjectGenetic Algorithmen_US
dc.subjectHeuristicsen_US
dc.subjectK-Nearest Neighborhooden_US
dc.subjectVariable Neighborhood Descenten_US
dc.titleSolving capacitated location routing problem by variable neighborhood descent and GA–Artificial neural network hybrid method
dc.typeArticle

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