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Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

dc.contributor.authorCano J.A
dc.contributor.authorCortés P
dc.contributor.authorCampo E.A
dc.contributor.authorCorrea-Espinal A.A.
dc.date.accessioned2022-09-14T14:34:07Z
dc.date.available2022-09-14T14:34:07Z
dc.date.created2021
dc.identifier.issn20888708
dc.identifier.urihttp://hdl.handle.net/11407/7572
dc.descriptionThis paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times. © 2021 Institute of Advanced Engineering and Science. All rights reserved.eng
dc.language.isoeng
dc.publisherInstitute of Advanced Engineering and Science
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85101187748&doi=10.11591%2fijece.v11i3.pp2516-2524&partnerID=40&md5=30471010225fabb03c725d449fb77fe9
dc.sourceInternational Journal of Electrical and Computer Engineering
dc.titleSolving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programAdministración de Empresas
dc.type.spaArtículo
dc.identifier.doi10.11591/ijece.v11i3.pp2516-2524
dc.subject.keywordGrouped genetic algorithmseng
dc.subject.keywordHeterogeneous load capacityeng
dc.subject.keywordMultiple pickerseng
dc.subject.keywordOrder batchingeng
dc.subject.keywordSequencingeng
dc.relation.citationvolume11
dc.relation.citationissue3
dc.relation.citationstartpage2516
dc.relation.citationendpage2524
dc.publisher.facultyFacultad de Ciencias Económicas y Administrativas
dc.affiliationCano, J.A., Faculty of Economics and Administrative Sciences, Universidad de Medellín, Carrera 87 # 30-65, Medellin, Colombia
dc.affiliationCortés, P., Escuela Técnica Superior de Ingeniería, Seville University, Spain
dc.affiliationCampo, E.A., ESACS-Escuela Superior en Administración de Cadena de Suministro, Colombia
dc.affiliationCorrea-Espinal, A.A., Departamento de Ingeniería de la Organización, Universidad Nacional de Colombia, Colombia
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dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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