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MULTI-OBJECTIVE GROUPING GENETIC ALGORITHM FOR THE JOINT ORDER BATCHING, BATCH ASSIGNMENT, AND SEQUENCING PROBLEM

dc.contributor.authorCano J.A
dc.contributor.authorCortés P
dc.contributor.authorCampo E.A
dc.contributor.authorCorrea-Espinal A.A.
dc.date.accessioned2022-09-14T14:33:55Z
dc.date.available2022-09-14T14:33:55Z
dc.date.created2021
dc.identifier.issn17509653
dc.identifier.urihttp://hdl.handle.net/11407/7519
dc.descriptionThis article solves the order batching, batch assignment, and sequencing problem (JOBASP) given multiple objectives and heterogeneous picking vehicles in multi-parallel-aisle warehouse systems. A multi-objective grouping genetic algorithm (GGA) is developed to minimize total travel time and total tardiness by implementing an encoding scheme where a gene represents orders grouped in a batch and the assignment of the batch to a picking vehicle. Computer simulations show that the proposed algorithm performs 25.4% better than a first come, first served (FCFS) rule–based heuristic and 10.2% better than an earliest due date (EDD) rule–based heuristic. The proposed GGA provides significant savings of up to 46.8% and 28.4% on travel time and tardiness, respectively, for these benchmark heuristics. Therefore, this article introduces a GGA to solve the JOBASP with a reasonable computing time, making this approach interesting for warehouse operators using heterogeneous picking vehicles and addressing multiple objectives. © 2021 International Society of Management Science and Engineering Management.eng
dc.language.isoeng
dc.publisherTaylor and Francis Ltd.
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118711477&doi=10.1080%2f17509653.2021.1991852&partnerID=40&md5=99460c6c6d9fc9623edf1f082208077c
dc.sourceInternational Journal of Management Science and Engineering Management
dc.titleMULTI-OBJECTIVE GROUPING GENETIC ALGORITHM FOR THE JOINT ORDER BATCHING, BATCH ASSIGNMENT, AND SEQUENCING PROBLEM
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programAdministración de Empresas
dc.type.spaArtículo
dc.identifier.doi10.1080/17509653.2021.1991852
dc.subject.keywordBatch assignmenteng
dc.subject.keywordGrouping genetic algorithmseng
dc.subject.keywordJoint order pickingeng
dc.subject.keywordMulti-objective problemeng
dc.subject.keywordOrder batchingeng
dc.subject.keywordOrder pickingeng
dc.subject.keywordSequencingeng
dc.publisher.facultyFacultad de Ciencias Económicas y Administrativas
dc.affiliationCano, J.A., Faculty of Economic and Administrative Sciences, Universidad de Medellín, Medellín, Colombia
dc.affiliationCortés, P., Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, Spain
dc.affiliationCampo, E.A., Politécnico Colombiano Jaime Isaza Cadavid, Medellín, Colombia
dc.affiliationCorrea-Espinal, A.A., Facultad de Minas, Universidad Nacional de Colombia, Medellín, 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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