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dc.contributor.authorSyah, Rahmadspa
dc.contributor.authorElveny, Marischaspa
dc.contributor.authorSoerjati, Ennispa
dc.contributor.authorGrimaldo Guerrero, John Williamspa
dc.contributor.authorRead Jowad, Rawyaspa
dc.contributor.authorSuksatan, Wanichspa
dc.contributor.authorAravindhan, Surendarspa
dc.contributor.authorYuryevna Voronkova, Olgaspa
dc.contributor.authorMavaluru, Dineshspa
dc.date.accessioned2022-08-04T14:25:14Z
dc.date.available2022-08-04T14:25:14Z
dc.date.issued2022
dc.identifier.citationSyah,R.,Elveny,M.,Soerjati,E.,Guerrero,J.,Jowad,R.,Suksatan,W.,Aravindhan,S.,Voronkova,O. & Mavaluru,D.(2022).Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach. Foundations of Computing and Decision Sciences,47(2) 177-192. https://doi.org/10.2478/fcds-2022-0010spa
dc.identifier.issn0867-6356spa
dc.identifier.urihttps://hdl.handle.net/11323/9430spa
dc.description.abstractUsing hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.eng
dc.format.extent16 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherWalter de Gruyter GmbHspa
dc.rights© 2022 Rahmad Syah et al., published by Sciendo This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.spa
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.titleOptimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approacheng
dc.typeArtículo de revistaspa
dc.identifier.urlhttps://doi.org/10.2478/fcds-2022-0010spa
dc.source.urlhttps://sciendo.com/es/article/10.2478/fcds-2022-0010spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doi10.2478/fcds-2022-0010spa
dc.identifier.eissn2300-3405spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.publisher.placeGermanyspa
dc.relation.ispartofjournalFoundations of Computing and Decision Sciencesspa
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dc.subject.proposalHubeng
dc.subject.proposalReinforced epsilon constraint methodeng
dc.subject.proposalMultilevel serviceseng
dc.subject.proposalQueue theoryeng
dc.subject.proposalMulti-objective optimizationeng
dc.subject.proposalLocation-assignmenteng
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dc.relation.citationendpage192spa
dc.relation.citationstartpage177spa
dc.relation.citationissue2spa
dc.relation.citationvolume47spa
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© 2022 Rahmad Syah et al., published by Sciendo This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Excepto si se señala otra cosa, la licencia del ítem se describe como © 2022 Rahmad Syah et al., published by Sciendo This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.