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Optimization under uncertainty of the pharmaceutical supply chain in hospitals

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dc.creatorFranco Franco, Carlos Alberto
dc.creatorAlfonso-Lizarazo, Edgarspa
dc.date.accessioned2020-05-26T00:05:13Z
dc.date.available2020-05-26T00:05:13Z
dc.date.created2020spa
dc.description.abstractIn this paper, a simulation-optimization approach based on the stochastic counterpart or sample path method is used for optimizing tactical and operative decisions in the pharmaceutical supply chain. This approach focuses on the pharmacy-hospital echelon, and it takes into account random elements related to demand, costs and the lead times of medicines. Based on this approach, two mixed integer programming (MIP) models are formulated, these models correspond to the stochastic counterpart approximating problems. The first model considers expiration dates, the service level required, perishability, aged-based inventory levels and emergency purchases; the optimal policy support decisions related to the replenishment, supplier selection and the inventory management of medicines. The results of this model have been evaluated over real data and simulated scenarios. The findings show that the optimal policy can reduce the current hospital supply and managing costs in medicine planning by 16% considering 22 types of medicines. The second model is a bi-objective optimization model solved with the epsilon-constraint method. This model determines the maximum acceptable expiration date, thereby minimizing the total amount of expired medicines. © 2019 Elsevier Ltdeng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1016/j.compchemeng.2019.106689
dc.identifier.issn981354
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/23768
dc.language.isoengspa
dc.publisherElsevier Ltdspa
dc.relation.citationTitleComputers and Chemical Engineering
dc.relation.citationVolumeVol. 135
dc.relation.ispartofComputers and Chemical Engineering, ISSN:981354, Vol.135,(2020)spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081236702&doi=10.1016%2fj.compchemeng.2019.106689&partnerID=40&md5=432be97781b244eb0f460bd7bab68c61spa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordHospitalsspa
dc.subject.keywordInventory controlspa
dc.subject.keywordMathematical programmingspa
dc.subject.keywordMedicinespa
dc.subject.keywordStochastic modelsspa
dc.subject.keywordStochastic systemsspa
dc.subject.keywordSupply chainsspa
dc.subject.keywordBi-objective optimizationspa
dc.subject.keywordEpsilon-constraint methodspa
dc.subject.keywordMixed integer programming modelspa
dc.subject.keywordOptimization under uncertaintyspa
dc.subject.keywordPharmaceutical supply chainsspa
dc.subject.keywordSample pathspa
dc.subject.keywordSimulation optimizationspa
dc.subject.keywordStochastic counterpartspa
dc.subject.keywordInteger programmingspa
dc.subject.keywordMathematical programmingspa
dc.subject.keywordMedicine replenishmentspa
dc.subject.keywordPharmaceutical supply chainspa
dc.subject.keywordSample Pathspa
dc.subject.keywordSimulation-optimizationspa
dc.subject.keywordStochastic counterpartspa
dc.titleOptimization under uncertainty of the pharmaceutical supply chain in hospitalsspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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