Optimization under uncertainty of the pharmaceutical supply chain in hospitals

In 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 re...

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Detalles Bibliográficos
Autores: Franco Franco, Carlos Alberto, Alfonso-Lizarazo, Edgar
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:Colombia
Institución:Universidad del Rosario
Repositorio:Repositorio EdocUR - U. Rosario
Idioma:inglés
OAI Identifier:oai:repository.urosario.edu.co:10336/23768
Acceso en línea:https://doi.org/10.1016/j.compchemeng.2019.106689
https://repository.urosario.edu.co/handle/10336/23768
Access Level:acceso abierto
Palabra clave:Hospitals
Inventory control
Mathematical programming
Medicine
Stochastic models
Stochastic systems
Supply chains
Bi-objective optimization
Epsilon-constraint method
Mixed integer programming model
Optimization under uncertainty
Pharmaceutical supply chains
Sample path
Simulation optimization
Stochastic counterpart
Integer programming
Medicine replenishment
Pharmaceutical supply chain
Sample Path
Simulation-optimization
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spelling Optimization under uncertainty of the pharmaceutical supply chain in hospitalsFranco Franco, Carlos AlbertoAlfonso-Lizarazo, EdgarHospitalsInventory controlMathematical programmingMedicineStochastic modelsStochastic systemsSupply chainsBi-objective optimizationEpsilon-constraint methodMixed integer programming modelOptimization under uncertaintyPharmaceutical supply chainsSample pathSimulation optimizationStochastic counterpartInteger programmingMathematical programmingMedicine replenishmentPharmaceutical supply chainSample PathSimulation-optimizationStochastic counterpartIn 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 LtdElsevier Ltd20202020-05-26T00:05:13Zinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1016/j.compchemeng.2019.106689981354https://repository.urosario.edu.co/handle/10336/23768reponame:Repositorio EdocUR - U. Rosarioinstname:Universidad del Rosarioinstacron:Universidad del Rosarioenghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081236702&doi=10.1016%2fj.compchemeng.2019.106689&partnerID=40&md5=432be97781b244eb0f460bd7bab68c61info:eu-repo/semantics/openAccess2022-05-02T07:37:14Z
dc.title.none.fl_str_mv Optimization under uncertainty of the pharmaceutical supply chain in hospitals
title Optimization under uncertainty of the pharmaceutical supply chain in hospitals
spellingShingle Optimization under uncertainty of the pharmaceutical supply chain in hospitals
Franco Franco, Carlos Alberto
Hospitals
Inventory control
Mathematical programming
Medicine
Stochastic models
Stochastic systems
Supply chains
Bi-objective optimization
Epsilon-constraint method
Mixed integer programming model
Optimization under uncertainty
Pharmaceutical supply chains
Sample path
Simulation optimization
Stochastic counterpart
Integer programming
Mathematical programming
Medicine replenishment
Pharmaceutical supply chain
Sample Path
Simulation-optimization
Stochastic counterpart
title_short Optimization under uncertainty of the pharmaceutical supply chain in hospitals
title_full Optimization under uncertainty of the pharmaceutical supply chain in hospitals
title_fullStr Optimization under uncertainty of the pharmaceutical supply chain in hospitals
title_full_unstemmed Optimization under uncertainty of the pharmaceutical supply chain in hospitals
title_sort Optimization under uncertainty of the pharmaceutical supply chain in hospitals
dc.creator.none.fl_str_mv Franco Franco, Carlos Alberto
Alfonso-Lizarazo, Edgar
author Franco Franco, Carlos Alberto
author_facet Franco Franco, Carlos Alberto
Alfonso-Lizarazo, Edgar
author_role author
author2 Alfonso-Lizarazo, Edgar
author2_role author
dc.subject.none.fl_str_mv Hospitals
Inventory control
Mathematical programming
Medicine
Stochastic models
Stochastic systems
Supply chains
Bi-objective optimization
Epsilon-constraint method
Mixed integer programming model
Optimization under uncertainty
Pharmaceutical supply chains
Sample path
Simulation optimization
Stochastic counterpart
Integer programming
Mathematical programming
Medicine replenishment
Pharmaceutical supply chain
Sample Path
Simulation-optimization
Stochastic counterpart
topic Hospitals
Inventory control
Mathematical programming
Medicine
Stochastic models
Stochastic systems
Supply chains
Bi-objective optimization
Epsilon-constraint method
Mixed integer programming model
Optimization under uncertainty
Pharmaceutical supply chains
Sample path
Simulation optimization
Stochastic counterpart
Integer programming
Mathematical programming
Medicine replenishment
Pharmaceutical supply chain
Sample Path
Simulation-optimization
Stochastic counterpart
description In 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 Ltd
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-05-26T00:05:13Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.compchemeng.2019.106689
981354
https://repository.urosario.edu.co/handle/10336/23768
url https://doi.org/10.1016/j.compchemeng.2019.106689
https://repository.urosario.edu.co/handle/10336/23768
identifier_str_mv 981354
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081236702&doi=10.1016%2fj.compchemeng.2019.106689&partnerID=40&md5=432be97781b244eb0f460bd7bab68c61
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier Ltd
publisher.none.fl_str_mv Elsevier Ltd
dc.source.none.fl_str_mv reponame:Repositorio EdocUR - U. Rosario
instname:Universidad del Rosario
instacron:Universidad del Rosario
instname_str Universidad del Rosario
instacron_str Universidad del Rosario
institution Universidad del Rosario
reponame_str Repositorio EdocUR - U. Rosario
collection Repositorio EdocUR - U. Rosario
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score 15.812455

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