A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals

Managing resources in hospitals is one of the most challenging duties in healthcare. The complexity of supply chain management in hospitals is high due to different factors such as life cycle of medicines, demand uncertainty, variation of prices, monetary resources, space constraints, among others....

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Autores:
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/23753
Acceso en línea:
https://doi.org/10.1007/978-3-030-00353-1_5
https://repository.urosario.edu.co/handle/10336/23753
Palabra clave:
Decision making
Health care
Hospitals
Life cycle
Optimization
Uncertainty analysis
Demand uncertainty
Managing resources
Model uncertainties
Monetary resources
Optimal solutions
Optimization modeling
Robust optimization
Sources of uncertainty
Supply chain management
Hospital planning
Optimization
Optimization under scenarios
Pharmaceutical logistics
Robust optimization
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License
http://purl.org/coar/access_right/c_abf2
id EDOCUR2_c5b3c10abe0f4bd995e96102f1e19604
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repository_id_str
spelling A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in HospitalsDecision makingHealth careHospitalsLife cycleOptimizationUncertainty analysisDemand uncertaintyManaging resourcesModel uncertaintiesMonetary resourcesOptimal solutionsOptimization modelingRobust optimizationSources of uncertaintySupply chain managementHospital planningOptimizationOptimization under scenariosPharmaceutical logisticsRobust optimizationManaging resources in hospitals is one of the most challenging duties in healthcare. The complexity of supply chain management in hospitals is high due to different factors such as life cycle of medicines, demand uncertainty, variation of prices, monetary resources, space constraints, among others. The main important factor of the supply chain in hospitals is the welfare of patients which depends of the correct management and administration of medicines, in this way backorders or stockouts are not allowed. In this paper we propose a mathematical model to make real planning over a health care supply chain considering real factors face by decision makers. For testing results we have used real data considering different sources of uncertainty. We have choose 5 different types of medicines and run the optimization model to determine the optimal solution over a set of scenarios generated for modeling uncertainty. For testing the results, we have compare over a year planning the results obtained by our policy and the results obtained by the hospital, improving the results in terms of costs. © 2018, Springer Nature Switzerland AG.Springer Verlag20182020-05-26T00:05:06Zinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fapplication/pdfhttps://doi.org/10.1007/978-3-030-00353-1_518650929https://repository.urosario.edu.co/handle/10336/23753instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURenghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85053994555&doi=10.1007%2f978-3-030-00353-1_5&partnerID=40&md5=f9ab5fd3375d1b6248ab1615965b78bchttp://purl.org/coar/access_right/c_abf2Franco Franco, Carlos AlbertoLópez-Santana E.R.Figueroa-García J.C.oai:repository.urosario.edu.co:10336/237532022-05-02T07:37:16Z
dc.title.none.fl_str_mv A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals
title A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals
spellingShingle A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals
Decision making
Health care
Hospitals
Life cycle
Optimization
Uncertainty analysis
Demand uncertainty
Managing resources
Model uncertainties
Monetary resources
Optimal solutions
Optimization modeling
Robust optimization
Sources of uncertainty
Supply chain management
Hospital planning
Optimization
Optimization under scenarios
Pharmaceutical logistics
Robust optimization
title_short A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals
title_full A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals
title_fullStr A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals
title_full_unstemmed A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals
title_sort A Mathematical Model Under Uncertainty for Optimizing Medicine Logistics in Hospitals
dc.subject.none.fl_str_mv Decision making
Health care
Hospitals
Life cycle
Optimization
Uncertainty analysis
Demand uncertainty
Managing resources
Model uncertainties
Monetary resources
Optimal solutions
Optimization modeling
Robust optimization
Sources of uncertainty
Supply chain management
Hospital planning
Optimization
Optimization under scenarios
Pharmaceutical logistics
Robust optimization
topic Decision making
Health care
Hospitals
Life cycle
Optimization
Uncertainty analysis
Demand uncertainty
Managing resources
Model uncertainties
Monetary resources
Optimal solutions
Optimization modeling
Robust optimization
Sources of uncertainty
Supply chain management
Hospital planning
Optimization
Optimization under scenarios
Pharmaceutical logistics
Robust optimization
description Managing resources in hospitals is one of the most challenging duties in healthcare. The complexity of supply chain management in hospitals is high due to different factors such as life cycle of medicines, demand uncertainty, variation of prices, monetary resources, space constraints, among others. The main important factor of the supply chain in hospitals is the welfare of patients which depends of the correct management and administration of medicines, in this way backorders or stockouts are not allowed. In this paper we propose a mathematical model to make real planning over a health care supply chain considering real factors face by decision makers. For testing results we have used real data considering different sources of uncertainty. We have choose 5 different types of medicines and run the optimization model to determine the optimal solution over a set of scenarios generated for modeling uncertainty. For testing the results, we have compare over a year planning the results obtained by our policy and the results obtained by the hospital, improving the results in terms of costs. © 2018, Springer Nature Switzerland AG.
publishDate 2018
dc.date.none.fl_str_mv 2018
2020-05-26T00:05:06Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_c94f
dc.identifier.none.fl_str_mv https://doi.org/10.1007/978-3-030-00353-1_5
18650929
https://repository.urosario.edu.co/handle/10336/23753
url https://doi.org/10.1007/978-3-030-00353-1_5
https://repository.urosario.edu.co/handle/10336/23753
identifier_str_mv 18650929
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053994555&doi=10.1007%2f978-3-030-00353-1_5&partnerID=40&md5=f9ab5fd3375d1b6248ab1615965b78bc
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rights_invalid_str_mv http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv instname:Universidad del Rosario
reponame:Repositorio Institucional EdocUR
instname_str Universidad del Rosario
institution Universidad del Rosario
reponame_str Repositorio Institucional EdocUR
collection Repositorio Institucional EdocUR
repository.name.fl_str_mv
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