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....
- 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
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
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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 |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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 |
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Repositorio Institucional EdocUR |
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1803710457900433408 |