Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach

Autores
Pulido, Raul; Aguirre, Adrian Marcelo; Ibañez Herrero, Natalia; Ortega Mier, Miguel; García Sanchez, Alvaro; Mendez, Carlos Alberto
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The operating theatres are the engine of the hospitals; proper management of the operating rooms and its staff represents a great challenge for managers and its results impact directly in the budget of the hospital. This work presents a MILP model for the efficient schedule of multiple surgeries in Operating Rooms (ORs) during a working day. This model considers multiple surgeons and ORs and different types of surgeries. Stochastic strategies are also implemented for taking into account the uncertain in surgery durations (pre-incision, incision, post-incision times). In addition, a heuristic-based methods and a MILP decomposition approach is proposed for solving large-scale ORs scheduling problems in computational efficient way. All these computer-aided strategies has been implemented in AIMMS, as an advanced modeling and optimization software, developing a user friendly solution tool for the operating room management under uncertainty.
Fil: Pulido, Raul. Escuela Técnica Superior de Ingenieros Industriales; España
Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Ibañez Herrero, Natalia. Escuela Técnica Superior de Ingenieros Industriales; España
Fil: Ortega Mier, Miguel. Escuela Técnica Superior de Ingenieros Industriales; España
Fil: García Sanchez, Alvaro. Escuela Técnica Superior de Ingenieros Industriales; España
Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Materia
Stochastic Optimization
Decomposition Approach
Scheduling
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/9580
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/9580
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approachPulido, RaulAguirre, Adrian MarceloIbañez Herrero, NataliaOrtega Mier, MiguelGarcía Sanchez, AlvaroMendez, Carlos AlbertoStochastic OptimizationDecomposition ApproachSchedulinghttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2The operating theatres are the engine of the hospitals; proper management of the operating rooms and its staff represents a great challenge for managers and its results impact directly in the budget of the hospital. This work presents a MILP model for the efficient schedule of multiple surgeries in Operating Rooms (ORs) during a working day. This model considers multiple surgeons and ORs and different types of surgeries. Stochastic strategies are also implemented for taking into account the uncertain in surgery durations (pre-incision, incision, post-incision times). In addition, a heuristic-based methods and a MILP decomposition approach is proposed for solving large-scale ORs scheduling problems in computational efficient way. All these computer-aided strategies has been implemented in AIMMS, as an advanced modeling and optimization software, developing a user friendly solution tool for the operating room management under uncertainty.Fil: Pulido, Raul. Escuela Técnica Superior de Ingenieros Industriales; EspañaFil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Ibañez Herrero, Natalia. Escuela Técnica Superior de Ingenieros Industriales; EspañaFil: Ortega Mier, Miguel. Escuela Técnica Superior de Ingenieros Industriales; EspañaFil: García Sanchez, Alvaro. Escuela Técnica Superior de Ingenieros Industriales; EspañaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaTadbir Operational Research Group2014-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/9580Pulido, Raul; Aguirre, Adrian Marcelo; Ibañez Herrero, Natalia; Ortega Mier, Miguel; García Sanchez, Alvaro; et al.; Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach; Tadbir Operational Research Group; Journal of Applied Operational Research; 6; 9-2014; 145-1571735-8523enginfo:eu-repo/semantics/altIdentifier/url/http://orlabanalytics.ca/jaor/archive/v6n3.htminfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:58:07Zoai:ri.conicet.gov.ar:11336/9580instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:58:07.469CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach
title Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach
spellingShingle Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach
Pulido, Raul
Stochastic Optimization
Decomposition Approach
Scheduling
title_short Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach
title_full Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach
title_fullStr Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach
title_full_unstemmed Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach
title_sort Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach
dc.creator.none.fl_str_mv Pulido, Raul
Aguirre, Adrian Marcelo
Ibañez Herrero, Natalia
Ortega Mier, Miguel
García Sanchez, Alvaro
Mendez, Carlos Alberto
author Pulido, Raul
author_facet Pulido, Raul
Aguirre, Adrian Marcelo
Ibañez Herrero, Natalia
Ortega Mier, Miguel
García Sanchez, Alvaro
Mendez, Carlos Alberto
author_role author
author2 Aguirre, Adrian Marcelo
Ibañez Herrero, Natalia
Ortega Mier, Miguel
García Sanchez, Alvaro
Mendez, Carlos Alberto
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Stochastic Optimization
Decomposition Approach
Scheduling
topic Stochastic Optimization
Decomposition Approach
Scheduling
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The operating theatres are the engine of the hospitals; proper management of the operating rooms and its staff represents a great challenge for managers and its results impact directly in the budget of the hospital. This work presents a MILP model for the efficient schedule of multiple surgeries in Operating Rooms (ORs) during a working day. This model considers multiple surgeons and ORs and different types of surgeries. Stochastic strategies are also implemented for taking into account the uncertain in surgery durations (pre-incision, incision, post-incision times). In addition, a heuristic-based methods and a MILP decomposition approach is proposed for solving large-scale ORs scheduling problems in computational efficient way. All these computer-aided strategies has been implemented in AIMMS, as an advanced modeling and optimization software, developing a user friendly solution tool for the operating room management under uncertainty.
Fil: Pulido, Raul. Escuela Técnica Superior de Ingenieros Industriales; España
Fil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
Fil: Ibañez Herrero, Natalia. Escuela Técnica Superior de Ingenieros Industriales; España
Fil: Ortega Mier, Miguel. Escuela Técnica Superior de Ingenieros Industriales; España
Fil: García Sanchez, Alvaro. Escuela Técnica Superior de Ingenieros Industriales; España
Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina
description The operating theatres are the engine of the hospitals; proper management of the operating rooms and its staff represents a great challenge for managers and its results impact directly in the budget of the hospital. This work presents a MILP model for the efficient schedule of multiple surgeries in Operating Rooms (ORs) during a working day. This model considers multiple surgeons and ORs and different types of surgeries. Stochastic strategies are also implemented for taking into account the uncertain in surgery durations (pre-incision, incision, post-incision times). In addition, a heuristic-based methods and a MILP decomposition approach is proposed for solving large-scale ORs scheduling problems in computational efficient way. All these computer-aided strategies has been implemented in AIMMS, as an advanced modeling and optimization software, developing a user friendly solution tool for the operating room management under uncertainty.
publishDate 2014
dc.date.none.fl_str_mv 2014-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/9580
Pulido, Raul; Aguirre, Adrian Marcelo; Ibañez Herrero, Natalia; Ortega Mier, Miguel; García Sanchez, Alvaro; et al.; Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach; Tadbir Operational Research Group; Journal of Applied Operational Research; 6; 9-2014; 145-157
1735-8523
url http://hdl.handle.net/11336/9580
identifier_str_mv Pulido, Raul; Aguirre, Adrian Marcelo; Ibañez Herrero, Natalia; Ortega Mier, Miguel; García Sanchez, Alvaro; et al.; Optimization methods for the operation room management under uncertainty: Stochastic programming vs. Decomposition approach; Tadbir Operational Research Group; Journal of Applied Operational Research; 6; 9-2014; 145-157
1735-8523
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://orlabanalytics.ca/jaor/archive/v6n3.htm
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Tadbir Operational Research Group
publisher.none.fl_str_mv Tadbir Operational Research Group
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269502402723840
score 13.13397

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