Vehicle location models for Emergency Medical Services. An application for a Colombian company
Ambulance location, in a certain area of coverage, is a key element in reducing the waiting time of a potential patient and the initiation a healthcare service in Emergency Medical Services (EMS). The Operations Research area, developing and solving adequate mathematical models, helps to make good d...
- Autores:
-
Zapata Murillo, Pablo
Baldoquin de la Peña, Maria Gulnara
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Universidad EIA .
- Repositorio:
- Repositorio EIA .
- Idioma:
- spa
- OAI Identifier:
- oai:repository.eia.edu.co:11190/5032
- Acceso en línea:
- https://repository.eia.edu.co/handle/11190/5032
https://doi.org/10.24050/reia.v15i29.1211
- Palabra clave:
- Emergency Medical Services
hospital logistics
ambulance location
mathematical models
linear programming
mathematical models
linear programming
optimization models
- Rights
- openAccess
- License
- Revista EIA - 2018
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Vehicle location models for Emergency Medical Services. An application for a Colombian company |
| dc.title.translated.eng.fl_str_mv |
Vehicle location models for Emergency Medical Services. An application for a Colombian company |
| title |
Vehicle location models for Emergency Medical Services. An application for a Colombian company |
| spellingShingle |
Vehicle location models for Emergency Medical Services. An application for a Colombian company Emergency Medical Services hospital logistics ambulance location mathematical models linear programming mathematical models linear programming optimization models |
| title_short |
Vehicle location models for Emergency Medical Services. An application for a Colombian company |
| title_full |
Vehicle location models for Emergency Medical Services. An application for a Colombian company |
| title_fullStr |
Vehicle location models for Emergency Medical Services. An application for a Colombian company |
| title_full_unstemmed |
Vehicle location models for Emergency Medical Services. An application for a Colombian company |
| title_sort |
Vehicle location models for Emergency Medical Services. An application for a Colombian company |
| dc.creator.fl_str_mv |
Zapata Murillo, Pablo Baldoquin de la Peña, Maria Gulnara |
| dc.contributor.author.spa.fl_str_mv |
Zapata Murillo, Pablo Baldoquin de la Peña, Maria Gulnara |
| dc.subject.spa.fl_str_mv |
Emergency Medical Services hospital logistics ambulance location mathematical models linear programming mathematical models linear programming optimization models |
| topic |
Emergency Medical Services hospital logistics ambulance location mathematical models linear programming mathematical models linear programming optimization models |
| description |
Ambulance location, in a certain area of coverage, is a key element in reducing the waiting time of a potential patient and the initiation a healthcare service in Emergency Medical Services (EMS). The Operations Research area, developing and solving adequate mathematical models, helps to make good decisions in EMS. We propose two variants of a Mixed Integer Linear Programming model for locating heterogeneous fleet vehicles to support different types of services in emergency medical care, taking into account the operational requirements of a health care service company in Colombia. The proposed models do not exactly match any of those found in the literature. The models are solved with Gurobi solver and the modeling language AMPL and they are successfully validated with historical data of the company under study and some estimates based on external sources. These obtained results are compared using an adaptation of the concept of Preparedness taken from the literature for ambulance positioning, as regard to measure the readiness of the system to the expected demand. The new results show that the relevance of each model depends of the prioritization of services and/or areas that the company considers. |
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2018-04-30 |
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ALSALLOUM, O. I. & RAND, G. K. 2006. Extensions to emergency vehicle location models. Computers & Operations Research, 33, 2725-2743. ANDERSSON, T. & VÄRBRAND, P. 2007. Decision support tools for ambulance dispatch and relocation. Journal of the Operational Research Society, 58, 195-201. BÉLANGER, V., RUIZ, A. & SORIANO, P. 2015. Recent advances in emergency medical services management, Tech. Rep. CIRRELT-2015-28, CIRRELT. BERALDI, P., BRUNI, M. E. & CONFORTI, D. 2004. Designing robust emergency medical service via stochastic programming. European Journal of Operational Research, 158, 183-193. BROTCORNE, L., LAPORTE, G. & SEMET, F. 2003. Ambulance location and relocation models. European journal of operational research, 147, 451-463. CÉSPEDES, S., VELASCO, N. & AMAYA, C. 2009. Localización y relocalización de ambulancias del centro regulador de urgencias y emergencias de Bogotá. CHURCH, R. & REVELLE, C. The maximal covering location problem. Papers of the Regional Science Association, 1974. Springer, 101-118. DASKIN, M. S. 1983. A maximum expected covering location model: formulation, properties and heuristic solution. Transportation science, 17, 48-70. DASKIN, M. S. 1984. Location, dispatching and routing models for emergency services with stochastic travel times. DASKIN, M. S. & STERN, E. H. 1981. A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 15, 137-152. GENDREAU, M., LAPORTE, G. & SEMET, F. 1997. Solving an ambulance location model by tabu search. Location science, 5, 75-88. GOLDBERG, J., DIETRICH, R., CHEN, J. M., MITWASI, M. G., VALENZUELA, T. & CRISS, E. 1990. Validating and applying a model for locating emergency medical vehicles in Tuczon, AZ. European Journal of Operational Research, 49, 308-324. GOLDBERG, J. B. 2004. Operations research models for the deployment of emergency services vehicles. EMS management Journal, 1, 20-39. GRODZEVICH, O. & ROMANKO, O. 2006. Normalization and other topics in multi-objective optimization. HOGAN, K. & REVELLE, C. 1986. Concepts and applications of backup coverage. Management science, 32, 1434-1444. MANDELL, M. B. 1998. Covering models for two-tiered emergency medical services systems. Location Science, 6, 355-368. ORTEGA, A. E. R., POMAR, L. A. & PEÑA, J. P. 2007. Diseño metodológico para la ubicación de ambulancias del sector de atención prehospitalaria en bogotá dc 1. Revista Ingeniería Industrial, 6. REVELLE, C. & MARIANOV, V. 1991. A probabilistic FLEET model with individual vehicle reliability requirements. European Journal of Operational Research, 53, 93-105. SAATY, T. L. 2008. Decision making with the analytic hierarchy process. International journal of services sciences, 1, 83-98. SCHILLING, D., ELZINGA, D. J., COHON, J., CHURCH, R. & REVELLE, C. 1979. The TEAM/FLEET models for simultaneous facility and equipment siting. Transportation Science, 13, 163-175. SCHMID, V. & DOERNER, K. F. 2010. Ambulance location and relocation problems with time-dependent travel times. European journal of operational research, 207, 1293-1303. SINGER, M. & DONOSO, P. 2008. Assessing an ambulance service with queuing theory. Computers & operations research, 35, 2549-2560. SU, Q., LUO, Q. & HUANG, S. H. 2015. Cost-effective analyses for emergency medical services deployment: A case study in Shanghai. International Journal of Production Economics, 163, 112-123. TOREGAS, C., SWAIN, R., REVELLE, C. & BERGMAN, L. 1971. The location of emergency service facilities. Operations Research, 19, 1363-1373. TORO-DÍAZ, H., MAYORGA, M. E., CHANTA, S. & MCLAY, L. A. 2013. Joint location and dispatching decisions for emergency medical services. Computers & Industrial Engineering, 64, 917-928. VILLEGAS, J. G., CASTAÑEDA, C. & BLANDÓN, K. A. 2012. Mejoramiento de la localización de ambulancias de atención prehospitalaria en medellín (colombia) con modelos de optimización. CLAIO/SBPO2012, 123, 12. |
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Zapata Murillo, Pablof55c2cca98506b708afe8bd10f18be2a300Baldoquin de la Peña, Maria Gulnara98dc595f9c9d4f065b75c3bc52b7efb63002018-04-30 00:00:002022-06-17T20:19:47Z2018-04-30 00:00:002022-06-17T20:19:47Z2018-04-301794-1237https://repository.eia.edu.co/handle/11190/503210.24050/reia.v15i29.12112463-0950https://doi.org/10.24050/reia.v15i29.1211Ambulance location, in a certain area of coverage, is a key element in reducing the waiting time of a potential patient and the initiation a healthcare service in Emergency Medical Services (EMS). The Operations Research area, developing and solving adequate mathematical models, helps to make good decisions in EMS. We propose two variants of a Mixed Integer Linear Programming model for locating heterogeneous fleet vehicles to support different types of services in emergency medical care, taking into account the operational requirements of a health care service company in Colombia. The proposed models do not exactly match any of those found in the literature. The models are solved with Gurobi solver and the modeling language AMPL and they are successfully validated with historical data of the company under study and some estimates based on external sources. These obtained results are compared using an adaptation of the concept of Preparedness taken from the literature for ambulance positioning, as regard to measure the readiness of the system to the expected demand. The new results show that the relevance of each model depends of the prioritization of services and/or areas that the company considers.Ambulance location, in a certain area of coverage, is a key element in reducing the waiting time of a potential patient and the initiation a healthcare service in Emergency Medical Services (EMS). The Operations Research area, developing and solving adequate mathematical models, helps to make good decisions in EMS. We propose two variants of a Mixed Integer Linear Programming model for locating heterogeneous fleet vehicles to support different types of services in emergency medical care, taking into account the operational requirements of a health care service company in Colombia. The proposed models do not exactly match any of those found in the literature. The models are solved with Gurobi solver and the modeling language AMPL and they are successfully validated with historical data of the company under study and some estimates based on external sources. These obtained results are compared using an adaptation of the concept of Preparedness taken from the literature for ambulance positioning, as regard to measure the readiness of the system to the expected demand. The new results show that the relevance of each model depends of the prioritization of services and/or areas that the company considers.application/pdfspaFondo Editorial EIA - Universidad EIARevista EIA - 2018https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://revistas.eia.edu.co/index.php/reveia/article/view/1211Emergency Medical Serviceshospital logisticsambulance locationmathematical modelslinear programmingmathematical modelslinear programmingoptimization modelsVehicle location models for Emergency Medical Services. An application for a Colombian companyVehicle location models for Emergency Medical Services. An application for a Colombian companyArtículo de revistaJournal articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARTREFhttp://purl.org/coar/version/c_970fb48d4fbd8a85ALSALLOUM, O. I. & RAND, G. K. 2006. Extensions to emergency vehicle location models. Computers & Operations Research, 33, 2725-2743.ANDERSSON, T. & VÄRBRAND, P. 2007. Decision support tools for ambulance dispatch and relocation. Journal of the Operational Research Society, 58, 195-201.BÉLANGER, V., RUIZ, A. & SORIANO, P. 2015. Recent advances in emergency medical services management, Tech. Rep. CIRRELT-2015-28, CIRRELT.BERALDI, P., BRUNI, M. E. & CONFORTI, D. 2004. Designing robust emergency medical service via stochastic programming. European Journal of Operational Research, 158, 183-193.BROTCORNE, L., LAPORTE, G. & SEMET, F. 2003. Ambulance location and relocation models. European journal of operational research, 147, 451-463.CÉSPEDES, S., VELASCO, N. & AMAYA, C. 2009. Localización y relocalización de ambulancias del centro regulador de urgencias y emergencias de Bogotá.CHURCH, R. & REVELLE, C. The maximal covering location problem. Papers of the Regional Science Association, 1974. Springer, 101-118.DASKIN, M. S. 1983. A maximum expected covering location model: formulation, properties and heuristic solution. Transportation science, 17, 48-70.DASKIN, M. S. 1984. Location, dispatching and routing models for emergency services with stochastic travel times.DASKIN, M. S. & STERN, E. H. 1981. A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 15, 137-152.GENDREAU, M., LAPORTE, G. & SEMET, F. 1997. Solving an ambulance location model by tabu search. Location science, 5, 75-88.GOLDBERG, J., DIETRICH, R., CHEN, J. M., MITWASI, M. G., VALENZUELA, T. & CRISS, E. 1990. Validating and applying a model for locating emergency medical vehicles in Tuczon, AZ. European Journal of Operational Research, 49, 308-324.GOLDBERG, J. B. 2004. Operations research models for the deployment of emergency services vehicles. EMS management Journal, 1, 20-39.GRODZEVICH, O. & ROMANKO, O. 2006. Normalization and other topics in multi-objective optimization.HOGAN, K. & REVELLE, C. 1986. Concepts and applications of backup coverage. Management science, 32, 1434-1444.MANDELL, M. B. 1998. Covering models for two-tiered emergency medical services systems. Location Science, 6, 355-368.ORTEGA, A. E. R., POMAR, L. A. & PEÑA, J. P. 2007. Diseño metodológico para la ubicación de ambulancias del sector de atención prehospitalaria en bogotá dc 1. Revista Ingeniería Industrial, 6.REVELLE, C. & MARIANOV, V. 1991. A probabilistic FLEET model with individual vehicle reliability requirements. European Journal of Operational Research, 53, 93-105.SAATY, T. L. 2008. Decision making with the analytic hierarchy process. International journal of services sciences, 1, 83-98.SCHILLING, D., ELZINGA, D. J., COHON, J., CHURCH, R. & REVELLE, C. 1979. The TEAM/FLEET models for simultaneous facility and equipment siting. Transportation Science, 13, 163-175.SCHMID, V. & DOERNER, K. F. 2010. Ambulance location and relocation problems with time-dependent travel times. European journal of operational research, 207, 1293-1303.SINGER, M. & DONOSO, P. 2008. Assessing an ambulance service with queuing theory. Computers & operations research, 35, 2549-2560.SU, Q., LUO, Q. & HUANG, S. H. 2015. Cost-effective analyses for emergency medical services deployment: A case study in Shanghai. International Journal of Production Economics, 163, 112-123.TOREGAS, C., SWAIN, R., REVELLE, C. & BERGMAN, L. 1971. The location of emergency service facilities. Operations Research, 19, 1363-1373.TORO-DÍAZ, H., MAYORGA, M. E., CHANTA, S. & MCLAY, L. A. 2013. Joint location and dispatching decisions for emergency medical services. Computers & Industrial Engineering, 64, 917-928.VILLEGAS, J. G., CASTAÑEDA, C. & BLANDÓN, K. A. 2012. Mejoramiento de la localización de ambulancias de atención prehospitalaria en medellín (colombia) con modelos de optimización. CLAIO/SBPO2012, 123, 12.https://revistas.eia.edu.co/index.php/reveia/article/download/1211/1174Núm. 29 , Año 201846293115Revista EIAPublicationOREORE.xmltext/xml2615https://repository.eia.edu.co/bitstreams/6de7d185-a883-43eb-b837-b199c960ae07/download2112f8d1e59da317b6fcf8580fc96bcdMD5111190/5032oai:repository.eia.edu.co:11190/50322023-07-25 17:22:39.492https://creativecommons.org/licenses/by-nc-sa/4.0/Revista EIA - 2018metadata.onlyhttps://repository.eia.edu.coRepositorio Institucional Universidad EIAbdigital@metabiblioteca.com |