Comparación de métodos de optimización para la generación de horarios para personal de enfermería

dc.contributorOrjuela-Cañón, Alvaro David
dc.contributorGiBiome
dc.creatorCastrillón Rodríguez, Johan Alejandro
dc.date.accessioned2021-06-16T15:20:21Z
dc.date.accessioned2022-09-22T13:46:55Z
dc.date.available2021-06-16T15:20:21Z
dc.date.available2022-09-22T13:46:55Z
dc.date.created2021-06-16T15:20:21Z
dc.identifierhttps://repository.urosario.edu.co/handle/10336/31620
dc.identifierhttps://doi.org/10.48713/10336_31620
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3432447
dc.description.abstractThe organization of nursing staff is an optimization problem that has been studied extensively in the last 4 decades since computing in various ways. One of the forms of work that has been presented is the use of artificial intelligence to find optimal solutions from the definition of restrictions, periods of time to organize and the amount of personnel available to cover the needs of the different schools on the that you can apply these types of activities. For the development of this document, a search was carried out for AI methods in which the health field has relied widely to solve different problems that involve optimizing resources to guarantee the best possible operation, methods among which are the Genetic Algorithms, the Simulated Annealing, and some variants of swarm intelligence such as the Search by School of Fish, methods with which algorithms were developed to solve a problem of optimization of nursing personnel and their efficiencies were subsequently compared in depending on the time required for each of the models to arrive at solutions that are applicable in a real work environment.
dc.languagespa
dc.publisherUniversidad del Rosario
dc.publisherIngeniería Biomédica
dc.publisherEscuela de Medicina y Ciencias de la Salud
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/2.5/co/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAbierto (Texto Completo)
dc.rightsEL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma.
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia
dc.sourceF. S. Palma, S. V. Süazo y O. S. Alvarado, «EL TRABAJO DEL PROFESIONALDE ENFERMERÍA: REVISIÓN DE LA LITERATURA,»Ciencia y enfermería, n.o2,2015.doi:http://dx.doi.org/10.4067/S0717-95532015000200002.
dc.sourceO. del Carmen De Arco-Canoles y Z. K. Suarez-Calle, «Rol de los profesionales deenfermería en el sistema de salud colombiano,»Universidad y salud, n.o2, 2018.doi:https://doi.org/10.22267/rus.182002.121.
dc.sourceA. Wren, «Scheduling, Timetabling and Rostering - A Special Relationship?»Interna-tional Conference on the Practice and Theory of Automated Timetabling, págs. 46-75,1996.doi:https://doi.org/10.1007/3-540-61794-9_51.
dc.sourceD. M. Warner, «Scheduling Nursing Personnel According to Nursing Preference: AMathematical Programming Approach,»Operations Research, n.o5, págs. 842-856,1976.doi:http://dx.doi.org/10.1287/opre.24.5.842.
dc.sourceF. Seguel y S. Valenzuela, «Relación entre la fatiga laboral y el síndrome burnouten personal de enfermería de centros hospitalarios,»Enfermería universitaria, n.o4,págs. 119-127, 2014.
dc.sourceA. Fernández-Sánchez, A. Juárez-García, F. Arias-Galicia y M. E. González-Zermeño,«Agotamiento profesional en personal de enfermería su relación con variables demográ-ficas y laborales,»Revista de Enfermería del Instituto Mexicano del Seguro Social, n.o1,págs. 15-22, 2010
dc.sourceR. R. Gonzales, Y. R. Doval y O. M. Pérez, « Estrés Laboral, consideraciones sobre suscaracterísticas y formas de afrontamiento,»Revista Internacional De Psicología, n.o1,págs. 1-19, 2002.doi:https://doi.org/10.33670/18181023.v3i01.13.
dc.sourceS. Çelik, N. Taşdemir, A. Kurt, E. İlgezdi y Ö. Kubalas, «Fatigue in Intensive Care Nur-ses and Related Factors,»the international journal of occupational and environmentalmedicine, n.o4, págs. 199-206, 2017.doi:10.15171/ijoem.2017.1137.
dc.sourceM. E. S. Meza y G. M. Moré, «Errores de enfermería en la administración de fármacosen unidades hospitalarias,» 2014.
dc.sourceP. Innocent y R. John, «Computer aided fuzzy medical diagnosis,»Information Scien-ces, n.o2, págs. 81-104, 2017.doi:https://doi.org/10.1016/j.ins.2004.03.003.
dc.sourceP. Johnson, L. Vandewater, W. Wilson y P. Maruff, «Genetic algorithm with logisticregression for prediction of progression to Alzheimer’s disease,»BMC Bioinformatics,S11, 2014.doi:10.1186/1471-2105-15-S16-S11.
dc.sourceA. S. Miller, B. H. Blott y T. K. hames, «Review of neural network applications inmedical imaging and signal processing,» págs. 449-464, 1992.doi:https://doi.org/10.1007/BF02457822.
dc.sourceL. T. Merlot, N. Boland, B. D. Hughes y P. J. Stuckey, «A Hybrid Algorithm forthe Examination Timetabling Problem,»International Conference on the Practice andTheory of Automated Timetabling, págs. 207-231, 2003.doi:10.1007/978-3-540-45157-0_14.
dc.sourceZ. Jin y F. Teng, «Research of Genetic Algorithm in the Medical Logistics DistributionRouting Optimization,»Second International Conference on Intelligent ComputationTechnology and Automation, 2009.doi:10.1109/icicta.2009.116.
dc.sourceT. Vidal, T. G. Crainic, M. Gendreau y C. Prins, «A hybrid genetic algorithm withadaptive diversity management for a large class of vehicle routing problems with time-windows,»Computers Operations Research, n.o1, págs. 475-489, 2013.doi:10.1016/j.cor.2012.07.018.
dc.sourceC. Catania, C. Zanni-Merk, F. de Bertrand de Beuvron y P. Collet, «A Multi ObjectiveEvolutionary Algorithm for Solving a Real Health Care Fleet Optimization Problem,»Procedia Computer Science, págs. 256-265, 2015.doi:10.1016/j.procs.2015.08.125.
dc.sourceH. Kawanaka, K. Yamamoto, T. Yoshikawa, T. Shinogi y S. Tsuruoka, «Genetic al-gorithm with the constraints for nurse scheduling problem,»Proceedings of the 2001Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), págs. 1123-1130,2001.doi:10.1109/CEC.2001.934317.
dc.sourceS. Kundu, M. Mahato, B. Mahanty y S. Acharyya, «Comparative Performance of Simu-lated Annealing and Genetic Algorithm in Solving Nurse Scheduling Problem,»LectureNotes in Engineering and Computer Science, págs. 19-21, 2008.
dc.sourceG. Du, Z. Jiang, Y. Yao y X. Diao, «Clinical Pathways Scheduling Using Hybrid GeneticAlgorithm,»Journal of Medical Systems, n.o9945, 2013.doi:https://doi.org/10.1007/s10916-013-9945-4.
dc.sourceR. Patel, I. M. L. Jr. y M. E. Halloran, «Finding optimal vaccination strategies forpandemic influenza using genetic algorithms,»Journal of Theoretical Biology, n.o2,págs. 201-212, 2005.doi:https://doi.org/10.1016/j.jtbi.2004.11.032
dc.sourceG. A. Ezzell y L. Gaspar, «Application of a genetic algorithm to optimizing radia-tion therapy treatment plans for pancreatic carcinoma,»Medical Dosimetry, n.o2,págs. 93-97, 2000.doi:https://doi.org/10.1016/S0958-3947(00)00035-2.
dc.sourceL. Zhang, F. C. Chang y R. Xu, «The Patient Admission Scheduling of an OphthalmicHospital Using Genetic Algorithm,»Proceedings of the 2012 2nd International Confe-rence on Computer and Information Application (ICCIA 2012), págs. 1-7, 2012.doi:https://doi.org/10.2991/iccia.2012.1.
dc.sourceC. S. Moreno y F. Castaño, «Evaluación de reglas de prioridad para la programaciónde cirugías en ambientes con limitada disponibilidad de recursos,»Scientia et technica,n.o1, págs. 58-67, 2018.doi:https://doi.org/10.22517/issn.2344-7214.
dc.sourceS. webb, «Optimisation of conformal radiotherapy dose distributions by simulated an-nealing,»Physics in Medicine and Biology, n.o10, págs. 1349-1370, 1989.doi:10.1088/0031-9155/34/10/002.
dc.sourceS. M. Morrill, R. G. Lane e I. I. Rosen, «Treatment planning optimization using cons-trained simulated annealing,»Physics in Medicine Biology, n.o10, págs. 1341-1361,1991.doi:10.1088/0031-9155/36/10/004
dc.sourceF. Hu, M. Wang, Y. Zhu, J. Liu e Y. Jia, «A time simulated annealing-back propagationalgorithm and its application in disease prediction,»Modern Physics Letters B, n.o25,2018.doi:10.1142/s0217984918503037.
dc.sourceS. N. Kumar, A. L. Fred y P. S. Varghese, «Compression of CT Images using ContextualVector Quantization with Simulated Annealing for Telemedicine Application,»Journalof Medical Systems, n.o48, 2018.doi:https://doi.org/10.1007/s10916-018-1090-7.
dc.sourceJ. Kennedy y R. Eberhart, «Particle swarm optimization,»Proceedings of ICNN’95-international conference on neural networks, págs. 1942-1948, 1995.doi:10.1109/mhs.1995.494215
dc.sourceG. M.Jaradat, A. Al-Badareen, M. Ayob, M. Al-Smadi, I. Al-Marashdeh, M. Ash-Shuqran y E. Al-Odat, «Hybrid Elitist-Ant System for Nurse-Rostering Problem,»Jour-nal of King Saud University – Computer and Information Sciences, n.o3, págs. 378-384,2019.doi:https://doi.org/10.1016/j.jksuci.2018.02.009.
dc.sourceL. Altamirano, M. Riff y L. Trilling, «A PSO algorithm to solve a real anaesthesio-logy nurse scheduling problem,»2010 International Conference of Soft Computing andPattern Recognition, 2010.doi:10.1109/SOCPAR.2010.5685868.
dc.sourceP.-C. Chang, J.-J. Lin y C.-H. Liu, «An attribute weight assignment and particle swarmoptimization algorithm for medical database classifications,»Computer Methods andPrograms in Biomedicine, n.o3, págs. 382-392, 2012.doi:https://doi.org/10.1016/j.cmpb.2010.12.004.
dc.sourceC. B. Filho, F. de Lima Neto, A. Lins, A. I. Nascimento y d Marília P. Lima, «FishSchool Search,»Nature-Inspired Algorithms for Optimisation, págs. 261-277, 2009.doi:https://doi.org/10.1007/978-3-642-00267-0_9.
dc.sourceA. Ernst, H. Jiang, M. Krishnamoorthy y, «Staff scheduling and rostering: A review ofapplications, methods and models,»European Journal of Operational Research, n.o1,págs. 3-27, 2004.
dc.sourceG. M. B. Nicho, «PLANIFICACIÓN DE HORARIOS DEL PERSONAL DE CIRU-GÍA DE UN HOSPITAL DEL ESTADO APLICANDO ALGORITMOS GENÉTICOS(TIME TABLING PROBLEM),»PONTIFICIA UNIVERSIDAD CATÓLICA DELPERÚ FACULTAD DE CIENCIAS E INGENIERÍA, 2010.
dc.sourceS. E. Haupt, «Introduction to Genetic Algorithms,»Artificial Intelligence Methods inthe Environmental Sciences, págs. 103-125, 2009.doi:.https://doi.org/10.1007/978-1-4020-9119-3_5.
dc.sourceK. Y. Lee y M. A. El-Sharkawi, «Fundamentals of genetic algorithms,»MODERN HEU-RISTIC OPTIMIZATION TECHNIQUES: THEORY AND APPLICATIONS TO PO-WER SYSTEMS (IEEE PRESS SERIES ON POWER ENGINEERING), págs. 25-42,2008
dc.sourceM. Mitchell, «An Introduction to Genetic Algorithms,»Complex Adaptive Systems fromMIT press, capitulo 2, 1996.
dc.sourceM. B. M. Batista, J. A. M. Pérez y J. M. M. Vega, «Algoritmos Genéticos. Una visiónpráctica,»Números: Revista de didáctica de las matemáticas (Ejemplar dedicado a:Darwin), n.o71, pág. 4, 2009.
dc.sourceK. Dowsland y A. Díaz, «Diseño de heurísticas y fundamentos del recocido simulado,»Handbook of Natural Computing, n.o19, págs. 93-101, 2003
dc.sourceK. A. Dowsland y J. M. Thompson, «Simulated Annealing,»Handbook of NaturalComputing, págs. 1623-1655, 2012.doi:https://doi.org/10.1007/978-3-540-92910-9_49.
dc.sourceW. Odziemczyk, «Application of simulated annealing algorithm for 3D coordinate trans-formation problem solution,»Open Geosciences, vol. 12, págs. 491-502, jul. de 2020.doi:10.1515/geo-2020-0038.
dc.sourceH. E. Romeijn y R. L. Smith, «Simulated Annealing and Adaptive Search in Glo-bal Optimization,»Probability in the Engineering and Informational Sciences, n.o4,págs. 571-590, 1994.doi:https://doi.org/10.1017/S0269964800003624
dc.sourceR. C. Eberhart e Y. Shi, «Particle Swarm Optimization: Developments, Applications andResources,»2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546),2001.doi:10.1109/cec.2001.934374.
dc.sourceI. Atsuko y N. Akira, «A subproblem-centric model and approach to the nurse schedulingproblem,»Mathematical Programming, n.o3, págs. 517-541, 2003.doi:10.1007/s10107-003-0426-2.
dc.sourceK. Candotti, D. Mavares y R. Velásquez, «Comparación de métodos metaheurísticos deoptimización: recocido simulado, algoritmos genéticos y búsqueda del cuco.,»Universi-dad, Ciencia y Tecnología, n.o71, 2014
dc.sourceA. Ghaher, S. Shoar, M. Naderan y S. S. Hoseini, «The Applications of Genetic Algo-rithms in Medicine,»Oman Medical Journal, n.o6, págs. 406-416, 2015.doi:10.5001/omj.2015.82.
dc.sourceinstname:Universidad del Rosario
dc.sourceinstname:Universidad del Rosario
dc.sourcereponame:Repositorio Institucional EdocUR
dc.subjectAlgoritmos Genéticos
dc.subjectRecocido Simulado
dc.subjectBúsqueda por Cardumen de Peces
dc.subjectProblemas de programación de Horarios
dc.titleComparación de métodos de optimización para la generación de horarios para personal de enfermería
dc.typebachelorThesis


Este ítem pertenece a la siguiente institución