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Modelo de simulación para analizar el problema de re-localización de las ambulancia de un servicio de emergencia médico (SEM).

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Rodríguez Quintero, Alma Karina
Abstract (Spanish)
Los Servicios de Emergencias Médicas-SEM son sistemas responsables de la estabilización y transporte pre-hospitalario de pacientes con urgencia y emergencia médicas. Por esta razón generalmente la diferencia entre la vida y la muerte de los pacientes ante la ocurrencia de un evento se ve afectada por la capacidad de un SEM de responder adecuadamente cuando se le solicita un servicio. Para lograr esto un SEM debe ubicar sus vehículos de tal manera que se garantice un óptimo desempeño. Sin embargo, esta es una decisión que no es fácil tomar. Los acelerados cambios de los entornos, variaciones de la demanda dependiendo del día de la semana o época del año y adicionalmente, en algunos casos, restricciones operacionales debido a la disponibilidad de los recursos hacen que los SEM tengan la necesidad de cambiar constantemente la ubicación de sus vehículos. Esto se conoce en la literatura como problemas de relocalización de vehículos. Teniendo en cuenta lo anterior, en el presente trabajo se desarrolló un modelo de simulación de eventos discretos que permite evaluar políticas de relocalización de un SEM. Para esto se utilizó como sistema de referencia el servicio de Coomeva Emergencia Médica. El cual es un servicio de atención médica a domicilio y atención de urgencias y emergencia las 24 horas del día. Se encontró que aunque en algunos casos es posible que debido a la política actual del SEM de estudio, de ubicar los vehículos en los barrios donde se prestó el último servicio, se presenten mayores tiempos de respuesta, cuando analizamos todo los servicios se observa que con esta política se generan menores porcentajes de cancelaciones y de tiempos de espera. Además, la política actual permite una mayor disponibilidad de los vehículos para atender las solicitudes de los pacientes, debido a que están disponibles inmediatamente después de terminar un servicio. A diferencia de esto, cuando los vehículos deben desplazarse a una base después de finalizar un servicio, se presenta un aumento significativo en los tiempos de viaje, lo cual afecta la disponibilidad de los vehículos y aumenta los porcentajes de cancelaciones y los tiempos de espera de los pacientes.
Abstract (English)
The Emergency Medical Services-SEM are systems responsible for the pre-hospital stabilization and transport of patients with medical urgency and emergency. For this reason, the difference between the life and death of patients in the event of an event is generally affected by the ability of an EMS to respond adequately when a service is requested. To achieve this, a SEM must locate its vehicles in such a way that optimal performance is guaranteed. However, this is a decision that is not easy to make. The rapid changes in environments, variations in demand depending on the day of the week or time of year, and additionally, in some cases, operational restrictions due to the availability of resources, mean that SEMs have the need to constantly change the location of their vehicles. This is known in the literature as vehicle relocation problems. Taking into account the above, in the present work a discrete event simulation model was developed that allows evaluating relocation policies of an SEM. For this, the Coomeva Emergency Medical service was used as a reference system. Which is a home health care service and urgent and emergency care 24 hours a day. It was found that although in some cases it is possible that due to the current policy of the study SEM, of locating the vehicles in the neighborhoods where the last service was provided, there are longer response times, when we analyze all the services it is observed that with This policy generates lower percentages of cancellations and waiting times. In addition, the current policy allows for greater availability of vehicles to meet patient requests, since they are available immediately after a service is completed. In contrast to this, when vehicles must travel to a base after a service ends, there is a significant increase in travel times, which affects the availability of vehicles and increases the percentages of cancellations and wait times of the patients.
Extent
69 páginas.
URI: https://repositorio.escuelaing.edu.co/handle/001/2050
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References

E. S. Savas, “Simulation and cost-effectiveness analysis of New York’s emergency ambulance service,” Manage. Sci., vol. 15, no. 12, pp. 608 –627, 1969.

A. Ingolfsson, E. Erkut, and S. Budge, “Simulation of single start station for Edmonton EMS,” J. Oper. Res. Soc., vol. 54, no. 7, pp. 736–746, 2003.

K. Inakawa, T. Furuta, and A. Suzuki, “Effect of ambulance station locations and number of ambulances to the quality of the emergency service,” in The Ninth International Symposium on Operations Research and Its Applications (ISORA’10), 2010, pp. 340–347.

L. Aboueljinane, Z. Jemai, and E. Sahin, “Reducing ambulance response time using simulation: The case of Val-de-Marne department Emergency Medical Service,” in Proceedings winter simulation conference (WSC), 2012, pp. 1–12.

D. H. Uyeno and C. Seeberg, “A practical methodology for ambulance location,” Simulation, vol. 43, no. 2, pp. 79–87, 1984.

W. H. Iskander, “Simulation modeling for emergency medical service systems,” in Proceedings winter simulation (WSC ), 1989, pp. 1107–1111.

S. Su and C.-L. Shih, “Modeling an emergency medical services system using computer simulation,” Int. J. Med. Inform., vol. 72, no. 1–3, pp. 57–72, 2003.

S. Su and C.-L. Shih, “Resource reallocation in an emergency medical service system using computer simulation.,” Am. J. Emerg. Med., vol. 20, no. 7, pp. 627–34, 2002.

Y. Liu, Y. Yuan, Y. Li, and H. Pang, “A chance constrained programming model for reliable emergency vehicles relocation problem,” Procedia - Soc. Behav. Sci., vol. 96, pp. 671–682, 2013.

S. Cretin and T. R. Willemain, “A model of prehospital death from ventricular fibrillation following myocardial infarction,” Health Serv. Res., vol. 14, no. 3, pp. 221–34, 1979.

M. Eisenberg, L. Bergner, and A. Hallstrom, “Paramedic programs and out-of-hospital cardiac arrest: I. Factors associated with successful resuscitation,” Am. J. Public Health, vol. 69, no. 1, pp. 30–8, 1979.

J. Naoum-Sawaya and S. Elhedhli, “A stochastic optimization model for real-time ambulance redeployment,” Comput. Oper. Res., vol. 40, no. 8, pp. 1972–1978, 2013.

M. Zhong, C. Shi, T. Fu, L. He, and J. Shi, “Study in performance analysis of China Urban Emergency Response System based on Petri net,” Saf. Sci., vol. 48, no. 6, pp. 755–762, 2010.

J. Ma, F. Mao, and W. Zhou, “Agent-based simulation for urban emergency response planning,” in Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, 2009, pp. 200–203.

A. Borshchev and A. Filippov, “From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools,” in The 22nd International Conference of the System Dynamics Society, 2004, p. 23.

A. K. Rodríguez, G. M. Osorno, and P. A. Maya, “Relocalización de vehículos en Servicios de Emergencias Médicas: una revisión,” Ing. y Cienc., 2015.

S. M. Céspedes, N. Velasco, and C. A. Amaya, “Localización y relocalización de ambulancias del centro regulador de urgencias y emergencias de Bogotá,” Universidad de los Andes, Bogotá, 2008.

J. G. Villegas R., C. Castañeda P., and K. A. Blandón, “Mejoramiento de la localización de ambulancias de atención prehospitalaria en Medellín (Colombia) con modelos de optimización,” in Congreso Latino-Iberoamericano de Investigación Operativa, 2012, p. 12.

L. Aboueljinane, E. Sahin, and Z. Jemai, “A review on simulation models applied to emergency medical service operations,” Comput. Ind. Eng., vol. 66, no. 4, pp. 734–750, 2013.

L. Zhen, K. Wang, H. Hu, and D. Chang, “A simulation optimization framework for ambulance deployment and relocation problems,” Comput. Ind. Eng., vol. 72, pp. 12–23, 2014.

K. Sudtachat, M. E. Mayorga, and L. A. Mclay, “A nested-compliance table policy for emergency medical service systems under relocation,” Omega, Jun. 2015.

M. Gendreau, G. Laporte, and F. Semet, “The maximal expected coverage relocation problem for emergency vehicles,” J. Oper. Res. Soc., vol. 57, no. 1, pp. 22–28, 2006.

M. Moeini, Z. Jemai, and E. Sahin, “Location and relocation problems in the context of the emergency medical service systems: a case study,” Cent. Eur. J. Oper. Res., vol. 23, no. 3, pp. 641–658, Dec. 2014.

Y. Yue, L. Marla, and R. Krishnan., “An efficient simulation-based approach to ambulance fleet allocation and dynamic redeployment,” in Proceedings of the national conference on artificial intelligence, 2012, pp. 398–405.

M. Gendreau, G. Laporte, and F. Semet, “A dynamic model and parallel tabu search heuristic for real-time ambulance relocation,” Parallel Comput., vol. 27, no. 12, pp. 1641–1653, Nov. 2001.

T. Andersson and P. Värbrand, “Decision support tools for ambulance dispatch and relocation,” Oper. Res. Soc., vol. 58, no. 2, pp. 195–201, Feb. 2007.

M. S. Maxwell, S. G. Henderson, and H. Topaloglu, “Ambulance redeployment: An approximate dynamic programming approach,” in Proceedings of the winter simulation conference (WSC), 2009, pp. 1850–1860.

M. Van Buuren, R. Van der Mei, K. Aardal, and H. Post, “Evaluating dynamic dispatch strategies for emergency medical services: TIFAR simulation tool,” in Proceedings of the 2012 Winter Simulation Conference (WSC), 2012, pp. 1–12.

C. J. Jagtenberg, S. Bhulai, and R. D. van der Mei, “An efficient heuristic for real-time ambulance redeployment,” Oper. Res. Heal. Care, vol. 4, pp. 27–35, Mar. 2015.

P. L. van den Berg and K. Aardal, “Time-dependent MEXCLP with start-up and relocation cost,” Eur. J. Oper. Res., vol. 242, no. 2, pp. 383–389, Apr. 2015.

V. Schmid, “Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming,” Eur. J. Oper. Res., vol. 219, no. 3, pp. 611–621, 2012.

H. K. Rajagopalan, C. Saydam, and J. Xiao, “A multiperiod set covering location model for dynamic redeployment of ambulances,” Comput. Oper. Res., vol. 35, no. 3, pp. 814–826, 2008.

M. S. Maxwell, M. Restrepo, S. G. Henderson, and H. Topaloglu, “Approximate dynamic programming for ambulance redeployment,” INFORMS J. Comput., vol. 22, no. 2, pp. 266–281, May 2010.

K. Schneeberger, K. F. Doerner, A. Kurz, and M. Schilde, “Ambulance location and relocation models in a crisis,” Cent. Eur. J. Oper. Res., pp. 1–27, Jul. 2014.

M. Moeini, Z. Jemai, and E. Sahin, “An integer programming model for the dynamic location and relocation of emergency vehicles: A case study,” in International Symposium on Operational Research in Slovenia, SOR, 2013, pp. 343–350.

J. F. Repede and J. J. Bernardo, “Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky,” Eur. J. Oper. Res., vol. 75, no. 3, pp. 567– 581, 1994.

K. Peleg and J. S. Pliskin, “A geographic information system simulation model of EMS: reducing ambulance response time,” Am. J. Emerg. Med., vol. 22, no. 3, pp. 164–170, 2004.

V. Schmid and K. F. Doerner, “Ambulance location and relocation problems with time-dependent travel times,” Eur. J. Oper. Res., vol. 207, no. 3, pp. 1293–1303, 2010.

L. Aboueljinane, E. Sahin, Z. Jemai, and J. Marty, “A simulation study to improve the performance of an emergency medical service: application to the French Val-de-Marne department,” Simul. Model. Pract. theory, vol. 47, pp. 46–59, Sep. 2014.

R. Alanis, A. Ingolfsson, and B. Kolfal, “A markov chain model for an EMS system with repositioning,” Prod. Oper. Manag., vol. 22, no. 1, pp. 216–231, Jan. 2013.

R. Nair and E. Miller-Hooks, “Evaluation of relocation strategies for emergency medical service vehicles,” Transp. Res. Rec., no. 2137, pp. 63–73, Dec. 2009.

H. Billhardt, M. Lujak, V. Sánchez-Brunete, A. Fernández, and S. Ossowski, “Dynamic coordination of ambulances for emergency medical assistance services,” Knowledge-Based Syst., vol. 70, pp. 268–280, Nov. 2014.

D. Degel, L. Wiesche, S. Rachuba, and B. Werners, “Time-dependent ambulance allocation considering data-driven empirically required coverage,” Health Care Manag. Sci., pp. 1–15, Mar. 2014.

L. Wiesche, “Time-dependent dynamic location and relocation of ambulances,” in Operations research proceedings 2013, D. Huisman, I. Louwerse, and A. P. M. Wagelmans, Eds. Cham: Springer International Publishing, 2014, pp. 481–486.

A. Haghani and S. Yang, “Real-time emergency response fleet deployment: concepts, systems, simulation & case studies,” in Dynamic fleet management, vol. 38, V. Zeimpekis, C. D. Tarantilis, G. M. Giaglis, and I. Minis, Eds. Boston, MA: Springer US, 2007, pp. 133–162.

L. A. C. G. Andrade and C. B. Cunha, “An ABC heuristic for optimizing moveable ambulance station location and vehicle repositioning for the city of São Paulo,” Int. Trans. Oper. Res., vol. 22, no. 3, pp. 473–501, May 2015.

O. Fujiwara, T. Makjamroen, and K. K. Gupta, “Ambulance deployment analysis: A case study of Bangkok,” Eur. J. Oper. Res., vol. 31, no. 1, pp. 9–18, 1987.

M. Poulton and G. Roussos, “Towards smarter metropolitan emergency response,” in IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2013, pp. 2576–2580.

S. G. Henderson and A. J. Mason, “Ambulance service planning: simulation and data visualisation,” in Operations research and health care, vol. 70, M. L. Brandeau, F. Sainfort, and W. P. Pierskalla, Eds. Boston: Kluwer Academic Publishers, 2005, pp. 77–102.

A. J. Mason, “Simulation and real-time optimised relocation for improving ambulance operations,” in Handbook of Healthcare Operations Management, vol. 184, B. T. Denton, Ed. New York, NY: Springer New York, 2013, pp. 289–317.

L. J. Shuman, H. Wolfe, and M. J. Gunter, “RURALSIM: the design and implementation of a rural EMS simulator,” J. Soc. Health Syst., vol. 3, no. 3, pp. 54–71, 1992.

J. Goldberg, R. Dietrich, J. M. Chen, M. Mitwasi, T. Valenzuela, and E. Criss, “A simulation model for evaluating a set of emergency vehicle base locations: Development, validation, and usage,” Socioecon. Plann. Sci., vol. 24, no. 2, pp. 125–141, 1990.

L. Brotcorne, G. Laporte, and F. Semet, “Ambulance location and relocation models,” Eur. J. Oper. Res., vol. 147, no. 3, pp. 451–463, 2003.

F. Glover and B. Melián, “Búsqueda Tabú,” Intel. Artif., vol. 19, pp. 29–48, 2003.

Coomeva Emergencias Médicas, “Atención de Servicios CEM.” p. 9, 2014.

E. G. Dunna, H. G. Reyes, and L. E. C. Barrón, Simulación y análisis de sistemas con ProModel. 2006.

J. Wang, J. Li, K. Tussey, and K. Ross, “Reducing Length of Stay in Emergency Department: A Simulation Study at a Community Hospital,” IEEE Trans. Syst. Man, Cybern. - Part A Syst. Humans, vol. 42, no. 6, pp. 1314–1322, Nov. 2012.

J. P. C. Kleijnen, “Verification and validation of simulation models,” Eur. J. Oper. Res., vol. 82, no. 1, pp. 145–162, Apr. 1995.

J. Barceló, Simulación de sistemas discretos. 1996.

R. G. Sargent, “Verification and validation of simulation models,” in Proceedings Winter Simulation Conference, 2010, vol. 1, pp. 13–24.

R. G. Sargent, “Verification and validation of simulation models,” J. Simul., vol. 7, no. 1, pp. 12– 24, Dec. 2013.