Emergency Ambulance Deployment in Val-de-Marne Department - A Simulation-based Iterative Approach

Lina Aboueljinane, Evren Sahin, Zied Jemai

2013

Abstract

The French Emergency Medical services, known as SAMU, are public safety systems responsible for the coordination of pre-hospital care under emergency conditions throughout a given geographic region. The goal of such systems is to respond timely and adequately to population calls by providing first aid services and transferring patients, when needed, to the appropriate care facility. In this paper, we propose a multi-period version of the Maximum Expected Covering Location Problem applied to the case of the SAMU 94 responsible for the Val-de-Marne department (France). The assumption that the busy fractions are identical for all demand points is relaxed by adopting an iterative method to compute a priori estimates of these parameters in the model using an ARENA discrete-event simulation model of the SAMU 94. The solutions obtained from the mathematical model are then assessed by simulation regarding the time required to respond to an emergency call by getting to the patient location, known as response time, which is a critical aspect for the SAMU providers. Experimental results showed that the proposed method increased average percentage of most serious calls responded to within the target time of 15 minutes up to 15\% compared to the current system performance.

References

  1. Aringhieri, R., Carello, G., Morale, D., 2007. Ambulance location through optimization and simulation: the case of Milano urban area, in: 38th Annual Conference of the Italian Operations Research Society Optimization and Decision Sciences.
  2. Batta, R., Dolan, J. M., Krishnamurthy, N. N., 1989. The Maximal Expected Covering Location Problem: Revisited. Transportation Science 23, 277-287.
  3. Berlin, G. N., Liebman, J. C., 1974. Mathematical analysis of emergency ambulance location. Socio-Economic Planning Sciences 8, 323-328.
  4. Bianchi, G., Church, R. L., 1988. A hybrid FLEET model for emergency medical service system design. Social science & medicine 26, 163-171.
  5. Church, R., ReVelle, C., 1974. The maximal covering location problem. Papers of the Regional Science Association 32, 101-118.
  6. Cummins, R. O., 1989. From concept to standard-of-care? Review of the clinical experience with automated external defibrillators. Annals of Emergency Medicine 18, 1269-1275.
  7. Daskin, M. S., 1983. A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution. Transportation Science 17, 48-70.
  8. Daskin, M. S., Stern, E. H., 1981. A Hierarchical Objective Set Covering Model for Emergency Medical Service Vehicle Deployment. Transportation Science 15, 137-152.
  9. Eaton, D. J., Héctor, M. L., Sanchez, U., Lantigua, R. R., Morgan, J., 1986. Determining Ambulance Deployment in Santo Domingo, Dominican Republic. Journal of the Operational Research Society 37, 113- 126.
  10. Fitzsimmons, J. A., 1971. An emergency medical system simulation model, in: Proceedings of the 1971 Winter Simulation Conference. ACM, New York, NY, USA, pp. 18-25.
  11. Fujiwara, O., Makjamroen, T., Gupta, K. K., 1987. Ambulance deployment analysis: A case study of Bangkok. European Journal of Operational Research 31, 9-18.
  12. Gendreau, M., Laporte, G., Semet, F., 1997. Solving an ambulance location model by tabu search. Location Science 5, 75-88.
  13. Goldberg, J., Dietrich, R., Chen, J. M., Mitwasi, M., Valenzuela, T., Criss, E., 1990a. A simulation model for evaluating a set of emergency vehicle base locations: Development, validation, and usage. SocioEconomic Planning Sciences 24, 125-141.
  14. Goldberg, J., Dietrich, R., Ming Chen, J., Mitwasi, M. G., Valenzuela, T., Criss, E., 1990b. Validating and applying a model for locating emergency medical vehicles in Tuczon, AZ. European Journal of Operational Research 49, 308-324.
  15. Gunes, E., Szechtman, R., 2005. A simulation model of a helicopter ambulance service, in: Proceedings of the 2005 Winter Simulation Conference.
  16. Harewood, S. I., 2002. Emergency ambulance deployment in Barbados: a multi-objective approach. Journal of the Operational Research Society 53, 185-192.
  17. Henderson, S. G., Mason, A. J., 2005. Ambulance Service Planning: Simulation and Data Visualisation, in: Brandeau, M.L., Sainfort, F., Pierskalla, W.P. (Eds.), Operations Research and Health Care. Kluwer Academic Publishers, Boston, pp. 77-102.
  18. Hogan, K., ReVelle, C., 1986. Concepts and Applications of Backup Coverage. Management Science 32, 1434- 1444.
  19. Inakawa, K., Furuta, T., Suzuki, A., 2010. Effect of Ambulance Station Locations and Number of Ambulances to the Quality of the Emergency Service, in: The 9th International Symposium on Operations Research and Its Applications (ISORA'10). ChengduJiuzhaigou, China, pp. 340-347.
  20. Ingolfsson, A., Erkut, E., Budge, S., 2003. Simulation of single start station for Edmonton EMS. The Journal of the Operational Research Society 54, 736-746.
  21. Kelton, W. D., Sadowski, R. P., Sturrock, D. T., 2008. Simulation with Arena, 4th edition. ed. McGraw-Hill, New York, NY, USA.
  22. Koch, O., Weigl, H., 2003. Modeling ambulance service of the Austrian Red Cross, in: Proceedings of the 2003 Winter Simulation Conference. S. Chick, P. J. Sánchez, D. Ferrin, and D. J. Morrice, eds, pp. 1701 - 1706.
  23. Larson, R. C., 1974. A hypercube queuing model for facility location and redistricting in urban emergency services. Computers & Operations Research 1, 67-95.
  24. Lee, T., Cho, S.-H., Jang, H., Turner, J. G., 2012. A simulation-based iterative method for a trauma center: air ambulance location problem, in: Proceedings of the 2012 Winter Simulation Conference. . C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A.M. Uhrmacher.
  25. Liu, M. S., Lee, J. T., 1988. A Simulation Of A Hospital Emergency Call System Using SLAMII. Simulation 51, 216-221.
  26. Lubicz, M., Mielczarek, B., 1987. Simulation modelling of emergency medical services. European Journal of Operational Research 29, 178-185.
  27. Marianov, V., Revelle, C., 1994. The queuing probabilistic location set covering problem and some extensions. Socio-Economic Planning Sciences 28, 167-178.
  28. Peleg, K., Pliskin, J. S., 2004. A geographic information system simulation model of EMS: reducing ambulance response time. The American Journal of Emergency Medicine 22, 164-170.
  29. Repede, J. F., Bernardo, J. J., 1994. Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky. European Journal of Operational Research 75, 567- 581.
  30. ReVelle, C., Hogan, K., 1989. The Maximum Availability Location Problem. Transportation Science 23, 192- 200.
  31. ReVelle, C. S., Marianov, V., 1991. A probabilistic FLEET model with individual reliability requirements. European Journal of Operational Research 53, 93-105.
  32. Savas, E. S., 1969. Simulation and Cost-Effectiveness Analysis of New York's Emergency Ambulance Service. Management Science 15, 608-627.
  33. Silva, P. M. S., Pinto, L. R., 2010. Emergency medical systems analysis by simulation and optimization, in: Proceedings of the 2010 Winter Simulation Conference. B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds, pp. 2422 -2432.
  34. Su, S., Shih, C. L., 2003. Modeling an emergency medical services system using computer simulation. Int J Med Inform 72, 57-72.
  35. Toregas, C., Swain, R., ReVelle, C., Bergman, L., 1971. The Location of Emergency Service Facilities. Operations Research 19, 1363-1373.
  36. Uyeno, D. H., Seeberg, C., 1984. A practical methodology for ambulance location. Simulation 43, 79-87.
  37. Van Buuren, M., van der Mei, R., Aardal, K., Post, H., 2012. Evaluating dynamic dispatch strategies for emergency medical services: TIFAR simulation tool, in: Proceedings of the 2012 Winter Simulation Conference. C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A.M. Uhrmacher, pp. 46:1- 46:11.
  38. Vukmir, R. B., 2006. Survival from prehospital cardiac arrest is critically dependent upon response time. Resuscitation 69, 229-234.
  39. Wang, Y., Luangkesorn, K. L., Shuman, L., 2012. Modeling emergency medical response to a mass casualty incident using agent based simulation. SocioEconomic Planning Sciences 46, 281-290.
  40. Wears, R. L., Winton, C. N., 1993. Simulation modeling of prehospital trauma care, in: Proceedings of the 1993 Winter Simulation Conference. G.W. Evans, M. Mollaghasemi, E.C. Russel, W.E. Biles.
  41. White, R., Asplin, B., Bugliosi, T., Hankins, D., 1996. High Discharge Survival Rate After Out-of-Hospital Ventricular Fibrillation With Rapid Defibrillation by Police and Paramedics. Annals of Emergency Medicine 28, 480-485.
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Paper Citation


in Harvard Style

Aboueljinane L., Sahin E. and Jemai Z. (2013). Emergency Ambulance Deployment in Val-de-Marne Department - A Simulation-based Iterative Approach . In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: HA, (SIMULTECH 2013) ISBN 978-989-8565-69-3, pages 565-576. DOI: 10.5220/0004623105650576


in Bibtex Style

@conference{ha13,
author={Lina Aboueljinane and Evren Sahin and Zied Jemai},
title={Emergency Ambulance Deployment in Val-de-Marne Department - A Simulation-based Iterative Approach},
booktitle={Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: HA, (SIMULTECH 2013)},
year={2013},
pages={565-576},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004623105650576},
isbn={978-989-8565-69-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: HA, (SIMULTECH 2013)
TI - Emergency Ambulance Deployment in Val-de-Marne Department - A Simulation-based Iterative Approach
SN - 978-989-8565-69-3
AU - Aboueljinane L.
AU - Sahin E.
AU - Jemai Z.
PY - 2013
SP - 565
EP - 576
DO - 10.5220/0004623105650576