Skip to main content
SpringerLink
Log in

Simulation and Optimization of the Pre-hospital Care System of the National University of Mexico

  • Chapter
  • First Online:
Applied Simulation and Optimization

  • 1402 Accesses

Abstract

This chapter presents two operational research techniques, simulation and integer programming, that we used to find a better ambulance location solution and shorten ambulance response time in the main campus, (Ciudad Universitaria) of the Universidad Nacional Autónoma de México, México City. Toregas’ integer programming model, known as the maximal covering model, is best suited for an approach to the needs of the problem; despite its age, it has proven to be a simple and efficient model. For this job, we not only employed the location model, but also linked it to a simulation model whose function was to identify the stochastic demand and analyze the results of the model so as to find the best possible solution, within the limits set when creating different scenarios; and, at the same time shorten the response time.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Baker et al. [5].

  2. 2.

    http://www.itl.nist.gov/div898/handbook/eda/section3/eda35e.htm.

  3. 3.

    http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm.

  4. 4.

    http://www.simio.com/index.php.

  5. 5.

    http://www.lindo.com/.

  6. 6.

    Pardines Lence [31].

References

  1. Aboueljinane, Lina, Jemai, Zied, Sahin, Evren (2012) Reducing ambulance response time using simulation: the case of Valde-Marne department emergency medical service. Proceedings of the 2012 Winter Simulation Conference, pp. 943–954.

    Google Scholar 

  2. Ambrosino, D. et al., (2009). A heuristic based on multi-exchange techniques for a regional fleet assignment location-routing problem. Computers & Operations Research, 36, 442–460.

    Google Scholar 

  3. Amponsah, S.K., Amoako, Gordon, Darkwah, K.F., Agyeman, E. (2011). Location of ambulance emergency medical service in the Kumasi metropolis, Ghana. African Journal of Mathematics and Computer Science Research Vol. 4(1), pp. 18–26, January, 2011

    Google Scholar 

  4. Aringhieri, Roberto, Carello, Giuliana, Morale, Daniela (2007). Ambulance location through optimization and simulation: the case of Milano urban area. http://air.unimi.it/handle/2434/40782. Accessed 15-02-14.

  5. Baker J. R., Clayton E. R., Taylor B. W. (1989). A Non-Linear Multi-Criteria Programming Approach for Determining County Emergency Medical Service Ambulance Allocations. The Journal of the Operational Research Society, 40(5), 423–432.

    Google Scholar 

  6. Ball, M. O., Lin, F. L. (1993). A reliability model applied to emergency vehicle location. Operations Research, 41(1), 18–36.

    Google Scholar 

  7. Batta, R., Dolan, J., Krishnamurthy, N. (1989). The Maximal Expected Covering Location Problem Revisited. Transportation Science, 23(4), 277–287.

    Google Scholar 

  8. Brotcorne, L., Laporte, G., Semet, F. (2003). Ambulance location and relocation models. European Journal of Operational Research, 147, 451–463.

    Google Scholar 

  9. Chakravari, Laha, Roy. (1967). “Handbook of Methods of Applied Statistics, Volume I”, John Wiley and Sons, pp. 392–394.

    Google Scholar 

  10. Church R. L., ReVelle C. (1974). The maximal covering location problem. Papers in Regional Science, 32(1), 101–118.

    Google Scholar 

  11. Daskin M. S., Stern E. H. (1981). A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 15(2), 137–152.

    Google Scholar 

  12. Daskin, M. S. (1983). A maximum expected covering location model Formulation, properties and heuristic solution. Transportation Science, 17 (1), 48–70.

    Google Scholar 

  13. Daskin Mark S. (1995). Network and Discrete Location, models, algorithms and applications, John Wiley & Sons, pp. 92–125, 198, 208.

    Google Scholar 

  14. Daskin, M.S. (2008). What should know about location modeling. Naval research logistics. Wiley InterScience. 283–294. doi:10.1002/nav.

  15. Flores de la Mota, Idalia, Mayra Elizondo Cortés. (2006) “Apuntes de Simulación”. México, Universidad Nacional Autónoma de México, Facultad de Ingeniería.

    Google Scholar 

  16. Fraga-Sastrías, Juan Manuel, (2010). “Sistemas médicos de emergencia en México, una perspectiva pre hospitalaria” Archivos de Medicina de urgencia en México, Enero - April 2010.

    Google Scholar 

  17. Gendreau, M., Laporte, G., Semet, F. (1997). Solving an ambulance location model by tabu search. Location Science, 5 (2), 75–88.

    Google Scholar 

  18. Gendreau, M., Laporte, G., Semet, F. (2001). A dynamic model and parallel tabu search heuristic for real-time ambulance relocation. Parallel Computing, 27, 1641–1653.

    Google Scholar 

  19. Goldberg, J., Dietrich, R., Chen, J. M., Mitwasi, G., Valenzuela, T., Criss, E. (1990). Validating and applying a model for locating emergency medical vehicles in Tucson, AZ. European Journal Of Operational Research, 49 (3), 308–324.

    Google Scholar 

  20. Reidar Hagtvedt, Mark Ferguson, Paul Griffin, Gregory Todd Jones, Pinar Keskinocak. (2009). Cooperative strategies to reduce ambulance diversion. Proceedings of the 2009 Winter Simulation Conference. pp. 1861–1874.

    Google Scholar 

  21. Henderson S.G. and Mason A.J. (2004). Ambulance Service Planning: Simulation and Data Visualisation. Operations Research and Health Care International Series in Operations Research & Management Science. 70, 70–102.

    Google Scholar 

  22. Hogan, K., ReVelle, C. (1986). Concepts and applications of backup coverage. Management Science, 32 (11), 1434–1444.

    Google Scholar 

  23. Lee, Taesik, Jang, Hoon, Cho, Soo-Haeng, Turner, John G. (2012). A simulation-based iterative method for a trauma center – air ambulance location problem. Proceedings of the 2012 Winter Simulation Conference. pp. 955–966.

    Google Scholar 

  24. Limpattanasiri, W., Taniguchi, E. (2013). Solving a Maximal Covering Model of Emergency Ambulance Location Problem in Urban Areas by Dynamic Programming Technique. Proceedings of the Eastern Asia Society for Transportation Studies, Vol. 9, 2013.

    Google Scholar 

  25. Mandell, M. (1998). Covering models for two-tiered emergency medical services systems. Location Science, 6(1–4), 355–368.

    Google Scholar 

  26. Marianov, V., ReVelle, C. (1994). The Queuing Probabilistic Location Set Covering Problem and some Extensions. Socio-Economic Planning Sciences, 28(3), 167–178.

    Google Scholar 

  27. Matthew S. Maxwell Shane G. Henderson Huseyin Topaloglu. (2009). Ambulance redeployment: an approximate dynamic programming approach. Proceedings of the 2009 Winter Simulation Conference. pp. 1850–1860.

    Google Scholar 

  28. Morohosi, Hosumi, Furuta, Takehiro (2013). Optimization model and simulation for improving ambulance service system. Operations Research and its Applications in Engineering, Technology and Management 2013 (ISORA 2013), 23–25 August, 2013.

    Google Scholar 

  29. Morohosi, Hosumi, Furuta, Takehiro (2012). Hypercube simulation analysis for a large-scale ambulance service system. Proceedings of the 2012 Winter Simulation Conference. pp. 1211–1218.

    Google Scholar 

  30. Morohosi, Hozumi (2008), A case study of optimal ambulance location problems. The 7th International Symposium on Operations Research and Its Applications (ISORA’08) Lijiang, China, October 31–Novemver 3, pp. 125–130.

    Google Scholar 

  31. Pardines Lence, Inmaculada; (2007). Técnicas paralelas aplicadas a optimización no lineal en sistemas de memoria distribuida. España 2007, p. 8.

    Google Scholar 

  32. Parra O. O.J. (2011). Revisión del estado del arte en modelos de localización y relocalización de vehículos para atención de emergencias. Revista Elementos 1.

    Google Scholar 

  33. Ramirez-Nafarrate, Adrian, Fowler, John W., Wu, Teresa. (2011). Design of centralized ambulance diversion policies using simulation-optimization. Proceedings of the 2011 Winter Simulation Conference, pp. 1251–1262.

    Google Scholar 

  34. Ramirez-Nafarrate, A. Baykal Hafizoglu, Esma S. Gel, John W. Fowler (2012). Comparison of ambulance diversion policies via simulation. Proceedings of the 2012 Winter Simulation Conference. pp. 967–978, 2012.

    Google Scholar 

  35. Repede, J., Bernardo, J. (1994). Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky. European Journal of Operational Research, 75(3), 567–581.

    Google Scholar 

  36. Restrepo M. (2008). Computational methods for static allocation and real-time redeployment of ambulances. Dissertation Faculty of the Graduate School of Cornell University.

    Google Scholar 

  37. ReVelle, C., Hogan, K. (1989). The Maximum Availability Location Problem. Transportation Science, 23(3), 192–200.

    Google Scholar 

  38. Sasaki S., Comber A., Suzuki H., Brunsdon C. (2010). Using genetic algorithms to optimise current and future health planning - the example of ambulance locations. International Journal of Health Geographics, 9(4), 1–10.

    Google Scholar 

  39. Schilling David, et al. (1979). The team / fleet models for simultaneous facility and equipment siting, Operations Research Society of America, pp. 163–175.

    Google Scholar 

  40. Schimd, Verena, Doerner, Karl F. (2010). Ambulance location and relocation problems with time-dependent travel times, European Journal of Operational Re-search 207 (2010) 1293–1303.

    Google Scholar 

  41. Shuib, Adibah, Zaharudin, Zati Aqmar. (2010). Framework of \({\rm TAZ}\_{\rm OPT}\) Model for Ambulance Location and Allocation Problem. (2010) World Academy of Science, Engineering and Technology. Vol: 48 2010–12-22.

    Google Scholar 

  42. Segura, Esther, Altamirano, Luis, Flores, Idalia. (2010). Simulation and Optimization of The Pre-Hospital Care System of the National University of Mexico Using Travelling Salesman Problem Algorithms. Proceedings of: SummerSim ’10-2010 Summer Simulation Multiconference, Ottawa, ON, Canada, July 11–14.

    Google Scholar 

  43. Stephens, M. A. (1974). EDF Statistics for Goodness of Fit and Some Comparisons, Journal of the American Statistical Association, 69, pp. 730–737.

    Google Scholar 

  44. Toregas, C., Swain, R., ReVelle, C., Bergman, L. (1971). The Location of Emergency Service Facilities. Operations Research, 19(6), 1363–1373.

    Google Scholar 

  45. Weber, A. (1909) Uber den standort der industrien, tubingen english translation, by J.C. Frieerich. Translated as Alfred Weber’s Theory of the location of industries. University of Chicago Press, 1929.

    Google Scholar 

  46. Weng, Mark L. and Houshmand, Ali A. (1999). Healthcare simulation: a case study at a local clinic, Proceedings of the 1999 Winter Simulation Conference, pp. 1577–1584.

    Google Scholar 

  47. Yisong Yue, Lavanya Marla, Ramayya, Krishnan. (2012). An Efficient Simula-tion-based Approach to Ambulance Fleet Allocation and Dynamic Redeployment. http://www.select.cs.cmu.edu/publications/scripts/papers.cgi?Yue-Marla-Krishnan:aaai2012. Accessed 28-02-14.

Download references

Acknowledgments

This research was supported by UNAM-PAPIIT grant IN116012.

Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Idalia Flores De La Mota & Alexander Vindel Garduño

  2. Instituto de Ingeniería, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Esther Segura Pérez

Authors
  1. Idalia Flores De La Mota
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Vindel Garduño
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Esther Segura Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Idalia Flores De La Mota .

Editor information

Editors and Affiliations

  1. Aviation Academy, Amsterdam University of Applied Sciences, Amsterdam, Noord-Holland, The Netherlands

    Miguel Mujica Mota

  2. Facultad de Ingeniería, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Idalia Flores De La Mota

  3. Optimisation Research Group, NICTA, Sydney, New South Wales, Australia

    Daniel Guimarans Serrano

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

De La Mota, I.F., Vindel Garduño, A., Segura Pérez, E. (2015). Simulation and Optimization of the Pre-hospital Care System of the National University of Mexico. In: Mujica Mota, M., De La Mota, I., Guimarans Serrano, D. (eds) Applied Simulation and Optimization. Springer, Cham. https://doi.org/10.1007/978-3-319-15033-8_8

Download citation

Publish with us

Policies and ethics

Search

Navigation