Skip to main content
SpringerLink
Log in

Reducing disparities in large-scale emergency medical service systems

  • General Paper
  • Published:
Journal of the Operational Research Society

Abstract

Emergency Medical Service (EMS) systems operate under the pressure of knowing that human lives might be directly at stake. In the public eye there is a natural expectation of efficient response. There is abundant literature on the topic of efficient planning of EMS systems (maximizing expected coverage or minimizing response time). Other objectives have been considered but the literature available is very sparse compared to efficiency-based works. Furthermore, while real size EMS systems have been studied, the use of exact models is usually hindered by the amount of computational time required to obtain solutions. We approach the planning of large-scale EMS systems including fairness considerations using a Tabu Search-based heuristic with an embedded approximation procedure for the queuing submodel. This allows for the analysis of large-scale real systems, extending the approach in which strategic decisions (location) and operative decisions (dispatching) are combined to balance efficiency and fairness.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  • Andersson T and Varbrand P (2006). Decision support tools for ambulance dispatch and relocation. Journal of the Operational Research Society 58 (2): 195–201.

    Article  Google Scholar 

  • Atkinson JB, Kovalenko IN, Kuznetsov N and Mykhalevych KV (2008). A hypercube queueing loss model with customer-dependent service rates. European Journal of Operational Research 191 (1): 223–239.

    Article  Google Scholar 

  • Aytug H and Saydam C (2002). Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study. European Journal of Operational Research 141 (3): 480–494.

    Article  Google Scholar 

  • Bandara D, Mayorga ME and McLay L (2012). Optimal dispatching strategies for emergency vehicles to increase patient survivability. International Journal of Operational Research 15 (2): 195–214.

    Article  Google Scholar 

  • Baptista S and Oliveira R (2012). A case study on the application of an approximated hypercube model to emergency medical systems management. Central European Journal of Operations Research 20 (4): 559–581.

    Article  Google Scholar 

  • Batta R, Dolan JM and Krishnamurthy NN (1989). The maximal expected covering location problem: Revisited. Transportation Science 23 (4): 277–287.

    Article  Google Scholar 

  • Battiti R and Tecchiolli G (1994). The reactive tabu search. ORSA Journal on Computing 6 (2): 126–140.

    Article  Google Scholar 

  • Benveniste R (1985). Solving the combined zoning and location problem for several emergency units. The Journal of the Operational Research Society 36 (5): 433–450.

    Article  Google Scholar 

  • Bertsimas D, Farias VF and Trichakis N (2011). The price of fairness. Operation Research 59 (1): 17–31.

    Article  Google Scholar 

  • Budge S, Ingolfsson A and Erkut E (2009). Approximating vehicle dispatch probabilities for emergency service systems with location-specific service times and multiple units per location. Operations Research 57 (1): 251–255.

    Article  Google Scholar 

  • Carter GM, Chaiken JM and Ignall E (1972). Response areas for two emergency units. Operations Research 20 (3): 571–594.

    Article  Google Scholar 

  • Eiselt HA and Laporte G, University of New Brunswick Faculty of A (1995). Objectives in Location Problems. Faculty of Administration, University of New Brunswick: Fredericton, NB.

    Book  Google Scholar 

  • Erdogan G, Erkut E, Ingolfsson A and Laporte G (2010). Scheduling ambulance crews for maximum coverage. Journal of the Operations Research Society 61 (4): 543–550.

    Article  Google Scholar 

  • Erkut E (1993). Inequality measures for location problems. Location Science 1 (3): 199–217.

    Google Scholar 

  • Erkut E, Ingolfsson A and Erdogan G (2008). Ambulance location for maximum survival. Naval Research Logistics 55 (1): 42–58.

    Article  Google Scholar 

  • Erkut E, Ingolfsson A, Sim T and Erdogan G (2009). Computational comparison of five maximal covering models for locating ambulances. Geographical Analysis 41 (1): 43–65.

    Article  Google Scholar 

  • Farahani RZ, Asgari N, Heidari N, Hosseininia M and Goh M (2012). Covering problems in facility location: A review. Computers and Industrial Engineering 62 (1): 368–407.

    Article  Google Scholar 

  • Felder S and Brinkmann H (2002). Spatial allocation of emergency medical services: Minimising the death rate or providing equal access? Regional Science and Urban Economics 32 (1): 27–45.

    Article  Google Scholar 

  • Galvão RD and Morabito R (2008). Emergency service systems: The use of the hypercube queueing model in the solution of probabilistic location problems. International Transactions in Operational Research 15 (5): 525–549.

    Article  Google Scholar 

  • Galvão RD, Chiyoshi FY and Morabito R (2005). Towards unified formulations and extensions of two classical probabilistic location models. Computers and Operations Research 32 (1): 15–33.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Geroliminis N, Karlaftis MG and Skabardonis A (2009). A spatial queuing model for the emergency vehicle districting and location problem. Transportation Research Part B: Methodological 43 (7): 798–811.

    Article  Google Scholar 

  • Geroliminis N, Kepaptsoglou K and Karlaftis MG (2011). A hybrid hypercube–genetic algorithm approach for deploying many emergency response mobile units in an urban network. European Journal of Operational Research 210 (2): 287–300.

    Article  Google Scholar 

  • Glover F (1986). Future paths for integer programming and links to artificial intelligence. Computers & Operations Research 13 (5): 533–549.

    Article  Google Scholar 

  • Glover F (1989). Tabu search—part I. ORSA Journal on Computing 1 (3): 190–206.

    Article  Google Scholar 

  • Goldberg J and Paz L (1991). Locating emergency vehicle bases when service time depends on call location. Transportation Science 25 (4): 264–280.

    Article  Google Scholar 

  • Hansen P (1986). The steepest ascent mildest descent heuristic for combinatorial programming. Proceedings of the Congress on Numerical Methods in Combinatorial Optimization, Capri, Italy.

  • Harder R (2013). OpenTS—java tabu search package, http://www.coin-or.org/Ots/, accessed 11 December 2013.

  • Iannoni AP and Morabito R (2007). A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways. Transportation Research Part E: Logistics and Transportation Review 43 (6): 755–771.

    Article  Google Scholar 

  • Iannoni AP, Morabito R and Saydam C (2008). A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways. Annals of Operations Research 157 (1): 207–224.

    Article  Google Scholar 

  • Iannoni AP, Morabito R and Saydam C (2011). Optimizing large-scale emergency medical system operations on highways using the hypercube queuing model. Socio-Economic Planning Sciences 45 (3): 105–117.

    Article  Google Scholar 

  • Ingolfsson A, Budge S and Erkut E (2008). Optimal ambulance location with random delays and travel times. Health Care Management Science 11 (3): 262–274.

    Article  Google Scholar 

  • Jarvis JP (1981). Optimal assignments in a Markovian queueing system. Computers and Operations Research 8 (1): 17–23.

    Article  Google Scholar 

  • Jarvis JP (1985). Approximating the equilibrium behavior of multi-server loss systems. Management Science 31 (2): 235–239.

    Article  Google Scholar 

  • Katehakis MN and Levine A (1986). Allocation of distinguishable servers. Computers and Operations Research 13 (1): 85–93.

    Article  Google Scholar 

  • Larson RC (1974). A hypercube queuing model for facility location and redistricting in urban emergency services. Computers and Operations Research 1 (1): 67–95.

    Article  Google Scholar 

  • Larson RC (1975). Approximating the performance of urban emergency service systems. Operations Research 23 (5): 845–868.

    Article  Google Scholar 

  • Leclerc PD, McLay LA and Mayorga ME (2012). Modeling Equity for Allocating Public Resources Community-Based Operations Research. volume 167 of International Series in Operations Research and Management Science, chapter 5, Springer: New York, pp 97–118.

    Book  Google Scholar 

  • Lee S (2011). The role of preparedness in ambulance dispatching. Journal of the Operational Research Society 62 (10): 1888–1897.

    Article  Google Scholar 

  • Li X, Zhao Z, Zhu X and Wyatt T (2011). Covering models and optimization techniques for emergency response facility location and planning: A review. Mathematical Methods of Operations Research 74 (3): 281–310.

    Article  Google Scholar 

  • Marsh MT and Schilling DA (1994). Equity measurement in facility location analysis: A review and framework. European Journal of Operational Research 74 (1): 1–17.

    Article  Google Scholar 

  • Mavrotas G (2009). Effective implementation of the ɛ-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation 213 (2): 455–465.

    Article  Google Scholar 

  • McCallion T (2012). The great ambulance response time debate continues. EMS Insider, February 16, http://www.jems.com/article/ems-insider/great-ambulance-response-time-debate.

  • McLay LA and Mayorga ME (2013). A dispatching model for server-to-customer systems that balances efficiency and equity. Manufacturing and Service Operations Management 15 (2): 205–220.

    Article  Google Scholar 

  • Mendonça FC and Morabito R (2001). Analysing emergency medical service ambulance deployment on a Brazilian highway using the hypercube model. The Journal of the Operational Research Society 52 (3): 261–270.

    Article  Google Scholar 

  • Naoum-Sawaya J and Elhedhli S (2013). A stochastic optimization model for real-time ambulance redeployment. Computers and Operations Research 40 (8): 1972–1978.

    Article  Google Scholar 

  • Ogryczak W (2000). Inequality measures and equitable approaches to location problems. European Journal of Operational Research 122 (2): 374–391.

    Article  Google Scholar 

  • Rajagopalan HK, Saydam C and Xiao J (2008). A multiperiod set covering location model for dynamic redeployment of ambulances. Computers and Operations Research 35 (3): 814–826.

    Article  Google Scholar 

  • Schmid V (2012). Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European Journal of Operational Research 219 (3): 611–621.

    Article  Google Scholar 

  • Stone DA (2002). Policy paradox: The Art of Political Decision Making. Norton: New York.

    Google Scholar 

  • Toregas C, Swain R, ReVelle C and Bergman L (1971). The location of emergency service facilities. Operations Research 19 (6): 1363–1373.

    Article  Google Scholar 

  • Toro-Díaz H, Mayorga ME, Sunarin C and McLay LA (2013). Joint location and dispatching decisions for emergency medical services. Computers and Industrial Engineering 64 (4): 917–928.

    Article  Google Scholar 

  • Zhu Z and McKnew MA (1993). A goal programming workload balancing optimization model for ambulance allocation: An application to Shanghai, P.R. China. Socio-Economic Planning Sciences 27 (2): 137–148.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Clemson University, Clemson, SC, USA

    Hector Toro-Díaz

  2. NC State University, Raleigh, NC, USA

    Maria E Mayorga

  3. University of Wisconsin, Madison, WI, USA

    Laura A McLay

  4. Francis Marion University, Florence, SC, USA

    Hari K Rajagopalan

  5. University of North Carolina, Charlotte, NC, USA

    Cem Saydam

Authors
  1. Hector Toro-Díaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria E Mayorga
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura A McLay
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hari K Rajagopalan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cem Saydam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria E Mayorga.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Toro-Díaz, H., Mayorga, M., McLay, L. et al. Reducing disparities in large-scale emergency medical service systems. J Oper Res Soc 66, 1169–1181 (2015). https://doi.org/10.1057/jors.2014.83

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2014.83

Keywords

Search

Navigation