Skip to main content
Log in

A case study on the application of an approximated hypercube model to emergency medical systems management

  • Original Paper
  • Published:
Central European Journal of Operations Research Aims and scope Submit manuscript

Abstract

This paper describes an application of the approximated hypercube model to Lisbon emergency medical services (EMS) management, namely for assessing alternative dispatching rules for assigning ambulances to emergency calls. The approximated hypercube (A-hypercube) is a queuing theory model that computes several performance measures such as average response time, server workloads or the probability of all servers being busy (loss probability). The assumptions of the extended model are Poisson customer arrivals, general service time (customer and server dependent) and a fixed preference assignment rule of servers to customers. The fact that dispatching rules are precisely a model parameter, turn this model into a valuable tool in the definition of efficient operating rules. In this paper, we propose new expressions for the computation of system performance measures during periods in which the emergency call arrival process is not stationary. Different dispatching rules are evaluated by comparing the system performance measures obtained from the extended A-hypercube model and a simulation model, using data collected from the Lisbon EMS Department.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Baker J, Clayton E, Taylor B (1989) A non-linear multi-criteria programming approach for determining county emergency medical service ambulance allocations. J Oper Res Soc 5: 423–432

    Google Scholar 

  • Baptista S (2007) Avaliação de regras de afectação em tempo real de veículos de emergência médica. Dissertation, Instituto Superior Técnico, Universidade Técnica de Lisboa

  • Benveniste R (1985) Solving the combined zoning and location problem for several emergency units. J Oper Res Soc 36: 433–450

    Google Scholar 

  • Brotcorne L, Laporte G, Semet F (2003) Ambulance location and relocation models. Eur J Oper Res 147: 451–463

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Burwell T, Jarvis J, Mcknew M (1993) Modeling co-located servers and dipatch ties in the hypercube model. Comput Oper Res 20: 113–119

    Article  Google Scholar 

  • Carter G, Chaiken J, Ignall E (1972) Response areas for two emergency units. Oper Res 20: 571–594

    Article  Google Scholar 

  • Chaterjee, Hadi (2006) Regression analysis by example, 4th edn. Wiley, New York

  • Cuninghame-Green R, Harries G (1988) Nearest-neighbour rules for emergency services. Zeitschrift für Operations Research 32: 299–306

    Article  Google Scholar 

  • Fleischer R, Wahl M (2000) On-line scheduling revisited. J Schedul 3: 343–353

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Galvão R, Chiyoshi F, Morabito R (2005) Towards unified formulations and extensions of two classical probabilistic location models. Comput Oper Res 32: 15–33

    Article  Google Scholar 

  • Goldberg J (2004) Operations research models for the deployment of emergency service vehicles. EMS Manag J 1: 20–39

    Google Scholar 

  • Goldberg J, Dietrich R, Chen J, Mitwasi G, Valenzuela T, Criss E (1990) Validating and applying a model for locating emergency medical vehicles in Tucson, Az. Eur J Oper Res 49: 308–324

    Article  Google Scholar 

  • Goldberg J, Paz L (1991) Locating emergency vehicle bases when service time depends on call location. Transport Sci 25: 264–280

    Article  Google Scholar 

  • Goldberg J, Szidarovsky F (1991) Methods for solving nonlinear equations used in evaluating emergency vehicle busy probabilities. Oper Res 39: 903–916

    Article  Google Scholar 

  • Iannnoni A, Morabito R, Saydam C (2008) A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways. Ann Oper Res 157: 207–224

    Article  Google Scholar 

  • Iannnoni A, Morabito R, Saydam C (2009) An optimization approach for ambulance location and the districting of the response segments on highways. Eur J Oper Res 195: 528–542

    Article  Google Scholar 

  • Jarvis J (1985) Approximating the equilibrium behavior of multi-server loss systems. Manag Sci 31: 235–239

    Article  Google Scholar 

  • Larson R (1974) A hypercube queuing model for facility location and redistricting in urban emergency services. Comput Oper Res 1: 67–95

    Article  Google Scholar 

  • Larson R (1975) Approximating the performance of urban emergency service systems. Oper Res 23: 845–868

    Article  Google Scholar 

  • Larson H (1982) Introduction to probability theory and statistical inference, 3rd edn. Wiley, New York

    Google Scholar 

  • Law A, Kelton W (2000) Simulation modeling and analysis. 3rd edn. McGraw Hill,

    Google Scholar 

  • Mclay L (2009) A maximum expected covering location model with two type of servers. IIE Trans 41: 730–741

    Article  Google Scholar 

  • Mendonça F, Morabito R (2001) Analysing emergency medical service ambulance deployment on a Brazilian highway using the hypercube model. J Oper Res Soc 52: 261–270

    Article  Google Scholar 

  • Parzen E (1962) Stochastic processes. SIAM, Philadelphia

    Google Scholar 

  • Rajagopalan HK, Saydam C, Xiao J (2008) A multiperiod set covering location model for dynamic redeployment of ambulances. Comput Oper Res 35: 814–826

    Article  Google Scholar 

  • Repede J, Bernardo J (1994) Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky. Eur J Oper Res 75: 567–581

    Article  Google Scholar 

  • Saydam C, Aytug H (2003) Accurate estimation of expected coverage: revisited. Soc Econ Plan Sci 37: 69–80

    Article  Google Scholar 

  • Singer M, Donoso P (2008) Assessing an ambulance service with queuing theory. Comput Oper Res 35: 2548–2560

    Article  Google Scholar 

  • Takeda R, Widmer J, Morabito R (2007) Analysis of ambulance decentralization in an urban emergency medical service using the hypercube queuing model. Comput Oper Res 34: 727–741

    Article  Google Scholar 

  • Venkateshan P, Mathur K, Ballou R (2010) Locating and staffing service centers under service level constraints. Eur J Oper Res 201: 55–70

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Susana Baptista.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Baptista, S., Oliveira, R.C. A case study on the application of an approximated hypercube model to emergency medical systems management. Cent Eur J Oper Res 20, 559–581 (2012). https://doi.org/10.1007/s10100-010-0187-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10100-010-0187-y

Keywords

Navigation