Advertisement

Designing modular capacitated emergency medical service using information on ambulance trip

  • Sondes HammamiEmail author
  • Aida Jebali
Original Paper

Abstract

In this paper, we investigate the design of a two-tiered emergency medical service (EMS) system. The objective remains in determining the location and the capacity of modular ambulance stations that minimize the EMS system’s cost while respecting a pre-specified response time. A novel approach considering advanced information on ambulance trip and accounting for ambulance busy fractions is proposed. This approach is compared to its counterpart traditional approach that does not consider ambulance trip. Two mixed integer linear programs are developed. Experimentation of the two models was conducted on a real life case study. The obtained results pointed out the usefulness and superiority of the proposed approach. A cost saving of 3% is achieved in addition to the reduction in ambulance round-trip time.

Keywords

Two-tiered EMS Location allocation problem Ambulance trip information Modular capacity Busy fraction Mixed integer linear programming 

Mathematics Subject Classification

90C11 Mixed integer programming 

Notes

References

  1. Beraldi P, Bruni ME (2009) A probabilistic model applied to emergency service vehicle location. Eur J Oper Res 196:323–331Google Scholar
  2. Beraldi P, Bruni ME, Conforti D (2004) Designing robust emergency medical service via stochastic programming. Eur J Oper Res 158:183–193Google Scholar
  3. Basar A, Catay B, Ünlüyurt T (2011) A multi-period double coverage approach for locating the emergency medical service stations in Istanbul. J Oper Res Soc 62(4):627–637Google Scholar
  4. Boujemaa R, Hammami S, Jebali A (2013) A stochastic programming model for ambulance location allocation problem in the Tunisian context. In: International conference on industrial engineering and systems management, IESM’2013, October 28–30, RABAT—MOROCCOGoogle Scholar
  5. Boujemaa R, Jebali A, Hammami S, Ruiz A, Bouchriha H (2018) A stochastic approach for designing two-tiered emergency medical service systems. Flex Serv Manuf J 30(1–2):123–152Google Scholar
  6. Church RL, ReVelle CS (1974) The maximal covering location problem. Pap Reg Sci Assoc 32:101–118Google Scholar
  7. Correia I, Captivo ME (2003) A Lagrangean heuristic for a modular capacitated location problem. Ann Oper Res 122(1):141–161Google Scholar
  8. Daskin MS (1982) Application of an expected covering model to emergency medical service system design. Decis Sci 13:416–439Google Scholar
  9. Daskin MS (1983) A maximum expected location problem: formulation, properties and heuristic solution. Transp Sci 17:416–439Google Scholar
  10. Daskin MS, Stern EH (1981) A hierarchical objective set covering model for emergency medical service vehicle deployment. Transp Sci 15:137–152Google Scholar
  11. Doerner KF, Gutjahr WJ, Hartl RF, Karall M, Reimann M (2005) Heuristic solution of an extended double-coverage ambulance location problem for Austria. Central Eur J Oper Res 13(4):325–340Google Scholar
  12. Gendreau M, Laporte G, Semet F (1997) Solving an ambulance location model by tabu search. Locat Sci 5:75–88Google Scholar
  13. Hogan K, ReVelle CS (1986) Concepts and application of backup coverage. Manag Sci 34:1434–1444Google Scholar
  14. Ingolfsson A, Erkut E, Buge S (2003) Simulating a single start station for edmonton EMS. J Oper Res Soc 54:736–746Google Scholar
  15. Ingolfsson A, Budge S, Erkut E (2008) Optimal ambulance location with random delays and travel times. Health Care Manag Sci 11:262–274Google Scholar
  16. Jebali A, Hammami S, Boujemaa R (2012) A mathematical model for ambulance location-allocation problem in the Tunisian context. In: International conference on computer related knowledge, ICCRK 2012, 5-7 July 2012, Sousse, TUNISIEGoogle Scholar
  17. Larson RC (1974) A hypercube queueing model for facility location and redistricting in urban emergency service. Comput Oper Res 1:67–95Google Scholar
  18. López B, Innocenti B, Busquets D (2008) A multiagent system for coordinating ambulances for emergency medical services. IEEE Intell Syst 23(5):50–57Google Scholar
  19. Maleki M, Majlesinasab N, Sepehri MM (2014) Two new models for redeployment of ambulances. Comput Ind Eng 78:271–284Google Scholar
  20. Mandell MB (1998) Covering models for two-tiered emergency medical services system. Locat Sci 6:355–368Google Scholar
  21. Marianov V, ReVelle CS (1992) A probabilistic fire-protection sitting model with joint vehicle reliability requirements. Pap Reg Sci 71:217–241Google Scholar
  22. McLay LA (2009) A maximum expected covering location model with two types of servers. IIE Trans 41(8):730–741Google Scholar
  23. Noyan N (2010) Alternate risk measures for emergency medical service system design. Ann Oper Res 181:559–589Google Scholar
  24. Revelle C, Hogan K (1988) A reliability-constrained siting model with local estimates of busy fractions. Environ Plann B: Plann Des 15:143–152Google Scholar
  25. Patel AB, Waters NM, Blanchard IE, Doig CJ, Ghali WA (2012) A validation of ground ambulance prehospital times modeled using geographic information systems. Int J Health Geographics 11(1):42Google Scholar
  26. Schilling DA, Elzinga DJ, Cohon J, Church RL, ReVelle CS (1979) The TEAM/FLEET models for simultaneous facility and equipment setting. Transp Sci 13:163–175Google Scholar
  27. Silva PMS, Pinto LR (2010) Emergency medical systems analysis by simulation and optimization. In: Simulation conference (wsc), proceedings of the 2010 Winter, pp 2422–2432.  https://doi.org/10.1109/wsc.2010.5678938
  28. Silva F, Serra D (2008) Locating emergency services with different priorities: the priority queuing covering location problem. J Oper Res Soc 59:1229–1238Google Scholar
  29. Sonoda T, Ishibai K (2015) Project on Information-support solution in emergency medical system. Fujitsu Sci Technol J 51(3):39–49Google Scholar
  30. Su S, Shih CL (2003) Modeling an emergency medical services system using computer simulation. Int J Med Inform 72:57–72Google Scholar
  31. Sudtachat (2014) Strategies to improve the efficiency of emergency medical service (EMS) systems under more realistic conditions. Doctorat Thesis, Clemson UniversityGoogle Scholar
  32. Schmid V (2012) Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. Eur J Oper Res 16(3):611–621Google Scholar
  33. Sudlow A, McConnell N, Egan G, Jansen JO (2011) Destination healthcare facility of patients with suspected traumatic brain injury in Scotland: Analysis of pre-hospital data. Injury 44(9):1237–1240Google Scholar
  34. Takeda RA, Widmer JA, Morabito R (2007) Analysis of ambulance decentralization in urban emergency medical service using the hypercube queueing model. Comput Oper Res 34(3):727–741Google Scholar
  35. Tien JM, EL-Tell K, Simons GR (1983) Improved formulations to the hierarchical health facility location-allocation problem. IEEE Trans Syst Man Cybern 13(6):1128–1132Google Scholar
  36. Toregas C, Swain R, ReVelle CS, Bergman L (1971) The location of emergency service facilities. Oper Res 19:1363–1373Google Scholar
  37. Van Essen JT, Hurink JL, Nickel S, Reuter M (2014) Models for ambulance planning on the strategic and the tactical level. Beta PublishingGoogle Scholar
  38. Yin P, Mu L (2012) Modular capacitated maximal covering location problem for the optimal siting of emergency vehicles. Appl Geogr 34:247–254Google Scholar
  39. Zhang Z, Jiang H (2014) A robust counterpart approach to the bi-objective medical service design problem. Appl Math Model 38:1033–1040Google Scholar
  40. Zuidhof GM (2010) Capacity planning of ambulance services: statistical analysis, forecasting and staffing. Dissertation, Amsterdam UniversityGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.LR-11-ES20 Laboratoire d’Analyse Conception et Commande des Systèmes, Ecole Nationale d’Ingénieurs de TunisUniversité de Tunis El ManarTunisTunisia
  2. 2.Ecole Nationale d’Ingénieurs de Carthage ENICarthageUniversité de CarthageTunisTunisia
  3. 3.Département Ingénierie des SystèmesUniversité Paris-Est, ESIEE ParisNoisy le Grand CedexFrance

Personalised recommendations