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Ambulance allocation for maximal survival with heterogeneous outcome measures


  • Knight, V.A.
  • Harper, P.R.
  • Smith, L.


This paper proposes new models for locating emergency medical services (EMS) by incorporating survival functions for capturing multiple-classes of heterogeneous patients. The Maximal Expected Survival Location Model for Heterogeneous Patients (MESLMHP) aims to maximize the overall expected survival probability of multiple-classes of patients, whereby different classes could be defined according to agreed patient categories based on response time targets, or by capturing differing medical conditions each with a corresponding survival function. Furthermore, we propose and demonstrate an approximation approach to solving the extended stochastic version of MESLMHP, which utilizes queuing theory to permit the modeling of congestion and utilization at each ambulance station, and does not require assumptions to be made on the utilization of ambulances. Both models are demonstrated using data from the ambulance service in Wales. We show that our multiple outcome measures and survival-maximizing approach, rather than one based on average response time targets alone or a single patient class provides more effective EMS ambulance allocations.

Suggested Citation

  • Knight, V.A. & Harper, P.R. & Smith, L., 2012. "Ambulance allocation for maximal survival with heterogeneous outcome measures," Omega, Elsevier, vol. 40(6), pages 918-926.
  • Handle: RePEc:eee:jomega:v:40:y:2012:i:6:p:918-926
    DOI: 10.1016/

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    References listed on IDEAS

    1. Brotcorne, Luce & Laporte, Gilbert & Semet, Frederic, 2003. "Ambulance location and relocation models," European Journal of Operational Research, Elsevier, vol. 147(3), pages 451-463, June.
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    6. M S Daskin & A Haghani, 1984. "Multiple vehicle routing and dispatching to an emergency scene," Environment and Planning A, Pion Ltd, London, vol. 16(10), pages 1349-1359, October.
    7. Jan M. Chaiken & Richard C. Larson, 1972. "Methods for Allocating Urban Emergency Units: A Survey," Management Science, INFORMS, vol. 19(4-Part-2), pages 110-130, December.
    8. Francisco Silva & Daniel Serra, 2008. "Locating emergency services with different priorities: The priority queuing covering location problem," Economics Working Papers 1092, Department of Economics and Business, Universitat Pompeu Fabra.
    9. Felder, Stefan & Brinkmann, Henrik, 2002. "Spatial allocation of emergency medical services: minimising the death rate or providing equal access?," Regional Science and Urban Economics, Elsevier, vol. 32(1), pages 27-45, January.
    10. Gwyn Bevan & Richard Hamblin, 2009. "Hitting and missing targets by ambulance services for emergency calls: effects of different systems of performance measurement within the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 161-190.
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    Cited by:

    1. Leknes, Håkon & Aartun, Eirik Skorge & Andersson, Henrik & Christiansen, Marielle & Granberg, Tobias Andersson, 2017. "Strategic ambulance location for heterogeneous regions," European Journal of Operational Research, Elsevier, vol. 260(1), pages 122-133.
    2. TALARICO, Luca & MEISEL, Frank & SÖRENSEN, Kenneth, 2014. "Ambulance routing for disaster response with patient groups," Working Papers 2014005, University of Antwerp, Faculty of Applied Economics.
    3. Almehdawe, Eman & Jewkes, Beth & He, Qi-Ming, 2016. "Analysis and optimization of an ambulance offload delay and allocation problem," Omega, Elsevier, vol. 65(C), pages 148-158.
    4. Sudtachat, Kanchala & Mayorga, Maria E. & Mclay, Laura A., 2016. "A nested-compliance table policy for emergency medical service systems under relocation," Omega, Elsevier, vol. 58(C), pages 154-168.
    5. repec:pal:jorsoc:v:68:y:2017:i:6:d:10.1057_s41274-016-0100-8 is not listed on IDEAS
    6. McCormack, Richard & Coates, Graham, 2015. "A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival," European Journal of Operational Research, Elsevier, vol. 247(1), pages 294-309.
    7. Steiner, Maria Teresinha Arns & Datta, Dilip & Steiner Neto, Pedro José & Scarpin, Cassius Tadeu & Rui Figueira, José, 2015. "Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil," Omega, Elsevier, vol. 52(C), pages 53-64.


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