Health Care Management Science

, Volume 15, Issue 3, pp 254–269 | Cite as

Resource planning for ambulance services in mass casualty incidents: a DES-based policy model

  • Marion S. Rauner
  • Michaela M. Schaffhauser-Linzatti
  • Helmut Niessner


Due to an increasing number of mass casualty incidents, which are generally complex and unique in nature, we suggest that decision makers consider operations research-based policy models to help prepare emergency staff for improved planning and scheduling at the emergency site. We thus develop a discrete-event simulation policy model, which is currently being applied by disaster-responsive ambulance services in Austria. By evaluating realistic scenarios, our policy model is shown to enhance the scheduling and outcomes at operative and online levels. The proposed scenarios range from small, simple, and urban to rather large, complex, remote mass casualty emergencies. Furthermore, the organization of an advanced medical post can be improved on a strategic level to increase rescue quality, including enhanced survival of injured victims. In particular, we consider a realistic mass casualty incident at a brewery relative to other exemplary disasters. Based on a variety of such situations, we derive general policy implications at both the macro (e.g., strategic rescue policy) and micro (e.g., operative and online scheduling strategies at the emergency site) levels.


Discrete event simulation Decision support system Disaster planning for ambulance services Management games 



We thank a large number of bachelors, masters, and PhD students, mainly at the University of Vienna, as well as practitioners, staff of the Red Cross and the Samaritan Organization, and members of other organizations, all of whom played our disaster policy game, and contributed significantly to the ongoing improvement of the underlying model. Special thanks go to Natasa Peric and Teresa Herdlicka, masters students at the University of Vienna who set up a large experiment designed to investigate the simulation game. They recruited, and tutored, 96 players under the co-supervision of Prof. Dr. Ulrike Leopold-Wildburger from the University of Graz, an Austrian expert on experimental games. The authors also wish to express their gratitude to the editors of HCMS, and three anonymous referees, for their most valuable comments and pertinent suggestions that led to improvements in both the presentation and content of this paper. Special thanks are due to Barnett R. Parker who helped us with a professional edit of the manuscript.


  1. 1.
    Aaby K, Herrmann JW, Jordan CS, Treadwell M, Wood K (2006) Montgomery county’s public health service uses operations research to plan emergency mass dispensing and vaccination clinics. Interfaces 36(6):569–579CrossRefGoogle Scholar
  2. 2.
    Adams HA (2006) Patientenversorgung im Katastrophenfall. Der Unfallchirurg 109(7):583–586CrossRefGoogle Scholar
  3. 3.
    Altay N, Green WG (2006) OR/MS research in disaster operations management. Eur J Oper Res 175(2):475–93CrossRefGoogle Scholar
  4. 4.
    Andersson T, Vaerbrand P (2007) Decision support tools for ambulance dispatch and relocation. J Oper Res Soc 58:195–201Google Scholar
  5. 5.
    Arnold JL (1999) International emergency medicine and the recent development of emergency medicine worldwide. Ann Emerg Med 33(1):97–103CrossRefGoogle Scholar
  6. 6.
    Auf der Heide E (2006) The importance of evidence-based disaster planning. Ann Emerg Med 47(1):34–49CrossRefGoogle Scholar
  7. 7.
    Batta R, Mannur NR (1990) Covering-location models for emergency situations that require multiple response units. Manag Sci 36(1):16–23CrossRefGoogle Scholar
  8. 8.
    Berman O, Gavious A (2007) Location of terror response facilities: A game between state and terrorist. Eur J Oper Res 177(2):1113–1133CrossRefGoogle Scholar
  9. 9.
    Brailsford SC, Gutjahr WJ, Rauner MS, Zeppelzauer W (2007) Combined discrete-event simulation and ant colony optimisation approach for selecting optimal screening policies for diabetic retinopathy. Comput Manag Sci 4(1):59–83CrossRefGoogle Scholar
  10. 10.
    Brennan A, Chick S, Davies R (2006) A taxonomy of model structures for economic evaluation of health technologies. Heal Econ 15(12):1295–1310CrossRefGoogle Scholar
  11. 11.
    Cagnan Z, Davidson RA (2007) Discrete event simulation of the post-earquake restoration process for electric power systems. Int J Risk Assess Manag 7(8):1138–1156CrossRefGoogle Scholar
  12. 12.
    Campbell AM, Jones PC (2011) Prepositioning supplies in preparation for disasters. Eur J Oper Res 209:156–165CrossRefGoogle Scholar
  13. 13.
    Center for Research on the Epidemiology of Disasters (CRED) (2011) EM-DAT: The OFDA/CRED International Disaster Database. Université Catholique de Louvain. Brussels. Accessed 5 April 2011
  14. 14.
    Chandes J, Paché G (2010) Investigating humanitarian logistics issues: from operations management to strategic action. J Manuf Technol Manag 21(3):320–340CrossRefGoogle Scholar
  15. 15.
    Channouf N, L’Ecuyer P, Ingolfsson A, Avramidis AN (2007) The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta. Health Care Manag Sci 10(1):25–45CrossRefGoogle Scholar
  16. 16.
    Chen X, Zhan F (2008) Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies. J Oper Res Soc 59(1):25–33CrossRefGoogle Scholar
  17. 17.
    Chen YW, Wang CH, Lin SJ (2008) A multi-objective geographic information system for route selection of nuclear waste transport. Omega 36(3):363–372CrossRefGoogle Scholar
  18. 18.
    Cooper K, Brailsford SC, Davies R (2007) Choice for modelling technique for evaluating health care interventions. J Oper Res Soc 58(2):168–176Google Scholar
  19. 19.
    Davies R, Davies HT (1994) Modelling patient flows and resource provision in health systems. Omega 22(2):123–131CrossRefGoogle Scholar
  20. 20.
    Dimopoulou M, Giannikos I (2004) Towards an integrated framework for forest fire control. Eur J Oper Res 152(2):476–486CrossRefGoogle Scholar
  21. 21.
    Dodo A, Davidson R, Xu N, Nozick L (2007) Application of regional earthquake mitigation optimization. Comput Oper Res 34(8):2478–2494CrossRefGoogle Scholar
  22. 22.
    Doerner K, Gutjahr W, Nolz P (2008) Multi-criteria location planning for public facilities in tsunami-prone coastal areas. OR Spectr 31:651–678Google Scholar
  23. 23.
    Doerner KF, Gutjahr WJ, Van Wassenhove L (2011) Special issue on optimization in disaster relief. OR Spectr 33(3):445–449CrossRefGoogle Scholar
  24. 24.
    Dykstra EH (1997) International models for the practice of emergency care. Am J Emerg Med 15:208–209CrossRefGoogle Scholar
  25. 25.
    Fiedrich F, Gehbauer F, Rickers U (2000) Optimized resource allocation for emergency response after earthquake disasters. Saf Sci 35(1–3):41–57CrossRefGoogle Scholar
  26. 26.
    Gansterer A (2008) Disaster management and its economic implications. Master Thesis, University of Vienna, AustriaGoogle Scholar
  27. 27.
    Genzmer H, Schuetz C, Kershner S (2009) Die groessten Katastrophen. Parragon, KoelnGoogle Scholar
  28. 28.
    Gong Q, Batta R (2007) Allocation and reallocation of ambulances to casualty clusters in a disaster relief operation. IIE Trans 39(1):27–39CrossRefGoogle Scholar
  29. 29.
    Gonzales RA (2010) Developing a multi-agent system of a crisis response organization. Bus Process Manag J 16(5):847–870CrossRefGoogle Scholar
  30. 30.
    Hansak P, Petutschnigg B, Boebel M, Huendorf HP, Lipp R, Veith J (2003) LPN-San Oesterreich - Lehrbuch für Rettungssanitaeter, Betriebssanitaeter und Bundesheersanitaeter. Stumpf & Kossendey, EdewechtGoogle Scholar
  31. 31.
    Horsman J, Furlong W, Feeny D, Torrance G (2003) The Health Utilities Index (HUI): concepts, measurement properties and applications. Health Qual Life Outcome 1:54CrossRefGoogle Scholar
  32. 32.
    Hupert N, Hollingsworth E, Xiong W (2007) Is overtriage associated with increased mortality? Insights from a simulation model of mass casualty trauma care. Disaster Med Public Health Prep 1(1):14–24CrossRefGoogle Scholar
  33. 33.
    Ingolfsson A, Budge S, Erkut E (2008) Optimal ambulance location with random delays and travel times. Health Care Manag Sci 11:262–274CrossRefGoogle Scholar
  34. 34.
    Jacobson S, McLay L, Virta J, Kobza J (2005) Integer programming models for deployment of airport baggage screening security devices. Optim Eng 6(3):339–359CrossRefGoogle Scholar
  35. 35.
    Kara B, Verter V (2004) Designing a road network for hazardous materials transportation. Transp Sci 38(2):188–196CrossRefGoogle Scholar
  36. 36.
    Koehler G, Foley D, Jones M (1992) A computer simulation of a California casualty collection point used to respond to a major earthquake. Prehospital Disaster Medicine 7(4):339–347Google Scholar
  37. 37.
    Kovács G, Spens KM (2007) Humanitarian logistics in disaster relief operations. Int J Phys Distrib Logist Manag 37(2):99–114CrossRefGoogle Scholar
  38. 38.
    Kovács G, Spens KM (2011) Trends and developments in humanitarian logistics - a gap analysis. Int J Phys Distrib Logist Manag 41(1):32–45CrossRefGoogle Scholar
  39. 39.
    Laurent JF, Richter F, Michel A (1999) Management of victims of urban chemical attack: the French approach. Resuscitation 42(2):141–149CrossRefGoogle Scholar
  40. 40.
    Law AM, Kelton WD (1991) Simulation modeling and analysis. Mc Graw-Hill, New YorkGoogle Scholar
  41. 41.
    Lee EK, Maheshwary S, Mason J, Glisson W (2006) Decision support system for mass dispensing of medications for infectious outbreaks and bioterrorist attacks. Ann Oper Res 148(1):25–53CrossRefGoogle Scholar
  42. 42.
    Lewis RJ (2007) Modeling complex systems: gaining valid insights and avoiding mathematical delusions. Acad Emerg Med 14(9):795–598Google Scholar
  43. 43.
    Li X, Zhao Z, Zhu X, Wyatt T (2011) Covering models and optimization techniques for emergency response facility location and planning: A review. Math Method Oper Res 74(3):281–310CrossRefGoogle Scholar
  44. 44.
    MacLellan J, Martell D (1996) Basing airtankers for forest fire control in Ontario. Oper Res 44(5):677–686CrossRefGoogle Scholar
  45. 45.
    Mentges D, Kirschenlohr R, Adamek H, Boldt J, Riemann JF (1997) Der rettungsdienstliche Ablauf bei Grossschadensereignissen - Eine Untersuchung von 21 Faellen. Anaesthesist 46(2):114–120CrossRefGoogle Scholar
  46. 46.
    Miller HE, Engemann K (2008) A Monte Carlo simulation model of supply chain risk due to natural disasters. Int J Tech Pol Manag 8(4):460–480CrossRefGoogle Scholar
  47. 47.
    Miller G, Randolph S, Patterson J (2006) Responding to bioterrorist smallpox in San Antonio. Interfaces 36(6):580–590CrossRefGoogle Scholar
  48. 48.
    Natarajarathinam M, Capar I, Narayanan A (2009) Managing supply chains in times of crisis: a review of literature and insights. Int J Phys Distrib Logist Manag 39(7):535–573CrossRefGoogle Scholar
  49. 49.
    Niessner H (2010) Der Rettungsdienst bei einem Massenanfall von Verletzten – ein Simulationsmodell in AnyLogic. Master Thesis, University of Vienna, Austria.Google Scholar
  50. 50.
    Noel Bryson K-M, Millar H, Joseph A, Mobolurin A (2002) Using formal MS/OR modeling to support disaster recovery planning. Eur J Oper Res 141(3):679–688CrossRefGoogle Scholar
  51. 51.
    Oesterreichisches Rotes Kreuz (2007) Rahmenvorschrift Grossunfaelle. Accessed 16 December 2009
  52. 52.
    Paton D, Jackson D (2002) Developing disaster management capability: an assessment center approach. Disast Prev Manag 11(2):115–122CrossRefGoogle Scholar
  53. 53.
    Peleg K, Pliskin J (2004) A geographic information system simulation model of EMS: reducing ambulance response time. Am J Emerg Med 22(3):164–70CrossRefGoogle Scholar
  54. 54.
    Pettit SJ, Beresford AKC (2005) Emergency relief logistics: an evaluation of military, non-military, and composite response models. Int J Logist 8(4):313–331CrossRefGoogle Scholar
  55. 55.
    Pfeiler H (2009) Befragung zu Ablaeufen in einer SanHist, commander group 902, Samaritan Organization. Interview on 27 November 2009Google Scholar
  56. 56.
    Pramendorfer W (2009) Medical aspects of an advanced medical post. medical doctor. Samaritan Organization. Interview on 31 October 2009Google Scholar
  57. 57.
    Rabin R, de Charro F (2001) EQ-5D: a measure of health status from the EuroQol group. Ann Med 33(5):337–43CrossRefGoogle Scholar
  58. 58.
    Rauner MS, Gutjahr W, Heidenberger K, Wagner J, Pasia J (2010) Dynamic policy modeling for chronic diseases: metaheuristic- based identification of pareto-optimal screening strategies. Oper Res 58(5):1269–1286CrossRefGoogle Scholar
  59. 59.
    Rauner MS, Schaffhauser-Linzatti MM, Niessner H (2011) Ein Planspiel fuer Großschadenseinsaetze - Ein simulationsbasiertes Managementplanspiel unterstuetzt den effektiven und effizienten Einsatz von Rettungsdiensten. Das oesterreichische Gesundheitswesen—Oesterreichische Krankenhauszeitschrift 52(7):8–11Google Scholar
  60. 60.
    Riener (2007) Die Aenderungen beim Patientenleitsystem. Samaritan OrganizationGoogle Scholar
  61. 61.
    Rotes Kreuz Niederoesterreich (2005) Durchfuehrungsbestimmungen für Grossunfaelle. Accessed 16 December 2009
  62. 62.
    Shaluf IM, Ahmadun Fakharu’l-razi, Aini MS (2003) A review of disaster and crisis. Disast Prev Manag 12(1):24–32CrossRefGoogle Scholar
  63. 63.
    Shuman LJ, Wolfe H, Gunter MJ (1992) RURALSIM: the design and implementation of a rural EMS simulator. J Soc Health Syst 3(3):54–71Google Scholar
  64. 64.
    Simpson NC, Hancock PG (2009) Fifty years of operational research and emergency response. Eur J Oper Res Soc 60:126–139CrossRefGoogle Scholar
  65. 65.
    Smith SW, Portelli I, Narzisi G, Nelson LS, Menges F, Rekow ED, Mincer JS, Mishra B, Goldfrank LR (2009) A novel approach to multihazard modeling and simulation. Disaster Med Public Health Prep 3(2):75–87CrossRefGoogle Scholar
  66. 66.
    Statistik Austria (2010) Statistik Austria – Statistiken. Accessed 15 December 2011
  67. 67.
    Steger M, Fuchs W, Stumpf D (2009) Organizational aspects of an advanced medical post. commanders of group 915. Samaritan Organization. Interview on 28 October 2009Google Scholar
  68. 68.
    Tamura H, Yamamoto K, Tomiyama S, Hatono I (2000) Modeling and analysis of decision making problems for mitigating natural disaster risks. Eur J Oper Res 122:461–468CrossRefGoogle Scholar
  69. 69.
    Tsai C-H, Chen C-W, Chiang W-L, Lin M-L (2008) Application of geographic information system to the allocation of disaster shelters via fuzzy models. Intl J Comp-Aided Engrg & Software 25(1):86–100CrossRefGoogle Scholar
  70. 70.
    Valdmanis V, Bernet P, Moises J (2011) Hospital capacity, capability, and emergency preparedness. Eur J Oper Res 207:1628–1634CrossRefGoogle Scholar
  71. 71.
    World Bank (2011) The recentearthquake and tsunami in Japan: implications for East Asia, world bank East asia and pacific economic update 2011, vol. 1,
  72. 72.
    XJ Technologies (2008) AnyLogic Help. Help system for the computer program AnyLogic/EAP_Update_ March2011_japan.pdf, Accessed 5 April 2011Google Scholar
  73. 73.
    Yi W, Oezdamar L (2007) A dynamic logistics coordination model for evacuation and support in disaster response activities. Eur J Oper Res 179(3):1177–1193CrossRefGoogle Scholar
  74. 74.
    Ziegenfuss T (2007) Notfallmedizin. Springer Medizin Verlag, HeidelbergGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Marion S. Rauner
    • 1
  • Michaela M. Schaffhauser-Linzatti
    • 2
  • Helmut Niessner
    • 3
  1. 1.Department of Innovation and Technology ManagementUniversity of ViennaViennaAustria
  2. 2.Department of External AccountingUniversity of ViennaViennaAustria
  3. 3.ViennaAustria

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