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Ambulance routing for disaster response with patient groups


  • TALARICO, Luca
  • MEISEL, Frank
  • SÖRENSEN, Kenneth


We consider a routing problem for ambulances in a disaster response scenario, in which a large number of injured people require medical aid at the same time. The ambulances are used to carry medical personnel and patients. We distinguish two groups of patients: slightly injured people who can be assisted directly in the field, and seriously injured people who have to be brought to hospitals. Since ambulances represent a scarce resource in disaster situations, their efficient usage is of the utmost importance. Two mathematical formulations are proposed to obtain route plans that minimize the latest service completion time among the people waiting for help. Since disaster response calls for high-quality solutions within seconds, we also propose a Large Neighborhood Search metaheuristic. This solution approach can be applied at high frequency to cope with the dynamics and uncertainties in a disaster situation. Our experiments show that the metaheuristic produces near optimal solutions for a large number of test instances within very short response time. Hence, it fulfills the criteria for applicability in a disaster situation. Within the experiments, we also analysed the effect of various structural parameters of a problem, like the number of ambulances, hospitals, and the type of patients, on both running time of the heuristic and quality of the solutions.

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  • 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.
  • Handle: RePEc:ant:wpaper:2014005

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    References listed on IDEAS

    1. Yi, Pengfei & George, Santhosh K. & Paul, Jomon Aliyas & Lin, Li, 2010. "Hospital capacity planning for disaster emergency management," Socio-Economic Planning Sciences, Elsevier, vol. 44(3), pages 151-160, September.
    2. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    3. Bertsimas, Dimitris & Van Ryzin, Garrett., 1991. "A stochastic and dynamic vehicle routing problem in the Euclidean plane," Working papers 3286-91., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    4. Huang, Michael & Smilowitz, Karen & Balcik, Burcu, 2012. "Models for relief routing: Equity, efficiency and efficacy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 2-18.
    5. Yi, Wei & Ozdamar, Linet, 2007. "A dynamic logistics coordination model for evacuation and support in disaster response activities," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1177-1193, June.
    6. Schmid, Verena & Doerner, Karl F., 2010. "Ambulance location and relocation problems with time-dependent travel times," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1293-1303, December.
    7. 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.
    8. Jotshi, Arun & Gong, Qiang & Batta, Rajan, 2009. "Dispatching and routing of emergency vehicles in disaster mitigation using data fusion," Socio-Economic Planning Sciences, Elsevier, vol. 43(1), pages 1-24, March.
    9. Ghiani, Gianpaolo & Guerriero, Francesca & Laporte, Gilbert & Musmanno, Roberto, 2003. "Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies," European Journal of Operational Research, Elsevier, vol. 151(1), pages 1-11, November.
    10. Schmid, Verena, 2012. "Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 219(3), pages 611-621.
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    More about this item


    Ambulance routing; Disaster response; Service time; Local search; Large neighborhood search; Metaheuristic;

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