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Dispatching and routing of emergency vehicles in disaster mitigation using data fusion

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  • Jotshi, Arun
  • Gong, Qiang
  • Batta, Rajan
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    Abstract

    The aim of this paper is to develop a robust methodology for the dispatching and routing of emergency vehicles (EVs) in a post-disaster environment with the support of data fusion. In this work, we consider an earthquake scenario with a large number of casualties needing medical attention. Given an influx of information (regarding casualties, road, traffic conditions, etc.), data are fused to provide estimates of the entities under consideration. We use this information to dispatch and route EVs to casualty pickup locations, followed by delivery to appropriate hospitals. Key factors here include patient priorities, clustering criteria, and distance. Similarly, factors affecting the dispatching of EVs from patient locations to hospitals include waiting time at hospital emergency rooms, hospital capacity, and distance. Routes must be generated for EVs by accounting for real-world road networks, existing road damage, congestion, and related issues. We develop a dispatching and routing simulation model, and utilize a case study to evaluate the performance of our proposed methodology.

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    Bibliographic Info

    Article provided by Elsevier in its journal Socio-Economic Planning Sciences.

    Volume (Year): 43 (2009)
    Issue (Month): 1 (March)
    Pages: 1-24

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    Handle: RePEc:eee:soceps:v:43:y:2009:i:1:p:1-24

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    Web page: http://www.elsevier.com/locate/seps

    Related research

    Keywords: Emergency response services Data fusion dispatching Routing Simulation Large-scale networks;

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    Cited by:
    1. Galindo, Gina & Batta, Rajan, 2013. "Review of recent developments in OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 230(2), pages 201-211.
    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. Lin, Yen-Hung & Batta, Rajan & Rogerson, Peter A. & Blatt, Alan & Flanigan, Marie, 2011. "A logistics model for emergency supply of critical items in the aftermath of a disaster," Socio-Economic Planning Sciences, Elsevier, vol. 45(4), pages 132-145, December.
    4. McLay, Laura A. & Boone, Edward L. & Brooks, J. Paul, 2012. "Analyzing the volume and nature of emergency medical calls during severe weather events using regression methodologies," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 55-66.
    5. Berkoune, Djamel & Renaud, Jacques & Rekik, Monia & Ruiz, Angel, 2012. "Transportation in disaster response operations," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 23-32.
    6. Najafi, Mehdi & Eshghi, Kourosh & Dullaert, Wout, 2013. "A multi-objective robust optimization model for logistics planning in the earthquake response phase," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 217-249.
    7. Wilson, Duncan T. & Hawe, Glenn I. & Coates, Graham & Crouch, Roger S., 2013. "A multi-objective combinatorial model of casualty processing in major incident response," European Journal of Operational Research, Elsevier, vol. 230(3), pages 643-655.
    8. Mingwu Ye & Jun Wang & Jing Huang & Shiyuan Xu & Zhenlou Chen, 2012. "Methodology and its application for community-scale evacuation planning against earthquake disaster," Natural Hazards, International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(3), pages 881-892, April.
    9. Falasca, Mauro & Zobel, Christopher, 2012. "An optimization model for volunteer assignments in humanitarian organizations," Socio-Economic Planning Sciences, Elsevier, vol. 46(4), pages 250-260.
    10. Dean, Matthew D. & Nair, Suresh K., 2014. "Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model," European Journal of Operational Research, Elsevier, vol. 238(1), pages 363-373.

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