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Resource Planning in Disaster Response

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  • Guido Schryen
  • Gerhard Rauchecker
  • Tina Comes

Abstract

Managing the response to natural, man-made, and technical disasters is becoming increasingly important in the light of climate change, globalization, urbanization, and growing conflicts. Sudden onset disasters are typically characterized by high stakes, time pressure, and uncertain, conflicting or lacking information. Since the planning and management of response is a complex task, decision makers of aid organizations can thus benefit from decision support methods and tools. A key task is the joint allocation of rescue units and the scheduling of incidents under different conditions of collaboration. The authors present an approach to support decision makers who coordinate response units by (a) suggesting mathematical formulations of decision models, (b) providing heuristic solution procedures, and (c) evaluating the heuristics against both current best practice behavior and optimal solutions. The computational experiments show that, for the generated problem instances, (1) current best practice behavior can be improved substantially by our heuristics, (2) the gap between heuristic and optimal solutions is very narrow for instances without collaboration, and (3) the described heuristics are capable of providing solutions for all generated instances in less than a second on a state-of-the-art PC. Copyright Springer Fachmedien Wiesbaden 2015

Suggested Citation

  • Guido Schryen & Gerhard Rauchecker & Tina Comes, 2015. "Resource Planning in Disaster Response," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 243-259, August.
  • Handle: RePEc:spr:binfse:v:57:y:2015:i:4:p:243-259
    DOI: 10.1007/s12599-015-0381-5
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Martijn Warnier & Vincent Alkema & Tina Comes & Bartel Walle, 2020. "Humanitarian access, interrupted: dynamic near real-time network analytics and mapping for reaching communities in disaster-affected countries," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 815-834, September.
    2. Davood Shiri & Vahid Akbari & F. Sibel Salman, 2020. "Online routing and scheduling of search-and-rescue teams," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 755-784, September.
    3. Qinxiao Yu & Yossiri Adulyasak & Louis-Martin Rousseau & Ning Zhu & Shoufeng Ma, 2022. "Team Orienteering with Time-Varying Profit," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 262-280, January.
    4. Sperling, Martina & Schryen, Guido, 2022. "Decision support for disaster relief: Coordinating spontaneous volunteers," European Journal of Operational Research, Elsevier, vol. 299(2), pages 690-705.
    5. Ghazaleh Ahmadi & Reza Tavakkoli-Moghaddam & Armand Baboli & Mehdi Najafi, 2022. "A decision support model for robust allocation and routing of search and rescue resources after earthquake: a case study," Operational Research, Springer, vol. 22(2), pages 1039-1081, April.
    6. Rauchecker, Gerhard & Schryen, Guido, 2019. "An exact branch-and-price algorithm for scheduling rescue units during disaster response," European Journal of Operational Research, Elsevier, vol. 272(1), pages 352-363.
    7. Zhengcai Cao & Lijie Zhou & Biao Hu & Chengran Lin, 2019. "An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 299-309, June.
    8. Jia, Chuanzhou & Zhang, Chi & Li, Yan-Fu & Li, Quan-Lin, 2023. "Joint pre- and post-disaster planning to enhance the resilience of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

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