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Resource Planning in Disaster Response

Decision Support Models and Methodologies

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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.

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Notes

  1. The situations are based on the description in earlier works but our approaches in this work go beyond these as follows:

    1. 1.

      Wex et al. (2011) model the situation without collaboration by means of a recursive optimization model. As recursion is difficult to solve when using optimizers, we suggest here a non-recursive model.

    2. 2.

      A basic and fuzzy version of our (non-recursive, crisp) model has been suggested in Wex et al. (2012); however, we improve this model by modifying constraints and removing redundant constraints.

    3. 3.

      Wex et al. (2013) model the situation with collaboration. Again, we improve this model by modifying and removing redundant constraints.

    4. 4.

      Wex et al. (2014) draw on Wex et al. (2012) and compare solutions obtained from applying heuristics for the model without collaboration with lower bounds of optimal solutions.

    We would like to stress that, beyond model improvements, our paper goes beyond the cited works not only with regard to model discussion but also with regard to computing optimal solutions.

  2. The average runtime for the instance size 30/10 is about 39 min with distribution set 1 and about 63 min with distribution set 2. Three out of ten instances even required more than 90 min in distribution set 1, and four out of ten instances required more than 85 min in distribution set 2.

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Correspondence to Guido Schryen.

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Accepted after four revisions by Prof. Dr. Suhl.

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Schryen, G., Rauchecker, G. & Comes, T. Resource Planning in Disaster Response. Bus Inf Syst Eng 57, 243–259 (2015). https://doi.org/10.1007/s12599-015-0381-5

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