Abstract
This paper reconsiders the single commodity allocation problem (SCAP) for disaster recovery, which determines where and how to stockpile a commodity before a disaster and how to route the commodity once the disaster has hit. It shows how to scale the SCAP algorithm proposed in [1] to a geographical area with up to 1,000 storage locations (over a million decision variables). More precisely, the paper shows that spatial and objective decompositions are instrumental in solving SCAP problems at the state scale (e.g., for the state of Florida). The practical benefits of these decompositions are demonstrated on large-scale hurricane disaster scenarios generated by Los Alamos National Laboratory using state-of-the-art disaster simulation tools.
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Coffrin, C., Van Hentenryck, P., Bent, R. (2011). Spatial and Objective Decompositions for Very Large SCAPs. In: Achterberg, T., Beck, J.C. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2011. Lecture Notes in Computer Science, vol 6697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21311-3_8
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DOI: https://doi.org/10.1007/978-3-642-21311-3_8
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