Spatial and objective decompositions for very large SCAPs

Authors:
Carleton Coffrin;Pascal Van Hentenryck;Russell Bent
Affiliations:
Brown University, Providence RI;Brown University, Providence RI;Los Alamos National Laboratory, Los Alamos NM
Venue:
CPAIOR'11 Proceedings of the 8th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Year:
2011

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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.