The vehicle routing problem
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
A compact model and tight bounds for a combined location-routing problem
Computers and Operations Research
Transportation Science
A survey on metaheuristics for stochastic combinatorial optimization
Natural Computing: an international journal
Spatial and objective decompositions for very large SCAPs
CPAIOR'11 Proceedings of the 8th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Computational disaster management
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Humanitarian/emergency logistics models: a state of the art overview
Proceedings of the 2013 Summer Computer Simulation Conference
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This paper considers the single commodity allocation problem (SCAP) for disaster recovery, a fundamental problem faced by all populated areas. SCAPs are complex stochastic optimization problems that combine resource allocation, warehouse routing, and parallel fleet routing. Moreover, these problems must be solved under tight runtime constraints to be practical in real-world disaster situations. This paper formalizes the specification of SCAPs and introduces a novel multi-stage hybrid-optimization algorithm that utilizes the strengths of mixed integer programming, constraint programming, and large neighborhood search. The algorithm was validated on hurricane disaster scenarios generated by Los Alamos National Laboratory using state-of-the-art disaster simulation tools and is deployed to aid federal organizations in the US.