Combining Benders and Dantzig-Wolfe Decompositions for Online Stochastic Combinatorial Optimization
نویسندگان
چکیده
Online resource allocation problems are difficult because the operator must make irrevocable decisions rapidly and with limited (or nonexistent) information on future requests. We propose a mathematical-programming-based framework that takes into account all the available forecasts and the limited computational time. We combine Benders decomposition, which allows us to measure the expected future impact of each decision, and Dantzig-Wolfe decomposition, which can tackle a wide range of combinatorial problems. We illustrate the modeling process and demonstrate the efficiency of this framework on real data sets for two applications: appointment booking and scheduling in a radiotherapy center, and task assignment and routing in a warehouse.
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