Data-Driven Adaptive Distributionally Robust Facility Location

We formulate an adaptive distributionally robust facility location problem, where the facility location decisions are affected by diverse scenarios with different demand uncertainty. We learn the ambiguity set for describing the distributional information of demand in a data-driven way. The combination of data and robust stochastic optimization models is expected to yield better solutions in real-world complication.

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