Tractable reformulations of two-stage distributionally robust linear programs over the type-∞ Wasserstein ball

This paper studies a two-stage distributionally robust stochastic linear program under the type-infinity Wasserstein ball by providing sufficient conditions under which the program can be efficiently computed via a tractable convex program. By exploring the properties of binary variables, the developed reformulation techniques are extended to those with mixed binary random parameters. The main tractable reformulations are projected into the original decision space. The complexity analysis demonstrates that these tractable results are tight under the setting of this paper. (C) 2020 Elsevier B.V. All rights reserved.