In the optimization under uncertainty, decision-makers first select a wait-and-see policy before any realization of uncertainty and then place a here-and-now decision after the uncertainty has been observed. Two-stage stochastic programming is a popular modeling paradigm for the optimization under uncertainty that the decision-makers first specifies a probability distribution, and then seek the best decisions to jointly optimize the deterministic wait-and-see and expected here-and-now costs. In practice, such a probability distribution may not be fully available but is probably observable through an empirical dataset. Therefore, this paper studies distributionally robust two-stage stochastic program (DRTSP) which jointly optimizes the deterministic wait-and-see and worst-case expected here-and-now costs, and the probability distribution comes from a family of distributions which are centered at the empirical distribution using ∞−Wasserstein metric. There have been successful developments on deriving tractable approximations of the worst-case expected here-and-now cost in DRTSP. Unfortunately, limited results on exact tractable reformulations of DRTSP. This paper fills this gap by providing sufficient conditions under which the worst-case expected here-and-now cost in DRTSP can be efficiently computed via a tractable convex program. By exploring the properties of binary variables, the developed reformulation techniques are extended to DRTSP with binary random parameters. The main tractable reformulations in this paper are projected into the original decision space and thus can be interpreted as conventional two-stage stochastic programs under discrete support with extra penalty terms enforcing the robustness. These tractable results are further demonstrated to be sharp through complexity analysis.
A joint chance constrained optimization problem involves multiple uncertain constraints, i.e., constraints with stochastic parameters, that are jointly required to be satisfied with probability exceeding a prespecified threshold. In a distributionally robust joint chance constrained optimization problem (DRCCP), the joint chance constraint is required to hold for all probability distributions of the stochastic parameters from a given ambiguity set. In this work, we consider DRCCP involving convex nonlinear uncertain constraints and an ambiguity set specified by convex moment constraints. We investigate deterministic reformulations of such problems and conditions under which such deterministic reformulations are convex. In particular we show that a DRCCP can be reformulated as a convex program if one the following conditions hold: (i) there is a single uncertain constraint, (ii) the ambiguity set is defined by a single moment constraint, (iii) the ambiguity set is defined by linear moment constraints, and (iv) the uncertain and moment constraints are positively homogeneous with respect to uncertain parameters. We further show that if the decision variables are binary and the uncertain constraints are linear then a DRCCP can be reformulated as a deterministic mixed integer convex program. Finally, we present a numerical study to illustrate that the proposed mixed integer convex reformulation can be solved efficiently by existing solvers.
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