Exploiting the Structure of Two-Stage Robust Optimization Models with Exponential Scenarios

This paper addresses a class of two-stage robust optimization models with an exponential number scenarios given implicitly. We apply Dantzig–Wolfe decomposition to exploit the structure these and show that original problem reduces single-stage problem. propose Benders algorithm for reformulated also develop heuristic dualizes linear programming relaxation inner maximization in model iteratively generates cuts shape convex hull uncertainty set. combine this create more effective hybrid algorithm. Because master subproblem are mixed-integer programs, it is computationally demanding solve them optimally at each iteration Therefore, we novel stopping conditions programs provide relevant convergence proofs. Extensive computational experiments on nurse planning two-echelon supply chain performed evaluate efficiency proposed algorithms.