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K-Adaptability in Two-Stage Mixed-Integer Robust Optimization

Anirudh Subramanyam(asubramanyam***at***cmu.edu)
Chrysanthos E. Gounaris(gounaris***at***cmu.edu)
Wolfram Wiesemann(ww***at***imperial.ac.uk)

Abstract: We study two-stage robust optimization problems with mixed discrete-continuous decisions in both stages. Despite their broad range of applications, these problems pose two fundamental challenges: (i) they constitute infinite-dimensional problems that require a finite-dimensional approximation, and (ii) the presence of discrete recourse decisions typically prohibits duality-based solution schemes. We address the first challenge by studying a K-adaptability formulation that selects K candidate recourse policies before observing the realization of the uncertain parameters and that implements the best of these policies after the realization is known. We establish conditions under which the K-adaptability problem remains continuous, convex and tractable, and we contrast them to the corresponding conditions for the two-stage robust optimization problem. We address the second challenge through a branch-and-bound scheme that enjoys asymptotic convergence in general and finite convergence under specific conditions. We illustrate the performance of our algorithm in numerical experiments involving benchmark data from several application domains.

Keywords: Robust Optimization, Two-Stage Problems, K-Adaptability, Branch-and-Bound

Category 1: Robust Optimization

Citation:

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Entry Submitted: 06/21/2017
Entry Accepted: 06/21/2017
Entry Last Modified: 06/21/2017

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