K -Adaptability in Two-Stage Robust Binary Programming

Grani A. Hanasusanto (), Daniel Kuhn () and Wolfram Wiesemann ()
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Grani A. Hanasusanto: Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom
Daniel Kuhn: Risk Analytics and Optimization Chair, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
Wolfram Wiesemann: Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom

Operations Research, 2015, vol. 63, issue 4, 877-891

Abstract: Over the last two decades, robust optimization has emerged as a computationally attractive approach to formulate and solve single-stage decision problems affected by uncertainty. More recently, robust optimization has been successfully applied to multistage problems with continuous recourse. This paper takes a step toward extending the robust optimization methodology to problems with integer recourse, which have largely resisted solution so far. To this end, we approximate two-stage robust binary programs by their corresponding K -adaptability problems, in which the decision maker precommits to K second-stage policies, here -and-now, and implements the best of these policies once the uncertain parameters are observed. We study the approximation quality and the computational complexity of the K -adaptability problem, and we propose two mixed-integer linear programming reformulations that can be solved with off-the-shelf software. We demonstrate the effectiveness of our reformulations for stylized instances of supply chain design, route planning, and capital budgeting problems.

Keywords: programming; integer; stochastic (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (41)

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