We propose a Robust Dual Dynamic Programming (RDDP) scheme for multi-stage robust optimization problems. The RDDP scheme takes advantage of the decomposable nature of these problems by bounding the costs arising in the future stages through inner and outer approximations. In contrast to Stochastic Dual Dynamic Programming, we refine the approximations using as a devise our inner approximations to determine the points of refinement. We prove that RDDP converges deterministically in finite time. We demonstrate the promising performance of our algorithm in stylized instances of inventory management problems.
Bio: Angelos Georghiou received his M.Sci. degree in Mathematics in 2008 and his Ph.D. degree in Operations Research at the Department of Computing in 2012, both from Imperial College London. From 2012–2013, he was a postdoctoral researcher at the Process Systems Engineering Laboratory at MIT, and from 2013–2016 a post-doctoral researcher at the Automatic Control Laboratory at ETH Zurich. He is currently an Assistant Professor of Operations Management in the Desautels Faculty of Management at McGill University. His research focuses on the development of tractable computational methods for the solution of stochastic and robust optimization problems, as well as applications in operations management, finance and energy.
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