Treffer: Technical Note—Multistage Robust Mixed-Integer Programming.

In the paper "Multistage Robust Mixed-Integer Programming," Krzysztof Postek, Ward Romeijnders, and Wolfram Wiesemann study multistage robust mixed-integer programming problems, a popular and powerful yet computationally demanding framework for decision making under uncertainty. The authors leverage dynamic programming principles to decompose multistage problems into large numbers of two-stage subproblems, and they use the finite adaptability approach to solve the latter to exact or approximate optimality. The resulting method allows for efficient parallelization, and the authors showcase its effectiveness on route planning and location-transportation problems. Multistage robust optimization, in which decisions are taken sequentially as new information becomes available about uncertain problem parameters, is a very versatile yet computationally challenging paradigm for decision making under uncertainty. In this technical note, we propose a new model and solution approach for multistage robust mixed-integer programs, which may contain both continuous and discrete decisions at any time stage. Our model builds upon the finite adaptability scheme developed for two-stage robust optimization problems, and it allows us to decompose the multistage problem into a large number of much simpler two-stage problems. We discuss how these two-stage problems can be solved both exactly and approximately, and we report numerical results for route planning and location-transportation problems. [ABSTRACT FROM AUTHOR]

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