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Krzysztof Postek(k.postektilburguniversity.edu) Abstract: We consider decision making problems under uncertainty, assuming that only partial distributional information is available  as is often the case in practice. In such problems, the goal is to determine hereandnow decisions, which optimally balance deterministic immediate costs and worstcase expected future costs. These problems are challenging, since the worstcase distribution needs to be determined while the underlying problem is already a difficult multistage recourse problem. Moreover, as found in many applications, the model may contain integer variables in some or all stages. Applying a wellknown result by BenTal and Hochman (1972), we are able to efficiently solve such problems without integer variables, assuming only readily available distributional information on means and mean absolute deviations. Moreover, we extend the result to the nonconvex integer setting by means of convex approximations (see Romeijnders et al. (2015a)), proving corresponding performance bounds. Our approach is straightforward to implement using oftheshelf software as illustrated in our numerical experiments. Keywords: robust; ambiguous; integer; recourse; stochastic; multistage Category 1: Robust Optimization Category 2: Stochastic Programming Citation: CentER Discussion Paper No. 2016039 Download: [PDF] Entry Submitted: 09/29/2016 Modify/Update this entry  
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