This work deals with Stochastic Programming Uncertainty is a key issue in many decision problems and ignoring randomness easily leads to inferior or even infeasible decisions. In contrast to the neighboring mathematical fields, such as online or robust optimization [3, 15, 16], stochastic programming models benefit from the assumption that probability distributions governing the data are known. This underlying probabilistic model of uncertainty turns finding optimal decisions into selecting “best” random variables and evokes the need to adequately compare random variables according to their utility in the respective context.
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