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Uncertainty is an inevitable feature of decision-making environments, and many researches have been developed for optimization under uncertainty. There are two typical approaches to optimize a system with incomplete information and considerable uncertainty: stochastic programming (see, e.g. ) and robust optimization (see [3, 4, 9, 10, 16]). For an uncertain optimization problem whose objective function includes uncertain data, standard stochastic programming
models assume that probability distributions governing the data are known or can be estimated, and maximize the expectation of cost function including random variables. On the other hand, robust optimization approach assumes that the uncertain data are known only within certain bounds, which is called uncertainty set U , and minimizes maximum cost by focusing on the
considerable worst case in U …….