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Karthik Natarajan(matkbnnus.edu.sg) Abstract: The traditional decisionmaking framework for newsvendor models is to assume a distribution of the underlying demand. However, the resulting optimal policy is typically sensitive to the choice of the distribution. A more conservative approach is to assume that the distribution belongs to a set parameterized by a few known moments. An ambiguityaverse newsvendor would choose to maximize the worstcase profit. Most models of this type assume that only the mean and the variance are known, but do not attempt to include asymmetry properties of the distribution. Other recent models address asymmetry by including skewness and kurtosis. However, closedform expressions on the optimal bounds are difficult to find for such models. In this paper, we propose a framework under which the expectation of a piecewise linear objective function is optimized over a set of distributions with known asymmetry properties. This asymmetry is represented by the first two moments of multiple random variables that result from partitioning the original distribution. In the simplest case, this reduces to semivariance. The optimal bounds can be solved through a secondorder cone programming (SOCP) problem. This framework can be applied to the riskaverse and riskneutral newsvendor problems and option pricing. We provide a closedform expression for the worstcase newsvendor profit with only mean, variance and semivariance information. Keywords: newsvendor model; asymmetry; semivariance; ambiguity averse; risk averse Category 1: Robust Optimization Citation: Download: [PDF] Entry Submitted: 07/11/2008 Modify/Update this entry  
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