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EN
In the paper we introduce the newsvendor problem with a satisficing-level objective, which is defined as maximization of the probability of exceeding the moving target. This target is defined as the expected profit, multiplied by a positive constant. The constant is chosen by the management and it indicates whether the low or the high goal should be achieved. We obtain closed form solutions of this newsvendor model with uniformly distributed demand. Additionally, we consider a bicriteria problem with the satisficing- -level and the classical objective.
EN
In this paper exponential distribution is implemented as a demand distribution in newsvendor model with two different and conflicting goals. The first goal is the standard objective of maximization of the expected profit. The second one is to maximize the probability of exceeding the expected profit, called survival probability. Using exponential distribution as the demand distribution allows us to obtain the exact solutions. Also for this distribution we can study the monotonicity of survival probability with respect to various model parameters analytically. Additional results are obtained when various sets of the parameters are considered. Finally, the bicriteria index which combines these conflicting objectives is optimized which gives the compromise solution. Moreover, in order to illustrate theoretical results, we present numerical examples and graphs of auxiliary functions.
EN
In this paper, we study the newsvendor problem with various degrees of risk tolerance. We consider bicriteria optimization where the first objective is the classical maximization of the expected profit and the second one is the satisficing-level objective. The results depend on the risk coefficient and are different for a risk-neutral, a risk-averse, and a risk-seeking retailer. We find the compromise solution of the bicriteria newsvendor problem numerically, since the two objectives are mutually conflicting. The formulas obtained are illustrated with exponentially distributed demand.
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