In retailer management, the Newsvendor problem has widely attracted attention as one of basic inventory models. In the traditional approach to solving this problem, it relies on the probability distribution of the demand. In theory, if the probability distribution is known, the problem can be considered as fully solved. However, in any real world scenario, it is almost impossible to even approx...

The classical newsvendor problem is to find a product's order quantity to maximize the expected profit under probabilistic demand. This paper restudies the problem under the perspective of competitive analysis, in which only the lower bound and upper bound rather than the distribution of the future demand are known. Some preliminary results of the problem with competitive analysis are given fir...

We introduce a novel strategy to address the issue of demand estimation in single-item single-period stochastic inventory optimisation problems. Our strategy analytically combines confidence interval analysis and inventory optimisation. We assume that the decision maker is given a set of past demand samples and we employ confidence interval analysis in order to identify a range of candidate ord...

Newsvendor problem is a fundamental singleitem inventory problem with stochastic demands over a single period. In the traditional newsvendor model, the expected utility theory is introduced to measure the risk attitude of decision-maker. However, behavior of decisionmaker for many practical problems cannot be explained with the expected utility theory. As a result, prospect theory of the behavi...

We use distributionally robust stochastic programs (DRSP) to model a general class of newsvendor problems where the underlying demand distribution is unknown, and so the goal is to find an order quantity that minimizes the worst-case expected cost among an ambiguity set of distributions. The ambiguity set consists of those distributions that are not far—in the sense of the so-called variation d...

In this paper, newsvendor problems for innovative products are analyzed. Because the product is new, no relevant historical data is available for statistical demand analysis. Instead of using the probability distribution, the possibility distribution is utilized to characterize the uncertainty of the demand. We consider products whose life cycles are expected to be smaller than the procurement ...