Decision analysts commonly use low-order discrete approximation of probability distributions to reduce computational effort. We have found that three popular discretization methods can lead to serious errors in certain applications, among them, news vendor problem and scheduling decisions for...

The analysis of stochastic models is often greatly complicated if there are censored observations of the random variables. This paper characterizes families of distributions which help keep tractable the analysis of such models. Our primary motivation is to provide guidance to practitioners in...

We investigate the firm's dynamic nonlinear pricing problem when facing consumers whose tastes vary according to a scalar index. We relax the standard assumption that the firm knows the distribution of this index. In general the firm should determine its marginal price schedule as if it were...

Economic lot sizing and batching models often assume reliable manufacturing facilities. In this research, we focus on the effects of machine breakdowns and corrective maintenance on the economic lot sizing decisions. Two production control policies are proposed for coping with these stochastic...

We analyze a continuous review (Q, r) stochastic inventory model in which orders placed with a make-to-order manufacturer can be shipped via two alternative freight modes differing in lead time and costs. The costs of placing an order and using each freight mode consist of fixed components and...