We consider a memoryless single-server queue in which users can purchase relative priority so as to reduce their expected waiting costs, which are linear with time. Relative priority is given in proportion to a price paid by customers present in the system. For two service disciplines, (weighted) processor sharing and (weighted) random entrance, we find the unique pure and symmetric Nash equilibrium price paid by the customers.

We study tandem ordering of constant-service stations with unlimited buffers and arbitrary arrival process where service at each station adds a certain value to the job. The goal is to order the stations such that the total expected value of the jobs in the system or, equivalently, the work-in-process inventory cost is minimized. Assuming that the added value–service length ratio increases with the service length, we provide a branch and bound method that finds an optimal order.

Extremal arrival processes, in the sense of increasing convex order of waiting time of queueing systems, are investigated. Two types of extremal processes are proposed: one in the class of processes that have identical marginal distributions and the other in the class of bounded stochastic processes that have the same mean and covariance structure. The worst performance with regard to waiting time in the sense of increasing convex order is guaranteed when these extremal processes are fed into a first in-first out single-server queue.

In this paper, we consider the interval availability distribution of a two-state single unit. We fit the interval availability distribution directly to a beta distribution, such that the probability of nil and a 100% availability is correct as well as the expectation and the variance. The method is fast and easy to implement and gives good results.

In many multi-echelon inventory systems, the lead times are random variables. A common and reasonable assumption in most models is that replenishment orders do not cross, which implies that successive lead times are correlated. However, the process that generates such lead times is usually not well defined, which is especially a problem for simulation modeling. In this paper, we use results from queuing theory to define a set of simple lead time processes guaranteeing that (a) orders do not cross and (b) prespecified means and variances of all lead times in the multiechelon system are attained.

Al-Osh and Alzaid (1988, Statistical Papers 29: 281–300) introduced a class of Poisson moving-average processes. In this paper, we analyze certain properties of such models. In particular, we show that the model has the property of time reversibility. Regression properties of the model are also given. Furthermore, we determine the asymptotic limit distribution for the sample autocovariance function and establish the asymptotic validity of a bootstrap approximation.

A two–time scale stochastic approximation algorithm is proposed for simulation-based parametric optimization of hidden Markov models, as an alternative to the traditional approaches to “infinitesimal perturbation analysis.” Its convergence is analyzed, and a queueing example is presented.

In the theory of multivariate statistics, it is well known that given a sample of n independent p-variate normally distributed random vectors with a common variance-covariance matrix, if at least one of the n vectors has nonzero means, then the sum of squares about the sample mean of the n vectors has a noncentral Wishart distribution. However, a detailed proof for this known result is rarely found in literature. In this paper, we present a formal and complete proof for the well-known result together with an example of its applications.