Consider n servers having different exponential service distributions. All servers are initially busy and there are m customers waiting in queue in an ordered line. A server becoming idle is offered to the first in line, if rejected it is then offered to the second, and so on. The objective of each person in line is to minimize their expected time until service completion. We give a simple approach for finding the optimal policy and also show that this policy also minimizes the expected sum of the times the customers spend in the system.

We study the steady-state queue-length vector in a multi-class queue with relative priorities. Upon service completion, the probability that the next served customer is from class k is controlled by class-dependent weights. Once a customer has started service, it is served without interruption until completion. We establish a state-space collapse for the scaled queue-length vector in the heavy-traffic regime, that is, in the limit the scaled queue-length vector is distributed as the product of an exponentially distributed random variable and a deterministic vector. We observe that the scaled queue length reduces as classes with smaller mean service requirement obtain relatively larger weights. We finally show that the scaled waiting time of a class-k customer is distributed as the product of two exponentially distributed random variables.

We consider a queueing loss system with heterogeneous skill based servers with arbitrary distributions. We assume Poisson arrivals, with each arrival having a vector indicating which of the servers are eligible to serve it. Arrivals can only be assigned to a server that is both idle and eligible. We assume arrivals are assigned to the idle eligible server that has been idle the longest and derive, up to a multiplicative constant, the limiting distribution for this system. We show that the limiting probabilities of the ordered list of idle servers depend on the service time distributions only through their means. Moreover, conditional on the ordered list of idle servers, the remaining service times of the busy servers are independent and have their respective equilibrium service distributions. We also provide an algorithm using Gibbs sampler Markov Chain Monte Carlo method for estimating the limiting probabilities and other desired quantities of this system.

This paper defines and studies the down entrance state (DES) and the restart entrance state (RES) classes of quasi-skip free (QSF) processes specified in terms of the nonzero structure of the elements of their transition rate matrix Q. A QSF process is a Markov chain with states that can be specified by tuples of the form (m, i), where $m \in {\open Z}$ represents the “current” level of the state and $i \in {\open Z}^{+}$ the current phase of the state, and its transition probability matrix Q does not permit one-step transitions to states that are two or more levels away from the current state in one direction of the level variable m. A QSF process is a DES process if and only if one step “down” transitions from a level m can only reach a single state in level m − 1, for all m. A QSF process is a RES process if and only if one step “up” transitions from a level m can only reach a single set of states in the highest level M2, largest of all m.

We derive explicit solutions and simple truncation bounds for the steady-state probabilities of both DES and RES processes, when in addition Q insures ergodicity. DES and RES processes have applications in many areas of applied probability comprising computer science, queueing theory, inventory theory, reliability, and the theory of branching processes. To motivate their applicability we present explicit solutions for the well-known open problem of the M/Er/n queue with batch arrivals, an inventory model, and a reliability model.

We show that overshoots over Erlang random variables give rise to a natural generalization of the stationary excess operator and its iterates. The new operators can be used to derive expansions for the expectation Eg(X) of a non-negative random variable, similar to Taylor-like expansions encountered when studying stationary excess operators.

We consider the invariant measure of homogeneous random walks in the quarter-plane. In particular, we consider measures that can be expressed as a finite linear combination of geometric terms and present conditions on the structure of these linear combinations such that the resulting measure may yield an invariant measure of a random walk. We demonstrate that each geometric term must individually satisfy the balance equations in the interior of the state space and further show that the geometric terms in an invariant measure must have a pairwise-coupled structure. Finally, we show that at least one of the coefficients in the linear combination must be negative.

In this paper, we continue our investigation of the shape of the failure rate of a mixture of two densities. In our recent paper, Block, Langberg and Savits [2], we introduced a variation of Glaser's method in which we emphasized the role of the mixing parameter q. There we determined all possible shapes of the failure rate function for a mixture of one exponential and one gamma density. Here we classify all possible shapes for a mixture of two gamma densities having shape parameters b, c>0 and scale parameters λ, μ>0.

In this paper, we study the bivariate Birnbaum–Saunders (BVBS) distribution from a reliability point of view. The monotonicity of the hazard rates of the univariate as well as the conditional distributions is discussed. Clayton's association measure is obtained in terms of the hazard gradient and its value in the case of the BVBS distribution is derived. The probability distributions, in the case of series and parallel systems, are derived and the monotonicity of the failure rate, in the case of series system, is discussed.

In this work, the relationship between the extreme value distributions of the parent and its weighted counterpart distribution is studied. Sufficient conditions are provided in terms of the weight w(x) under which the weighted and parent distributions belong to the same attractor (Fréchet, Weibull, or Gumbel) and the relation of the corresponding shape parameters of the limiting distributions is presented. Additionally, a biased sampling corrected extreme value index is proposed, when the extreme value index is estimated from a biased sample. Finally, some simulation results are presented that suggest the superiority of a biased sample in estimating the extreme value index.

This paper investigates properties of the percentile residual life (PRL) function for a single component and for a k-out-of-n system. The shape of the component PRL function can be determined by the component failure rate function. The intimate relations between these two functions are studied first. Then we generalize the results to a k-out-of-n system by assuming independent and identical components. We show that the behavior of the PRL for a k-out-of-n system is quite different from the component PRL. We also find that for series and parallel systems, the location of the change point of the PRL is monotone in the number of components in a system.