Linear birth–death processes with immigration and emigration are major models in the study of population processes of biological and ecological systems, and their transient analysis is important in the understanding of the structural behavior of such systems. The spectral method has been widely used for solving these processes; see, for example, Karlin and McGregor [11]. In this article, we provide an alternative approach: the method of characteristics. This method yields a Volterra-type integral equation for the chance of extinction and an explicit formula for the z-transform of the transient distribution. These results allow us to obtain closed-form solutions for the transient behavior of several cases that have not been previously explicitly presented in the literature.

In this paper, we develop a general approach to compute the probabilities of unresolved clones in random pooling designs. This unified and systematic approach gives better insight for handling the dependency issue among the columns and among the rows. Consequently, we identify some faster computation formulas for four random pooling designs proposed in the literature, and we derive some probability distribution functions of the number of unresolved clones that were not available before.

In connection with the classical insurance risk problem, Ross [2] develops a recursive formula for approximating a tail probability in a geometrically compounded distribution. Here, we consider rates of convergence for this approximation.

We consider the following queuing system which arises as a model of a wireless link shared by multiple users. There is a finite number N of input flows served by a server. The system operates in discrete time t = 0,1,2,…. Each input flow can be described as an irreducible countable Markov chain; waiting customers of each flow are placed in a queue. The sequence of server states m(t), t = 0,1,2,…, is a Markov chain with finite number of states M. When the server is in state m, it can serve μim customers of flow i (in one time slot).

The scheduling discipline is a rule that in each time slot chooses the flow to serve based on the server state and the state of the queues. Our main result is that a simple online scheduling discipline, Modified Largest Weighted Delay First, along with its generalizations, is throughput optimal; namely, it ensures that the queues are stable as long as the vector of average arrival rates is within the system maximum stability region.

The aim of this article is to derive the income and cost functionals required to determine the actuarial value of certain types of perishable inventory system. In the basic model, the arrival times of the items to be stored and the ones of the demands for those items form independent Poisson processes. The shelf lifetime of every item is finite and deterministic. Every demand is for a single item and is satisfied by the oldest item on the shelf, if available. The price of an item depends on its shelf age. For an actuarial valuation, it is important to know the distribution of the total value of the items in the system and the expected (discounted) total income and cost generated by the system when in steady state. All of these functionals are determined explicitly. As extensions of the original model, we also deal with the case of batch arrivals and general renewal interdemand times; in both cases, closed-form solutions are obtained.

In this note, we consider a simple immigration birth–death process with total catastrophes and we obtain the transient probabilities. Our approach involves a renewal argument. It is comparatively simpler and leads to more elegant expressions than other approaches that appeared in the literature recently.

This article provides us with some simple criteria to compare Birnbaum reliability importance measure of components in a general binary coherent system. Such criteria are particularly useful in the absence of information concerning component reliabilities. We also find several simple (necessary and sufficient) conditions concerning system structure, under which such comparison is possible. Examples are given to illustrate our results.

We study the value of information sharing in a two-stage supply chain with a single manufacturer and a single retailer in an infinite time horizon, where the manufacturer has finite production capacity and the retailer faces independent demand. The manufacturer receives demand information even during periods of time in which the retailer does not order. Allowing for time-varying cost functions, our objective is to characterize the impact of information sharing on the manufacturer's cost and service level. We develop a new approach to characterize the induced Markov chains under cyclic order-up-to policy and provide a simple proof for the optimality of cyclic order-up-to policy for the manufacturer under the average cost criterion. Using extensive computational analysis, we quantify the impact of information sharing on the manufacturer's performance in an infinite time horizon under both i.i.d. demand and independent but nonstationary demand.