It is well known that in the case of a Poisson process with constant intensity function the interarrival times are independent and identically distributed, each having exponential distribution. We study this problem when the intensity function is monotone. In particular, we show that in the case of a nonhomogeneous Poisson process with decreasing (increasing) intensity the interarrival times are increasing (decreasing) in the hazard rate ordering sense and they are also jointly likelihood ratio ordered (cf. Shanthikumar and Yao, 1991, Bivariate characterization of some stochastic order relations, Advances in Applied Probability 23: 642–659). This result is stronger than the usual stochastic ordering between the successive interarrival times. Also in this case, the interarrival times are conditionally increasing in sequence and, as a consequence, they are associated. We also consider the problem of comparing two nonhomogeneous Poisson processes in terms of the ratio of their intensity functions and establish some results on the successive number of events from one process occurring between two consecutive occurrences from the second process.

We discuss socially optimal and equilibrium retrial rates in a single-server queueing model. We extend known results, compare the two rates, and suggest ways to impose tolls on retrials (or rebates for waiting) in order to equate the equilibrium rate with the socially optimal one.

Several agents with different subjective probabilities make a binary decision at a time determined by a planner. Each agent chooses the action that has the highest probability of success. Given that their probabilities differ, so will their choices. From time 0 until decision time, all the agents are entitled to access the same increasing flow of information. The planner, who gains from having as many agents as possible making the right choice, faces the following tradeoff: the more information she feeds to the agents, the better off they will be in making their decisions, but the less likely they will be to diversify their actions, so the more difficult it will be for her to hedge her positions. The model gives rise to a continuous time optimal stopping problem.

It is known that a threshold policy (or trunk reservation policy) is optimal for Erlang's loss system under certain assumptions. This paper examines the robustness of this policy under departures from the standard assumption of Poisson arrivals and shows that the optimal policy has a generalized trunk reservation form.

In this paper, we present some new results on the makespan and flowtime in a two-parallel machine open shop. A set of n jobs is to be processed on two machines, the order of processing being immaterial. For the case where the machines are identical, and the jobs are nonoverlapping ordered, we show that the sequences that stochastically minimize the makespan are longest processing time first-shortest processing time first (LPT-SPT). Within this class of sequences, we show that the SPT sequence stochastically minimizes the flowtime.

In this paper, we discuss the late-arrival discrete-time Geom(n)/G(n)/1/N queue with state-dependent arrival and service processes. Whereas the interarrival times are geometrically distributed, service times are conditioned on the system length at the moment of service initiation. The model has wide applications in computer-communications systems, broadband integrated service digital network, asynchronous transfer mode, and so on. The analysis of the model has been carried out, using the supplementary variable technique, and the final results are presented in the form of recursive equations that can be easily implemented on any personal computer. In addition, relations among state probabilities at prearrival, postdeparture, and random epochs have been developed. To demonstrate the effectiveness of our method, some numerical examples have been presented for service-time distributions such as geometric, deterministic, arbitrary, and mixed. The results obtained in this paper should be found useful by system designers who wish to control the congestion by adjusting the service rate if the arrival traffic changes.

We analyze a family of queueing systems where the interarrival time In+1 between customers n and n + 1 depends on the service time Bn of customer n. Specifically, we consider cases where the dependency between In+1 and Bn is a proportionality relation and Bn is an exponentially distributed random variable. Such dependencies arise in the context of packet-switched networks that use rate policing functions to regulate the amount of data that can arrive to a link within any given time interval. These controls result in significant dependencies between the amount of work brought in by customers/packets and the time between successive customers. The models developed in the paper and the associated solutions are, however, of independent interest and are potentially applicable to other environments.

Several scenarios that consist of adding an independent random variable to the interarrival time, allowing the proportionality to be random and the combination of the two are considered. In all cases, we provide expressions for the Laplace-Stieltjes Transform of the waiting time of a customer in the system. Numerical results are provided and compared to those of an equivalent system without dependencies.

We study the problem of scheduling multiclass parallel server queues where the cost for each job is a general class-dependent function of the job's completion time and a second random variable. We characterize the structure of the optimal policy under fairly general conditions on the random variables.

We propose a new approach to the analysis of a discrete-time queueing system whose input is generated by a Markov-modulated process and whose service rate is constant. Renewal cycles are identified and the system state on each renewal cycle is modeled as a one-dimensional Markov chain.

Fixed point approximations for blocking probabilities arising from a link independence assumption and a light retrial rate limit are derived for circuitswitched network models that incorporate caller retrials. This approximation is a generalization of the well-known reduced load approximation for loss networks, which retains much of the versatility of this approximation.

We develop a method for computing the optimal double band [b, B] policy for switching between two diffusions with continuous rewards and switching costs. The two switch levels [b, B] are obtained as perturbations of the single optimal switching point a of the control problem with no switching costs. More precisely, we find that in the case of average reward problems the optimal switch levels can be obtained by intersecting two curves: (a) the function, γ(a), which represents the long-run average reward if we were to switch between the two diffusions at a and switches were free, and (b) a horizontal line whose height depends on the size of the transaction costs. Our semianalytical approach reduces, for example, the solution of a problem recently posed by Perry and Bar-Lev (1989, in Stochastic Analysis and Applications 7: 103–115) to the solution of one nonlinear equation.

This paper examines the availability of an inspected system where the rate of deterioration is governed by an exogenous random environment. We develop a qualitative result that exposes the relationship among remaining life, deterioration, and repairs. This result requires no specific distributional assumptions. We use this result in developing a simple bound on stationary availability. We investigate these relationships numerically for a Markovian system and provide insights useful to practitioners in selecting an inspection process that guarantees a target availability.

We consider Bayes' estimation of the number of independent homogeneous Poisson processes of a superimposed process with unknown rates. The estimation of the total rate of the undetected processes is also considered. Exact posterior distributions are obtained. Monotonicity and asymptotic properties of the Bayes' estimator are also discussed.

It is well known that mixtures of decreasing failure rate (DFR) distributions have the DFR property. A similar result is, of course, not true for increasing failure rate (IFR) distributions. In a recent note, Gurland and Sethuraman (1994, Technometrks36(4): 416–418) presented two examples where mixtures of IFR distributions show DFR property. In this paper, we present a general approach to study the mixtures of distributions and show that the failure rates of the unconditional and conditional distributions cross at most at one point. Mixtures of Weibull distribution with a shape parameter greater than 1 are examined in detail. This also enables us to study the monotonic properties of the mean residual life function of the mixture. Some examples are provided to illustrate the results.

Diameter measurements on successive metal hubs from a machining operation are modeled using a random walk with observation error and linear drift corresponding to tool wear. After producing and measuring a hub, the depth of the cutting tool on the lathe can be adjusted in integer multiples of 0.0001 inches. How should the tool be adjusted?

An optimal discrete adjustment strategy is derived assuming that the lathe automatically corrects for deterministic tool wear. The objective is to minimize expected run costs proportional to the sum of squared diameter deviations from a target plus fixed charges for manual tool adjustments. The optimal strategy makes no manual adjustment if an estimate of the process mean is near target. Otherwise, an adjustment is made to return the estimated mean as near to target as possible within the adjustment resolution.

The region where no adjustments are made widens near the end of the production run where adjustments have only short-term impact. The region converges as the number of remaining periods increases. Plots of expected run costs show that the extra cost of discreteness is small at high resolution but is substantial when the adjustment grid is coarse.

We consider an M/G/1 queue with the special feature of additional negative customers, who arrive according to a Poisson process. Negative customers require no service, but at their arrival a stochastic amount of work is instantaneously removed from the system. We show that the workload distribution in this M/G/1 queue with negative customers equals the waiting time distribution in a GI/G/1 queue with ordinary customers only; the effect of the negative customers is incorporated in the new arrival process.

An implementable on-line approach to solve the nonlinear filtering equation for a partially observed system in which both the state and observation processes are jump processes is presented. By making use of the special structure of jump processes, the new method allows us to obtain the solution of the resulting nonlinear filtering equation from solving a linear system of ordinary differential equations and a linear system of algebraic equations recursively and via a simple normalization procedure.

We consider a hierarchical linear regression model where the regression parameters for the units have a multivariate normal distribution whose parameters are unknown. Several replications are available for each unit. The design matrices for the units need not be the same. A complicating feature of the model is that each observation is subject to measurement error. The objective of the paper is to derive the predictive distribution of the “true” value of the response at a given design point. A Bayesian treatment is given to the problem. In addition to standard prior distributions, other prior distributions are considered. The calculations are done with the Gibbs sampler. An example is discussed in detail.

In this paper, we study the problem of dynamic allocation of a single server to parallel queues with finite-capacity buffers. The arrival processes are mutually independent, equal rate Poisson processes, and the service times are independent and identically distributed random variables with an arbitrary distribution. We are interested in characterizing the allocation policy that stochastically minimizes the number of customers lost due to buffer overflows. Using a coupling argument, we establish the optimality of the Fewest-Empty-Spaces policy, which allocates the server to the queue with the fewest empty buffer spaces, within the class of nonpreemptive and nonidling policies. The result extends to the class of preemptive policies, if the service times are exponentially distributed. We also briefly discuss the allocation problem under more general statistical assumptions on the arrival processes.