In this paper we present a unified approach to the optimal dimensioning and tariffing of loss networks. In our formulation the optimum is chosen to maximize the profit for the company operating the loss network. We assume that the operating company has the flexibility to determine tariffs and grade of service — although both of these can possibly be subject to regulatory constraints. The fact that the tariffing may affect demand and, hence, the dimensioning makes it essential that the operating company include the tariff/demand trade-off in determining the optimal way to dimension the loss network. A consequence of our formulation is that the optimal tariff structure has a particularly simple form, with the optimal tariff on a particular route separating into a term related to the tariff/demand trade-off on that route and a term that reflects the cost of the circuits used by the route.

This paper extends product form results for queueing networks with signals to allow history-dependent routing. The signals in these models carry information to nodes and induce multiple customers to move simultaneously within the network. Two models are studied in this paper. In the first one we assume that routing probabilities of a departing customer from a given class of nodes depend on the amount of service just received by the customer and whether its departure is the result of an actual service completion or the result of an arriving signal. In the second model we assume that the routing probabilities of a customer depend on the number of times this customer's service has been interrupted by signals in the past as well as the cause of its departure. We show that both models possess simple product form solutions. These results provide a new dimension in modeling and analyzing practical systems.

Consider an M/G/1 type queueing system with two service modes: regular speed and high speed. The service rule is characterized by two switch-over levels nR and nH, where nR and nH are given integers with 0 ≤ nH < nR. The server switches from regular speed mode to high speed mode when the number of customers present at a service completion epoch is equal to or larger than nR and switches from high speed mode to regular speed mode when the number of customers present decreases to nH. A key feature of the model is that the server takes a vacation for setup operations before a new service mode is available. This paper derives for the general model an expression for the generating function of the equilibrium queue-length distribution in terms of the switch-over levels. Two unknown parameters appear in the generating functions. Using a recursive method, we solve these unknown parameters and obtain a computationally tractable algorithm for the steady-state probabilities.

Motivated by extreme-value engineering in service systems, we develop and evaluate simple approximations for the distributions of maximum values of queueing processes over large time intervals. We provide approximations for several different processes, such as the waiting times of successive customers, the remaining workload at an arbitrary time, and the queue length at an arbitrary time, in a variety of models. All our approximations are based on extreme-value limit theorems. Our first approach is to approximate the queueing process by one-dimensional reflected Brownian motion (RBM). We then apply the extremevalue limit for RBM, which we derive here. Our second approach starts from exponential asymptotics for the tail of the steady-state distribution. We obtain an approximation by relating the given process to an associated sequence of i.i.d. random variables with the same asymptotic exponential tail. We use estimates of the asymptotic variance of the queueing process to determine an approximate number of variables in this associated i.i.d. sequence. Our third approach is to simplify GI/G/1 extreme-value limiting formulas in Iglehart [25] by approximating the distribution of an idle period by the stationary-excess distribution of an interarrival time. We use simulation to evaluate the quality of these approximations for the maximum workload. From the simulations we obtain a rough estimate of the time when the extreme-value limit theorems begin to yield good approximations.

We present a new estimator for the standard deviation parameter in the standard quality control model. We argue that it has a smaller mean square error than the usual estimator when the data are normal and that, unlike the usual estimator, it remains consistent even when the data are not normal.

Monte Carlo simulation is one alternative for analyzing options markets when the assumptions of simpler analytical models are violated. We introduce techniques for the sensitivity analysis of option pricing, which can be efficiently carried out in the simulation. In particular, using these techniques, a single run of the simulation would often provide not only an estimate of the option value but also estimates of the sensitivities of the option value to various parameters of the model. Both European and American options are considered, starting with simple analytically tractable models to present the idea and proceeding to more complicated examples. We then propose an approach for the pricing of options with early exercise features by incorporating the gradient estimates in an iterative stochastic approximation algorithm. The procedure is illustrated in a simple example estimating the option value of an American call. Numerical results indicate that the additional computational effort required over that required to estimate a European option is relatively small.

In this paper we consider scheduling n tasks on m parallel machines where the task processing times are i.i.d. random variables with a common distribution function F. Scheduling is done by an a priori assignment of tasks to machines. We show that if the distribution function F is a Pólya frequency function of order 2 (decreasing reverse hazard rate) then the assignment that attempts to place an equal number of tasks on each machine achieves the stochastically smallest makespan among all assignments. The condition embraces many important distributions, such as the gamma and truncated normal distributions. Assuming that the task processing times have a common density that is a Pólya frequency function of order 2 (increasing likelihood ratio), then we find that flatter schedules have stochastically smaller makespans in the sense of the “joint” likelihood ratio.

We study tandem ordering of constant-service stations with unlimited buffers where service at each station adds a certain value to the job. With the goal of minimizing the total expect value of the jobs in the system, we provide conditions under which some particular orderings are optimal and describe a heuristic that finds a near-optimal order for stations of arbitrary service lengths and added values.

This work studies sequential assignment match processes, in which random offers arrive sequentially according to a renewal process, and when an offer arrives it must be assigned to one of given waiting candidates or rejected. Each candidate as well as each offer is characterized by an attribute. If the offer is assigned to a candidate that it matches, a reward R is received; if it is assigned to a candidate that it does not match, a reward r ≤ R is received; and if it is rejected, there is no reward. There is an arbitrary discount function, which corresponds to the process terminating after a random lifetime. Using continuoustime dynamic programming, we show that if this lifetime is decreasing in failure rate and candidates have distinct attributes, then the policy that maximizes total expected discounted reward is of a very simple form that is easily determined from the optimal single-candidate policy. If the lifetime is increasing in failure rate, the optimal policy can be recursively determined: a solution algorithm is presented that involves scalar rather than functional equations. The model originated in the study of optimal donor-recipient assignment in live-organ transplants. Some other applications are mentioned as well.

We extend a recent strengthening of Azuma's inequality to allow for a non symmetric bound on the martingale differences.