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This paper formulates the optimal control problem for a class of mathematical models in which the system to be controlled is characterized by a finite-state discrete-time Markov process. The states of this internal process are not directly observable by the controller; rather, he has available a set of observable outputs that are only probabilistically related to the internal state of the system. The formulation is illustrated by a simple machine-maintenance example, and other specific application areas are also discussed. The paper demonstrates that, if there are only a finite number of control intervals remaining, then the optimal payoff function is a piecewise-linear, convex function of the current state probabilities of the internal Markov process. In addition, an algorithm for utilizing this property to calculate the optimal control policy and payoff function for any finite horizon is outlined. These results are illustrated by a numerical example for the machine-maintenance problem. [ABSTRACT FROM AUTHOR]

We study lot-size policies in a serial, multistage manufacturing/inventory system with two key generalizations, namely (1) random yields at each production stage and (2) an autoregressive demand process. Previous research shows that the optimal policies in models with random yields (even in models with a single installation) lack the familiar order-up-to structure and are not myopic. Thus, dynamic programming algorithms are needed to compute optimal policies, and one encounters the "curse of dimensionality"; this is exacerbated here by the need to expand the size and dimension of the state space to accommodate the autoregressive demand feature. Nevertheless, although our model is more complex, we prove that there is an optimal policy with the order-up-to feature and, more importantly, that the optimal policy is myopic. This avoids the computational burden of dynamic programming. Our results depend on two assumptions concerning the stochastic yield, namely that the expected yield at a work station is proportional to the lot size, and the distribution of the deviation of the yield from its mean does not depend on the lot size. We introduce the concept of echelon-like variables, a generalization of Clark and Scarf's classical concept of echelon variables, to derive the structure of optimal policies. Furthermore, we show that the same kind of policy is optimal for several criteria: infinite-horizon discounted cost, infinite-horizon long-run average cost, and finite-horizon discounted cost (with the appropriate choice of the salvage value function). [ABSTRACT FROM AUTHOR]

This paper discusses the distributional Little's law and examines its applications in a variety of queueing systems. The distributional law relates the steady-state distributions of the number in the system (or in the queue) and the time spent in the system (or in the queue) in a queueing system under FIFO. We provide a new proof of the distributional law and in the process we generalize a well known theorem of Burke on the equality of pre-arrival and postdeparture probabilities. More importantly, we demonstrate that the distributional law has important algorithmic and structural applications and can be used to derive various performance characteristics of several queueing systems which admit distributional laws. As a result, we believe that the distributional law is a powerful tool for the derivation of performance measures in queueing systems and can lead to a certain unification of queueing theory. [ABSTRACT FROM AUTHOR]

In this paper, we develop a stochastic, multiclass network equilibrium model of airline passenger transportation. The model explicitly incorporates the behavior of the passengers in regard to the decision whether or not to travel, and route selection, and formalizes the passenger's decision as an abstract network. The equilibrium between the realized demand for the routes of travel and the supply of the seats is shown to satisfy a system of nonlinear equations. A Gauss-Seidel algorithm is then proposed for the computation of the equilibrium and conditions for convergence are established. Finally, the algorithm is applied to compute the solution to the national Air Canada airline passenger network, and the output is compared to the actual observed flows. [ABSTRACT FROM AUTHOR]

Many practical location decision problems require facility locations to be restricted to a finite set of points. Quite often such restrictions arise from natural constraints that are specific to the application setting. Motivated by such situations, this paper examines the Stochastic Queue Median (SQM) model of Berman, Larson and Chiu, where potential facility locations are restricted to a finite set of discrete points. An efficient algorithm is presented to solve for the optimal facility location parametrically in the systemwide arrival rate of calls. The complexity of the algorithm and a worst case analysis for the model formulation are also discussed. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm to compute reliability measures on a stochastic network m which both nodes and links can fad The measures considered are the probability that nodes s and t can communicate for all node pairs s and t, the probability that all operative node pairs can communicate, and the expected number of node pairs communicating It also computes the latter two measures when all communication must proceed through a root node A specialized version of the algorithm is given for networks in which only nodes can fail. [ABSTRACT FROM AUTHOR]

Hit-and-Run algorithms are Monte Carlo procedures for generating points that are asymptotically distributed according to general absolutely continuous target distributions G over open bounded regions S. Applications include nonredundant constraint identification, global optimization, and Monte Carlo integration. These algorithms are reversible random walks that commonly incorporate uniformly distributed step directions. We investigate nonuniform direction choice and show that, under regularity conditions on the region S and target distribution G, there exists a unique direction choice distribution, characterized by necessary and sufficient conditions depending on S and G, which optimizes the Doob bound on rate of convergence. We include computational results demonstrating greatly accelerated convergence for this optimizing direction choice as well as for more easily implemented adaptive heuristic rules. [ABSTRACT FROM AUTHOR]