With Applications in Queues, Finance, and Supply Chains
Yao, David D., Zhang, Hanqin, Zhou, Xun Yu (Eds.)
2003, XI, 468 p.
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The objective of this volume is to highlight through a collection of chap ters some of the recent research works in applied prob ability, specifically stochastic modeling and optimization. The volume is organized loosely into four parts. The first part is a col lection of several basic methodologies: singularly perturbed Markov chains (Chapter 1), and related applications in stochastic optimal control (Chapter 2); stochastic approximation, emphasizing convergence properties (Chapter 3); a performance-potential based approach to Markov decision program ming (Chapter 4); and interior-point techniques (homogeneous self-dual embedding and central path following) applied to stochastic programming (Chapter 5). The three chapters in the second part are concerned with queueing the ory. Chapters 6 and 7 both study processing networks - a general dass of queueing networks - focusing, respectively, on limit theorems in the form of strong approximation, and the issue of stability via connections to re lated fluid models. The subject of Chapter 8 is performance asymptotics via large deviations theory, when the input process to a queueing system exhibits long-range dependence, modeled as fractional Brownian motion.
Discrete-time Singularly Perturbed Markov Chains * Nearly Optimal Controls of Markovian Systems * Stochastic Approximation, with Applications * Performance Potential Based Optimization and Markov Decision Processes * An Interior-Point and Decomposition Approach to Stochastic Programming * Stability of General Processing Networks * Large Deviations, Long-Range Dependence, and Queues * Markowitz's World in Continuous Time, and Beyond * A Markov Chain Method for Pricing Contingent Claims * Stochastic Network Models and Optimization of a Hospital System * Optimal Airline Booking Control with Cancellations * Information Revision and Decision Making in Supply Chain Management