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Stochastic Modeling and Optimization

With Applications in Queues, Finance, and Supply Chains

  • Book
  • © 2003

Overview

Authors:
  1. David D. Yao

    1. Department of Operations Research and Industrial Engineering, Columbia University, New York, USA

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  2. Xun Yu Zhou

    1. Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong, Shatin, Hong Kong, China

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  3. Hanqin Zhang

    1. Academy of Mathematics and System Sciences, Chinese Academy of Science, Beijing, China

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xi
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  2. Discrete-time Singularly Perturbed Markov Chains

    • G. Yin, Q. Zhang
    Pages 1-42

  3. Nearly Optimal Controls of Markovian Systems

    • Q. Zhang, R. H. Liu, G. Yin
    Pages 43-86

  4. Stochastic Approximation, with Applications

    • Han-Fu Chen
    Pages 87-109

  5. Performance Potential Based Optimization and MDPs

    • Xi-Ren Cao
    Pages 111-135

  6. An Interior-Point Approach to Multi-Stage Stochastic Programming

    • Shuzhong Zhang
    Pages 137-170

  7. A Brownian Model of Stochastic Processing Networks

    • Hong Chen
    Pages 171-192

  8. Stability of General Processing Networks

    • Jim Dai, Otis B. Jennings
    Pages 193-243

  9. Large Deviations, Long-Range Dependence, and Queues

    • C.-S. Chang, David D. Yao, Tim Zajic
    Pages 245-277

  10. Markowitz’s World in Continuous Time, and Beyond

    • Xun Yu Zhou
    Pages 279-309

  11. Variance Minimization in Stochastic Systems

    • Duan Li, Fucai Qian, Peilin Fu
    Pages 311-332

  12. A Markov Chain Method for Pricing Contingent Claims

    • Jin-Chuan Duan, Geneviève Gauthier, Jean-Guy Simonato
    Pages 333-362

  13. Stochastic Network Models and Optimization of a Hospital System

    • Xiuli Chao, Liming Liu, Shaohui Zheng
    Pages 363-393

  14. Optimal Airline Booking Control with Cancellations

    • Youyi Feng, Ping Lin, Baichun Xiao
    Pages 395-427

  15. Information Revision and Decision Making in Supply Chain Management

    • Houmin Yan, Hanqin Zhang
    Pages 429-457

  16. Back Matter

    Pages 459-468
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Keywords

About this book

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.

Reviews

From the reviews:

"The Workshop Stochastic Models and Optimization … in May 2001, forms the basis of the present volume. 14 papers from about 60 presentations at the workshop were selected and thoroughly revised making self-contained chapters of a book for a broad audience. It highlighted some recent advances in applied probability achieved mainly by scientists with Chinese background. … The book seems to be very suitable for seminar studies at the graduate level." (Hans-Joachim Girlich, OR News, 25, November 2005)

Authors and Affiliations

  • Department of Operations Research and Industrial Engineering, Columbia University, New York, USA

    David D. Yao

  • Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong, Shatin, Hong Kong, China

    Xun Yu Zhou

  • Academy of Mathematics and System Sciences, Chinese Academy of Science, Beijing, China

    Hanqin Zhang

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