Stochastic Modeling and Optimization

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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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.

• Markov Chains
• Markov chain
• Rang
• Stochastic Approximation
• Stochastic Programming
• Supply Chains
• decision making
• network models
• operations research
• optimal control
• optimization
• statistics
• stochastic network
• supply chain
• supply chain management
• quantitative finance

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)

• 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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