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Stochastic Modeling and Optimization

With Applications in Queues, Finance, and Supply Chains

  • David D. Yao
  • Xun Yu Zhou
  • Hanqin Zhang

Table of contents

  1. Front Matter
    Pages i-xi
  2. Q. Zhang, R. H. Liu, G. Yin
    Pages 43-86
  3. Jim Dai, Otis B. Jennings
    Pages 193-243
  4. C.-S. Chang, David D. Yao, Tim Zajic
    Pages 245-277
  5. Duan Li, Fucai Qian, Peilin Fu
    Pages 311-332
  6. Jin-Chuan Duan, Geneviève Gauthier, Jean-Guy Simonato
    Pages 333-362
  7. Xiuli Chao, Liming Liu, Shaohui Zheng
    Pages 363-393
  8. Youyi Feng, Ping Lin, Baichun Xiao
    Pages 395-427
  9. Back Matter
    Pages 459-468

About this book

Introduction

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.

Keywords

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

Authors and affiliations

  • David D. Yao
    • 1
  • Xun Yu Zhou
    • 2
  • Hanqin Zhang
    • 3
  1. 1.Department of Operations Research and Industrial EngineeringColumbia UniversityNew YorkUSA
  2. 2.Department of Systems Engineering and Engineering ManagementChinese University of Hong KongShatin, Hong KongChina
  3. 3.Academy of Mathematics and System SciencesChinese Academy of ScienceBeijingChina

Bibliographic information