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Stochastic Modeling and Analysis of Manufacturing Systems

  • David D. Yao

Part of the Springer Series in Operations Research book series (ORFE)

Table of contents

  1. Front Matter
    Pages i-xv
  2. John A. Buzacott, J. George Shanthikumar, David D. Yao
    Pages 1-45
  3. Paul Glasserman, David D. Yao
    Pages 133-188
  4. Cheng-Shang Chang, J. George Shanthikumar, David D. Yao
    Pages 189-231
  5. Paul Glasserman
    Pages 233-280

About this book

Introduction

Manufacturing systems have become increasingly complex over recent years. This volume presents a collection of chapters which reflect the recent developments of probabilistic models and methodologies that have either been motivated by manufacturing systems research or been demonstrated to have significant potential in such research.
The editor has invited a number of leading experts to present detailed expositions of specific topics. These include: Jackson networks, fluid models, diffusion and strong approximations, the GSMP framework, stochastic convexity and majorization, perturbation analysis, scheduling via Brownian models, and re-entrant lines and dynamic scheduling. Each chapter has been written with graduate students in mind, and several have been used in graduate courses that teach the modeling and analysis of manufacturing systems.

Keywords

Manufacturing Manufacturing System Manufacturing Systems Markov Chain Probability Models Stochastic Networks Stochastic model calculus communication modeling production search engine marketing (SEM)

Editors and affiliations

  • David D. Yao
    • 1
  1. 1.Department of Industrial Engineering and Operations ResearchColumbia UniversityNew YorkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-2670-3
  • Copyright Information Springer-Verlag New York 1994
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-7628-9
  • Online ISBN 978-1-4612-2670-3
  • Series Print ISSN 1431-8598
  • Buy this book on publisher's site