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Hierarchical Decision Making in Stochastic Manufacturing Systems

  • Suresh P. Sethi
  • Qing Zhang

Part of the Systems & Control: Foundations & Applications book series (SCFA)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Introduction and Models of Manufacturing Systems

    1. Front Matter
      Pages 1-1
    2. Suresh P. Sethi, Qing Zhang
      Pages 3-15
    3. Suresh P. Sethi, Qing Zhang
      Pages 17-28
  3. Optimal Control of Manufacturing Systems: Existence and Characterization

    1. Front Matter
      Pages 29-29
    2. Suresh P. Sethi, Qing Zhang
      Pages 31-62
    3. Suresh P. Sethi, Qing Zhang
      Pages 63-81
  4. Asymptotic Optimal Controls

    1. Front Matter
      Pages 83-83
    2. Suresh P. Sethi, Qing Zhang
      Pages 85-130
    3. Suresh P. Sethi, Qing Zhang
      Pages 131-155
    4. Suresh P. Sethi, Qing Zhang
      Pages 157-186
    5. Suresh P. Sethi, Qing Zhang
      Pages 219-247
  5. Multilevel Hierarchical Decisions

    1. Front Matter
      Pages 249-249
    2. Suresh P. Sethi, Qing Zhang
      Pages 251-276
    3. Suresh P. Sethi, Qing Zhang
      Pages 277-302
  6. Computations and Conclusions

    1. Front Matter
      Pages 303-303
    2. Suresh P. Sethi, Qing Zhang
      Pages 305-325
    3. Suresh P. Sethi, Qing Zhang
      Pages 327-334
  7. Back Matter
    Pages 335-422

About this book

Introduction

One of the most important methods in dealing with the optimization of large, complex systems is that of hierarchical decomposition. The idea is to reduce the overall complex problem into manageable approximate problems or subproblems, to solve these problems, and to construct a solution of the original problem from the solutions of these simpler prob­ lems. Development of such approaches for large complex systems has been identified as a particularly fruitful area by the Committee on the Next Decade in Operations Research (1988) [42] as well as by the Panel on Future Directions in Control Theory (1988) [65]. Most manufacturing firms are complex systems characterized by sev­ eral decision subsystems, such as finance, personnel, marketing, and op­ erations. They may have several plants and warehouses and a wide variety of machines and equipment devoted to producing a large number of different products. Moreover, they are subject to deterministic as well as stochastic discrete events, such as purchasing new equipment, hiring and layoff of personnel, and machine setups, failures, and repairs.

Keywords

Marketing Markov Markov chain Martingale calculus decision making model optimization production stochastic manufacturing systems verification

Authors and affiliations

  • Suresh P. Sethi
    • 1
  • Qing Zhang
    • 2
  1. 1.Faculty of ManagementUniversity of TorontoTorontoCanada
  2. 2.Department of MathematicsUniversity of GeorgiaAthensUSA

Bibliographic information