Nested Partitions Method, Theory and Applications

  • Leyuan Shi
  • Sigurdur Ólafsson

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 109)

Table of contents

  1. Front Matter
    Pages I-X
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Leyuan Shi, Sigurdur Ólafsson
      Pages 1-15
  3. Methodology

    1. Front Matter
      Pages 17-17
    2. Leyuan Shi, Sigurdur Ólafsson
      Pages 19-46
    3. Leyuan Shi, Sigurdur Ólafsson
      Pages 47-68
    4. Leyuan Shi, Sigurdur Ólafsson
      Pages 69-83
    5. Leyuan Shi, Sigurdur Ólafsson
      Pages 85-103
  4. Applications

    1. Front Matter
      Pages 105-105
    2. Leyuan Shi, Sigurdur Ólafsson
      Pages 107-124
    3. Leyuan Shi, Sigurdur Ólafsson
      Pages 125-155
    4. Leyuan Shi, Sigurdur Ólafsson
      Pages 157-171
    5. Leyuan Shi, Sigurdur Ólafsson
      Pages 173-191
    6. Leyuan Shi, Sigurdur Ólafsson
      Pages 193-206
    7. Leyuan Shi, Sigurdur Ólafsson
      Pages 207-225
    8. Leyuan Shi, Sigurdur Ólafsson
      Pages 227-246
  5. Back Matter
    Pages 247-257

About this book

Introduction

There is increasing need to solve large-scale complex optimization problems in a wide variety of science and engineering applications, including designing telecommunication networks for multimedia transmission, planning and scheduling problems in manufacturing and military operations, or designing nanoscale devices and systems. Advances in technology and information systems have made such optimization problems more and more complicated in terms of size and uncertainty. Nested Partitions Method, Theory and Applications provides a cutting-edge research tool to use for large-scale, complex systems optimization.

 

The Nested Partitions (NP) framework is an innovative mix of traditional optimization methodology and probabilistic assumptions. An important feature of the NP framework is that it combines many well-known optimization techniques, including dynamic programming, mixed integer programming, genetic algorithms and tabu search, while also integrating many problem-specific local search heuristics. The book uses numerous real-world application examples, demonstrating that the resulting hybrid algorithms are much more robust and efficient than a single stand-alone heuristic or optimization technique. This book aims to provide an optimization framework with which researchers will be able to discover and develop new hybrid optimization methods for successful application of real optimization problems.

 

Researchers and practitioners in management science, industrial engineering, economics, computer science, and environmental science will find this book valuable in their research and study. Because of its emphasis on practical applications, the book can appropriately be used as a textbook in a graduate course.

 

Keywords

Manufacturing Multimedia Scheduling Stochastic Optimization algorithm algorithms combinatorial optimization communication complex systems data mining genetic algorithms meta-heuristics mixed integer programming optimization programming

Authors and affiliations

  • Leyuan Shi
    • 1
  • Sigurdur Ólafsson
    • 2
  1. 1.University of Wisconsin-MadisonUSA
  2. 2.Iowa State UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-71909-2
  • Copyright Information Springer US 2009
  • Publisher Name Springer, Boston, MA
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-71908-5
  • Online ISBN 978-0-387-71909-2
  • Series Print ISSN 0884-8289
  • About this book