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Theory and Practice of Uncertain Programming

  • Book
  • © 2002

Overview

Authors:
  1. Baoding Liu

    1. Uncertain Systems Laboratory, Department of Mathematical Sciences, Tsinghua University, Beijing, China

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  • Most comprehensive and up-to-date information on uncertain programming theory
  • Includes theory and methodologies, modeling ideas and applications in practice
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 102)

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Table of contents (24 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Fundamentals

    1. Front Matter

      Pages 1-1
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    2. Mathematical Programming

      • Baoding Liu
      Pages 3-16

    3. Genetic Algorithms

      • Baoding Liu
      Pages 17-27

    4. Neural Networks

      • Baoding Liu
      Pages 29-36

  3. Stochastic Programming

    1. Front Matter

      Pages 37-37
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    2. Random Variables

      • Baoding Liu
      Pages 39-58

    3. Stochastic Expected Value Models

      • Baoding Liu
      Pages 59-75

    4. Stochastic Chance-Constrained Programming

      • Baoding Liu
      Pages 77-113

    5. Stochastic Dependent-Chance Programming

      • Baoding Liu
      Pages 115-143

  4. Fuzzy Programming

    1. Front Matter

      Pages 145-145
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    2. Fuzzy Variables

      • Baoding Liu
      Pages 147-165

    3. Fuzzy Expected Value Models

      • Baoding Liu
      Pages 167-176

    4. Fuzzy Chance-Constrained Programming

      • Baoding Liu
      Pages 177-196

    5. Fuzzy Dependent-Chance Programming

      • Baoding Liu
      Pages 197-208

    6. Fuzzy Programming with Fuzzy Decisions

      • Baoding Liu
      Pages 209-222

  5. Rough Programming

    1. Front Matter

      Pages 223-223
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    2. Rough Variables

      • Baoding Liu
      Pages 225-236

    3. Rough Programming

      • Baoding Liu
      Pages 237-248

  6. Fuzzy Random Programming

    1. Front Matter

      Pages 249-249
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Keywords

About this book

Real-life decisions are usually made in the state of uncertainty (randomness, fuzziness, roughness, etc.). How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory. It includes numerous modeling ideas, hybrid intelligent algorithms, and various applications in transportation problem, inventory system, facility location & allocation, capital budgeting, topological optimization, vehicle routing problem, redundancy optimization, and scheduling. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

Authors and Affiliations

  • Uncertain Systems Laboratory, Department of Mathematical Sciences, Tsinghua University, Beijing, China

    Baoding Liu

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