Advertisement

Optimization Models Using Fuzzy Sets and Possibility Theory

  • J. Kacprzyk
  • S. A. Orlovski

Part of the Theory and Decision Library book series (TDLB, volume 4)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Introductory Sections

  3. Advances in Fuzzy Decision Making, Fuzzy Optimization, and Fuzzy Mathematical Programming

    1. Front Matter
      Pages 73-73
    2. Fuzzy Preferences and Choice

      1. Marc Roubens, Philippe Vincke
        Pages 77-90
      2. Serge Ovchinnikov
        Pages 91-98
      3. V. B. Kuz’min, S. I. Travkin
        Pages 99-109
      4. Bernadette Bouchon
        Pages 110-120
    3. Aspects of Fuzzy Decision Making and Optimization

      1. V. I. Glushkov, A. N. Borisov
        Pages 141-153
      2. Didier Dubois, Henri Farreny, Henri Prade
        Pages 171-185
      3. H. Tanaka, J. Watada, K. Asai
        Pages 186-199
    4. Fuzzy Multicriteria Decision Making, Optimization, and Mathematical Programming: Analysis, Solution Procedures, and Interactive Approaches

    5. Fuzzy Network Optimization, Location, Transportation and Resource Allocation Models

      1. Stefan Chanas
        Pages 303-327
      2. John Darzentas
        Pages 328-341
      3. M. Delgado, J. L. Verdegay, M. A. Vila
        Pages 342-358
      4. Jaroslav Ramik, Josef Řimánek
        Pages 359-374
  4. Applications

  5. Back Matter
    Pages 459-463

About this book

Introduction

Optimization is of central concern to a number of discip­ lines. Operations Research and Decision Theory are often consi­ dered to be identical with optimizationo But also in other areas such as engineering design, regional policy, logistics and many others, the search for optimal solutions is one of the prime goals. The methods and models which have been used over the last decades in these areas have primarily been "hard" or "crisp", i. e. the solutions were considered to be either fea­ sible or unfeasible, either above a certain aspiration level or below. This dichotomous structure of methods very often forced the modeller to approximate real problem situations of the more-or-less type by yes-or-no-type models, the solutions of which might turn out not to be the solutions to the real prob­ lems. This is particularly true if the problem under considera­ tion includes vaguely defined relationships, human evaluations, uncertainty due to inconsistent or incomplete evidence, if na­ tural language has to be modelled or if state variables can only be described approximately. Until recently, everything which was not known with cer­ tainty, i. e. which was not known to be either true or false or which was not known to either happen with certainty or to be impossible to occur, was modelled by means of probabilitieso This holds in particular for uncertainties concerning the oc­ currence of events.

Keywords

calculus decision making decision support fuzzy sets linear optimization mathematical programming nonlinear optimization optimization regression regression analysis

Editors and affiliations

  • J. Kacprzyk
    • 1
  • S. A. Orlovski
    • 2
  1. 1.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  2. 2.International Institute for Applied Systems AnalysisLaxenburgAustria

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-009-3869-4
  • Copyright Information Springer Science+Business Media B.V. 1987
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-94-010-8220-4
  • Online ISBN 978-94-009-3869-4
  • Buy this book on publisher's site
Industry Sectors
Telecommunications
Oil, Gas & Geosciences