Fuzzy linear programming via simulated annealing

Rita Almeida Ribeiro; Fernando Moura Pires

Kybernetika (1999)

  • Volume: 35, Issue: 1, page [57]-67
  • ISSN: 0023-5954

Abstract

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This paper shows how the simulated annealing (SA) algorithm provides a simple tool for solving fuzzy optimization problems. Often, the issue is not so much how to fuzzify or remove the conceptual imprecision, but which tools enable simple solutions for these intrinsically uncertain problems. A well-known linear programming example is used to discuss the suitability of the SA algorithm for solving fuzzy optimization problems.

How to cite

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Ribeiro, Rita Almeida, and Pires, Fernando Moura. "Fuzzy linear programming via simulated annealing." Kybernetika 35.1 (1999): [57]-67. <http://eudml.org/doc/33409>.

@article{Ribeiro1999,
abstract = {This paper shows how the simulated annealing (SA) algorithm provides a simple tool for solving fuzzy optimization problems. Often, the issue is not so much how to fuzzify or remove the conceptual imprecision, but which tools enable simple solutions for these intrinsically uncertain problems. A well-known linear programming example is used to discuss the suitability of the SA algorithm for solving fuzzy optimization problems.},
author = {Ribeiro, Rita Almeida, Pires, Fernando Moura},
journal = {Kybernetika},
keywords = {fuzzy optimization; simulated annealing; fuzzy optimization; simulated annealing},
language = {eng},
number = {1},
pages = {[57]-67},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Fuzzy linear programming via simulated annealing},
url = {http://eudml.org/doc/33409},
volume = {35},
year = {1999},
}

TY - JOUR
AU - Ribeiro, Rita Almeida
AU - Pires, Fernando Moura
TI - Fuzzy linear programming via simulated annealing
JO - Kybernetika
PY - 1999
PB - Institute of Information Theory and Automation AS CR
VL - 35
IS - 1
SP - [57]
EP - 67
AB - This paper shows how the simulated annealing (SA) algorithm provides a simple tool for solving fuzzy optimization problems. Often, the issue is not so much how to fuzzify or remove the conceptual imprecision, but which tools enable simple solutions for these intrinsically uncertain problems. A well-known linear programming example is used to discuss the suitability of the SA algorithm for solving fuzzy optimization problems.
LA - eng
KW - fuzzy optimization; simulated annealing; fuzzy optimization; simulated annealing
UR - http://eudml.org/doc/33409
ER -

References

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  8. Lai Y.-J., Hwang C.-L., Fuzzy Multiple Objective Decision Making, (Lecture Notes in Economics and Mathematical Systems.) Springer–Verlag, Berlin 1994 Zbl0823.90070MR1266628
  9. Pires F. M., Moura J. Pires, Ribeiro R. A., Solving fuzzy optimisation problems: Flexible approaches using simulated annealing, In: ISSCI’96, Montpelier 1996 
  10. Ribeiro R. A., Pires F. M., Fuzzy site location problems and simulated annealing, In: Series Studies in Locational Analysis (B. Boffey and E. Declerque, eds.), to appear 
  11. Zeleny M., Fuzziness, knowledge and optimization: New optimality concepts, In: Fuzzy Optimization (M. Delgado, J. Kacprzyk, J.-L. Verdegay and M. A. Vila, eds.), Physica–Verlag, Berlin 1994 Zbl0826.90137MR1315053
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  13. Zimmermann H.-J., Fuzzy Set Theory and its Applications, Third edition. Kluwer, Boston 1986 Zbl0984.03042MR0814498

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