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Modeling fuzzy intervals with constraint logic programming

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Warianty tytułu
Konferencja
Evolutionary Computation and Global Optimization 2008 / National Conference (11 ; 2-4.06.2008 ; Szymbark, Poland)
Języki publikacji
EN
Abstrakty
EN
In this paper the method of modeling fuzzy intervals in fuzzy decision-making is presented. Described method makes use of constraint logic programming and it is based on the concept of descriptors. This approach is very general and it is consistent with Zadeh's extension principle and Bellman-Zadeh concept of fuzzy decision making. It fulfills Klir's requisite constraint and deals effectively with a drowning effect too. The idea of descriptors of fuzzy intervals and fuzzy constraints is illustrated with computational example of flexible scheduling problem in which robust for drowning effect schedule is found.
Rocznik
Tom
Strony
9--22
Opis fizyczny
Bibliogr. 15 poz., tab., rys., wykr.
Twórcy
Bibliografia
  • [1] Richard Bellman and Lofti Zadeh. Decision-making in a fuzzy environment. Management Science, 17(4):141-164, 1970.
  • [2] Rina Dechter. Constraint processing. Morgan Kaufmann Publishers, 2003.
  • [3] Didier Dubois, Helen Fargier, and Henri Prade. Fuzzy Sets, Neural Networks and Soft Computing, chapter Propagation and satisfaction of flexible constraints, pages 166-187. Kluwer Academic Publishers, 1994.
  • [4] Didier Dubois, Helene Fargier, and Henri Prade. Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty. Applied Intelligence, 4:287-309, 1996.
  • [5] Didier Dubois and Philippe Fortemps. Computing improved optimal solution to max-min flexible constraint satisfaction problems. European Journal of Operational Research, 118:95-126, 1999.
  • [6] Didier Dubois and Henri Prade. Possibility Theory. An Approach to Computerized Processing of Uncertainty. Plenum Press, 1988.
  • [7] Christian Holzbaur. OFAI clp(q, r) manual. Technical Report TR-95-09, Austrian Research Institute for Artificial Intelligence, Vienna, 1995.
  • [8] George J. Klir. Fuzzy arithmetic with requisite constraints. Fuzzy Sets and Systems, 91:165-175, 1997.
  • [9] Przemysław Kobylański. Soft Computing - Tools, Techniques and Applications, chapter Improving fuzzy solutions with constraint programming, pages 119-133. Akademicka Oficyna Wydawnicza EXIT, 2004.
  • [10] Przemysław Kobylański and Michał Kulej. Improved solutions for vehicle routing and scheduling with fuzzy time windows and fuzzy goal. Badania Operacyjne i Decyzje, 4, 2003.
  • [11] Przemysław Kobylański and Paweł Zieliński. Fuzzy modeling with constraint technology. In Proceedings of EUROFUSE 2002, 7th Meeting of the EURO Working Group on Fuzzy Sets, Workshop on Information Systems, 2002.
  • [12] Xudong Luo, Jimmy Ho-man Lee, Ho-fung Leung, and Nicholas R. Jennings. Prioritised fuzzy constraint satisfaction problems: axioms, instantiation and validation. Fuzzy Sets and Systems, 136:151-188, 2003.
  • [13] H.T. Nguyen. A note on the extension principle for fuzzy sets. J. Math. Anal. Appl., 68:369-380, 1978.
  • [14] Ulf Nilsson and Jan Maluszynski. Logic, Programming and Prolog. John Wiley & Sons Ltd., 1995.
  • [15] L.A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning. American Elsevier Publishing Co., 1973.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA9-0035-0001
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