Fuzzy PROMETHEE

Chapter
Part of the Green Energy and Technology book series (GREEN)

Abstract

In many decision-making problems the decision maker’s judgments are not crisp, and it is relatively difficult for the decision maker to provide precise numerical values for the criteria or attributes. These kinds of criteria make the evaluation process hard and vague. To deal with vagueness of human thought Zadeh [1] first introduced the fuzzy set theory, which was oriented to the rationality of uncertainty due to imprecision or vagueness. A major contribution of fuzzy set theory is its capability of representing vague data. In a classical set, an element belongs to, or does not belong to, a set whereas an element of a fuzzy set naturally belongs to the set with a membership value from the interval [0,1]. In this chapter, the procedure of the PROMETHEE method described in  Chap. 5 will be applied making the assumption that the performance of alternative solutions are fuzzy while the preferences of the decision-maker, such as the parameters of generalized criteria and the weighting factors, are not. That is, the performance of alternative solutions can be determined only approximately and therefore is introduced into the calculations as a fuzzy number.

Keywords

Enthalpy 

References

  1. 1.
    Zadeh LA (1965) Fussy sets. Inform Control 8:338–353CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Dubois D, Prade H (1978) Operations on fuzzy numbers. Int J Syst Sci 9:613–626CrossRefMATHMathSciNetGoogle Scholar
  3. 3.
    Goumas M, Lygerou V (2000) An extension of the PROMETHEE method for decision making in fuzzy environment: ranking of alternative energy exploitation projects. Eur J Oper Res 123:606–613CrossRefMATHGoogle Scholar
  4. 4.
    Yager RR (1981) A procedure for ordering fuzzy subsets of the unit interval. Inform Sci 24:143–161CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Zhang K, Kluck C, Achari G (2009) A comparative approach for ranking contaminated sites based on the risk assessment paradigm using fuzzy PROMETHEE. Environ Manage 44(5):952–967CrossRefGoogle Scholar
  6. 6.
    Aloini D, Dulmin R, Mininno V (2010) A hybrid fuzzy-promethee method for logistics service selection. Int J Uncertain Fuzz 18(4):345–369CrossRefGoogle Scholar
  7. 7.
    TuzkayaG Gülsün B, Kahraman C, Ozgen D (2010) An integrated fuzzy multi-criteria decision making methodology for material handling equipment selection problem and an application. Expert Syst Appl 37(4):2853–2863CrossRefGoogle Scholar
  8. 8.
    Yilmaz B, Dagdeviren M (2011) A combined approach for equipment selection: F-PROMETHEE method and zero-one goal programming. Expert Syst Appl 38(9):11641–11650CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Escuela Técnica Superior de NáuticaUniversity of CantabriaSantanderSpain

Personalised recommendations