Induced generalized intuitionistic fuzzy OWA operator for multi-attribute group decision making

  • Authors:

  • Zhi-xin Su;Guo-ping Xia;Ming-yuan Chen;Li Wang

  • Affiliations:

  • School of Economics and Management, Beihang University, No.37 Xueyuan Road, Haidian District, Beijing 100191, PR China;School of Economics and Management, Beihang University, No.37 Xueyuan Road, Haidian District, Beijing 100191, PR China;Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve W., Montreal, Quebec, Canada H3G 1M8;School of Economics and Management, Beihang University, No.37 Xueyuan Road, Haidian District, Beijing 100191, PR China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

With respect to multi-attribute group decision making (MAGDM) problems in which both the attribute weights and the decision makers (DMs) weights take the form of real numbers, attribute values provided by the DMs take the form of intuitionistic fuzzy numbers, a new group decision making method is developed. Some operational laws, score function and accuracy function of intuitionistic fuzzy numbers are introduced at first. Then a new aggregation operator called induced generalized intuitionistic fuzzy ordered weighted averaging (IG-IFOWA) operator is proposed, which extend the induced generalized ordered weighted averaging (IGOWA) operator introduced by Merigo and Gil-Lafuente [Merigo, J. M., & Gil-Lafuente, A. M. (2009). The induced generalized OWA operator. Information Sciences, 179, 729-741] to accommodate the environment in which the given arguments are intuitionistic fuzzy sets that are characterized by a membership function and a non-membership function. Some desirable properties of the IG-IFOWA operator are studied, such as commutativity, idempotency, monotonicity and boundary. And then, an approach based on the IG-IFOWA and IFWA (intuitionistic fuzzy weighted averaging) operators is developed to solve MAGDM problems with intuitionistic fuzzy information. Finally, a numerical example is used to illustrate the developed approach.