Dynamic intuitionistic fuzzy multi-attribute decision making

  • Authors:

  • Zeshui Xu;Ronald R. Yager

  • Affiliations:

  • Antai School of Economic and Management, Shanghai Jaotong University, Shanghai 200052, China;Machine Intelligence Institute, Iona College, New Rochelle, NY 10801, United States

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2008

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Abstract

The dynamic multi-attribute decision making problems with intuitionistic fuzzy information are investigated. The notions of intuitionistic fuzzy variable and uncertain intuitionistic fuzzy variable are defined, and two new aggregation operators: dynamic intuitionistic fuzzy weighted averaging (DIFWA) operator and uncertain dynamic intuitionistic fuzzy weighted averaging (UDIFWA) operator are presented. Some methods, including the basic unit-interval monotonic (BUM) function based method, normal distribution based method, exponential distribution based method and average age method, are introduced to determine the weight vectors associated with these operators. A procedure based on the DIFWA operator is developed to solve the dynamic intuitionistic fuzzy multi-attribute decision making (DIF-MADM) problems where all the decision information about attribute values takes the form of intuitionistic fuzzy numbers collected at different periods, and a procedure based on the UDIFWA operator is developed for DIF-MADM under interval uncertainty in which all the decision information about attribute values takes the form of interval-valued intuitionistic fuzzy numbers collected at different periods. Finally, a practical case is used to illustrate the developed procedures.