Minimum Deviation Models for Multiple Attribute Decision Making in Intuitionistic Fuzzy Setting

Abstract

With respect to intuitionistic fuzzy multiple attribute decision making problems with preference information on alternatives and incomplete weight information, a method based the minimum deviation is proposed.Firstly, some operational laws of intuitionistic fuzzy numbers, score function and accuracy function of intuitionistic fuzzy numbers are introduced.Then, to reflect the decision maker's preference information, an optimization model based on the minimum deviation method, by which the attribute weights can be determined, is established.For the special situations where the information about attribute weights is completely unknown, we establish another optimization model.By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights.We utilize the intuitionistic fuzzy weighted averaging (IFWA) operator to aggregate the intuitionistic fuzzy information corresponding to each alternative, and then rank the alternatives and select the most desirable one(s) according to the score function and accuracy function.The method can sufficiently utilize the objective information, and meet decision makers' subjective preference, can also be easily performed on computer.Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 31 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.