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

DOI

10.2991/ijcis.2011.4.2.6How to use a DOI?

Keywords

Multiple attribute decision-making; Intuitionistic fuzzy numbers; Intuitionistic fuzzy weighted averaging (IFWA) operator; Weight information, Preference

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.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Guiwu Wei
AU  - Xiaofei Zhao
PY  - 2011
DA  - 2011/04/01
TI  - Minimum Deviation Models for Multiple Attribute Decision Making in Intuitionistic Fuzzy Setting
JO  - International Journal of Computational Intelligence Systems
SP  - 174
EP  - 183
VL  - 4
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.2.6
DO  - 10.2991/ijcis.2011.4.2.6
ID  - Wei2011
ER  -