A general type-2 fuzzy logic based approach for Multi-Criteria Group Decision Making

Decision making could be viewed to include Multi-Criteria Group Decision Making (MCGDM). MCGDM is a decision tool which it is able to find a unique agreement from number of decision makers/users by evaluating the uncertain judgment among them. Several fuzzy logic based approaches have been employed in MCGDM to handle the linguistic uncertainties and hesitancy. However, there is a need to handle the high level of uncertainties that exist in decision making problems involving numbers of decision makers/experts/users with varying points of view. In this paper, we present a general type-2 fuzzy logic based approach for MCGDM. The proposed system aims to handle the high levels of uncertainties which exist due to the varying Decision Makers' (DMs) judgments and the vagueness of the appraisal. The proposed method utilizes general type-2 fuzzy sets. The aggregation operation in the proposed method aggregates the various DMs opinions which allow handling the disagreements of DMs' opinions into a unique approval. We will present results from the proposed system deployment for the assessment of the postgraduate study. The proposed system was able to model the variation in the group decision making process exhibited by the various decision makers' opinions. In addition, the proposed system showed agreement between the proposed method and the real decision outputs from DMs (as quantified by the Pearson Correlation) which outperformed the MCGDM systems based on type-1 fuzzy sets, interval type-2 fuzzy sets and interval type-2 fuzzy sets with hesitation index.

[1]  Milos Manic,et al.  General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering , 2012, IEEE Transactions on Fuzzy Systems.

[2]  Hani Hagras,et al.  A hybrid approach for Multi-Criteria Group Decision Making based on interval type-2 fuzzy logic and Intuitionistic Fuzzy evaluation , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[3]  Yong Qin,et al.  Multi-attribute group decision making models under interval type-2 fuzzy environment , 2012, Knowl. Based Syst..

[4]  Shyi-Ming Chen,et al.  Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets , 2012, Expert Syst. Appl..

[5]  Francisco Herrera,et al.  Hesitant Fuzzy Linguistic Term Sets for Decision Making , 2012, IEEE Transactions on Fuzzy Systems.

[6]  Hani Hagras,et al.  Toward General Type-2 Fuzzy Logic Systems Based on zSlices , 2010, IEEE Transactions on Fuzzy Systems.

[7]  Shyi-Ming Chen,et al.  Fuzzy Multiple Criteria Hierarchical Group Decision-Making Based on Interval Type-2 Fuzzy Sets , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Shyi-Ming Chen,et al.  Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method , 2010, Expert Syst. Appl..

[9]  Hani Hagras,et al.  Novel Methods for the Design of General Type-2 Fuzzy Sets based on Device Characteristics and Linguistic Labels Surveys , 2009, IFSA/EUSFLAT Conf..

[10]  Hiroaki Ishii,et al.  A Type-2 Fuzzy Portfolio Selection Problem Considering Possibility Measure and Crisp Possibilistic Mean Value , 2009, IFSA/EUSFLAT Conf..

[11]  Diyar Akay,et al.  A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method , 2009, Expert Syst. Appl..

[12]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[13]  M. Ramachandran,et al.  Application of multi-criteria decision making to sustainable energy planning--A review , 2004 .

[14]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[15]  Francisco Herrera,et al.  Linguistic decision analysis: steps for solving decision problems under linguistic information , 2000, Fuzzy Sets Syst..

[16]  Jerry M. Mendel,et al.  Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[17]  Jonathan M. Garibaldi,et al.  The development and implementation of an expert system for the analysis of umbilical cord blood , 1997, Artif. Intell. Medicine.

[18]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[19]  R. Yager Fuzzy decision making including unequal objectives , 1978 .

[20]  H. Hagras,et al.  Type-2 Fuzzy Logic in Multi-Criteria Group Decision Making with Intuitionistic Evaluation , 2011 .

[21]  Shyi-Ming Chen,et al.  Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets , 2010, Expert Syst. Appl..

[22]  J. Mendel Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .