Evidential reasoning approach for multiattribute decision analysis under both fuzzy and interval uncertainty

Authors:
Min Guo;Jian-Bo Yang;Kwai-Sang Chin;Hong-Wei Wang;Xin-Bao Liu
Affiliations:
Key Laboratory of Image Processing and Intelligent Control, Institute of Systems Engineering, Huazhong University of Science and Technology, Wuhan, China;Manchester Business School, The University of Manchester, Manchester, U.K. and School of Management, Hefei University of Technology, Hefei, China;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong;Key Laboratory of Image Processing and Intelligent Control, Institute of Systems Engineering, Huazhong University of Science and Technology, Wuhan, China;School of Management, Hefei University of Technology, Hefei, China
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2009

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Abstract

Many multiple attribute decision analysis (MADA) problems are characterized by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) and the interval grade ER (IER) approaches have been developed in recent years to support the solution of MADA problems with interval uncertainties and local ignorance in decision analysis. In this paper, the ER approach is enhanced to deal with both interval uncertainty and fuzzy beliefs in assessing alternatives on an attribute. In this newly developed fuzzy IER (FIER) approach, local ignorance and grade fuzziness are modeled under the integrated framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A numerical example is provided to illustrate the detailed implementation process of the FIER approach and its validity and applicability.