Research On Multi-attribute Decision-making Method Based On Interval-valued Intuitionistic Uncertain Linguistic Information

Multiple attribute decision making(MADM) is an new research field in several disciplines, such as the modern decision science, system engineering, management science and operations research. Its theory and method are widely applied to the political, economic, cultural and technological areas. However, the decision makers(DMs) are difficult to make certain, reasonable and objective decision because of complexity and uncertainty of the decision problems, limited knowledge of decision makers and fuzziness of thought. With the emergence of MADM of the intuitionistic fuzzy set(IFS) and uncertain linguistic information(ULI), it’s easy to solve the uncertainty of decision-making. In order to extend the application range of the IFS, this paper presents the concepts and properties of the interval-valued intuitionistic uncertain linguistic information(IVIULI) based on the theories of the interval-valued intuitionistic fuzzy set(IVIFS) and the intuitionistic uncertain linguistic set(IULS). The basic theory and method of IVIULI are studied in this paper. The research results of this paper stands out as follows:(1) The concepts, operation rules and properties of IVIULI are proposed, including the score function,the expectation,the normal Hamming distance and the comparison method.(2) Due to introducing IVIULI to GOWA, GHWA and the dependent operators, IVIULGOWA, IVIULGOWA, IVIULGHWA, IVIULWGA and IVIULDHG operators are developed. We study the properties of those operators and the method of MADM based on operators.(3) According to the subjective and objective preference of the MADM problems, the TOPSIS method based on both non-preference and known attribute weights and the MADM method of IVIULI based on both preference and unknown attribute weights are proposed.(4) According to the unknown attribute weight and the existence of subjective and objective weighting method of the MADM problems, the objective weighting method based on the maximizing deviation method and the grey relational analysis approach based on combination weighting approach in MADM are proposed.