Interval grey numbers based multi-attribute decision making method for supplier selection

Abstract

Purpose -The purpose of this paper is to study a novel grey possibility degree approach, which is combined with multi-attribute decision making (MADM) and applied MADM model for solving supplier selection problem under uncertainty information. Design/methodology/approach -The supplier selection problem is a typical MADM problem, in which information of a series of indexes should be aggregated. However, it is relatively easy for decision makers to define information in uncertainty, sometimes as a grey number, rather than a precise number. By transforming linguistic scale of rating supplier selection attributes into interval grey numbers, a novel grey MADM method is developed. Steps of proposed model were provided, and a novel grey possibility degree approach was proposed. Finally, a numerical example of supplier selection is utilized to demonstrate the proposed approach. Findings -The results show that the proposed approach could solve the uncertainty decision-making problem. A numerical example of supplier selection is utilized to demonstrate the proposed approach. The results show that the proposed method is useful to aggregate decision makers' information so as to select the potential supplier. Practical implications -The approach constructed in the paper can be used to solving uncertainty decision-making problems that the certain value of the decision information could not collect while the interval value set could be defined. Obviously it can be utilized for other MADM problem. Originality/value -The paper succeeded in redefining interval grey number, constructing a novel interval grey number based MADM approach and providing the solution of the proposed approach. It is very useful to solving system forecasting problem and it contributed undoubtedly to improve grey decision-making models.

A New Methodology of Multicriteria Decision-Making in Supplier Selection Based onZ-Numbers

“…-number theory takes the dependence of information into consideration, which can be useful in the framework with nonexclusive hypotheses [28,33,34]. Grey system theory can handle the sensitivity of the system efficiently for the interval number that was used to assess the importance weights of strategic and operational criterion [35,36]. However, they cannot deal with the reliability of decision system efficiently.…”

“…= ( 2 , 2 , 2 ), and 3 = ( 3 , 3 , 3 ); then the objective function according to(36) can be established. After the procedure of selection, mating, crossover, and mutation of genetic algorithm, the optimal fuzzy weight can be figured out as̃=…”

Fuzzy Risk Analysis for a Production System Based on the Nagel Point of a Triangle

“…The -cut ( -level set) of the fuzzy set̃is given by Hereafter, lower case lettering such as̃will be used to denote fuzzy numbers. Generally, certain special types of fuzzy numbers, such as triangular, trapezoidal, and -fuzzy numbers, are used for real life applications (e.g., see [1][2][3][4][5][6][7][8][9][10] and references therein).…”

“…Let̃= (0, 2, 4) ,̃=(1,4,5) , and̃= (1, 2, 3) be triangular fuzzy numbers and let̃= (5, 5, 5) be a crisp number (seeFigure 3). Then using(14) and Proposition 3 we get̃=(2, 0.0557) , = (2.2519, 0.0672) , = (2, 0.1716) , = (5, 1) .…”

Üçgeni̇n Gergonne Noktasi Yardimiyla Üçgensel Fuzzy Sayilari Siralama Yöntemi̇

“…Fuzzy sayıların sıralanması risk analizi, optimizasyon, sosyal ve ekonomik sistemler, hava tahmini gibi birçok günlük problemde önemli bir yer tutmaktadır (bkz. [1][2][3][4][5][6][7][8][9]).…”