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Selecting an appropriate robot among the alternative robots is a difficult problem for decision makers, since it is complicated to express attributes as crisp numbers in multiple attribute decision making problems. Generalized trapezoidal hesitant fuzzy numbers(GTHF-numbers) can be efficiently used to estimate information in the decision making process. This paper proposes an advanced type of TOPSIS (Technique for order preference by similarity to ideal solution) method, called TOPSIS method of GTHF-numbers. To do this, we introduce some novel distance measures including Hamming distance measure, Euclidean distance measure, λ-generalized distance measure, λ-generalized Hausdorff distance measure, γ-hybrid Hamming distance, hybrid Euclidean distance and λ-generalized hybrid distance measure on GTHF-numbers. Then, we develop a novel TOPSIS method of GTHF-numbers based on introduced distance measures. Finally, we provide a real example, for an auto company which desires to select a suitable robot for its production process, based on the proposed TOPSIS method of GTHF-numbers to prove the efficiency and the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the picture fuzzy multiple attribute decision making problems where the information about attribute weights is partly known or completely unknown. We introduce some notions, such as picture fuzzy ideal point, the normalized Hamming distance of picture fuzzy numbers. We also introduce the grey relational coefficient between the attribute value vectors of each alternative and the picture fuzzy ideal point. Then we establish the grey relational analysis models to measure the grey relational degree between each alternative and the picture fuzzy ideal point. Based on the grey relational analysis models, we can rank the given alternatives and then select the most desirable one. Finally, we illustrate the developed grey relational analysis models with a numerical example for potential evaluation of emerging technology commercialization. [ABSTRACT FROM AUTHOR]