Hesitant interval‐valued Pythagorean fuzzy VIKOR method

In this article, we investigate multiple attribute decision‐making problems with hesitant interval‐valued Pythagorean fuzzy information. First, the concepts of hesitant interval‐valued Pythagorean fuzzy set are defined, and the operation laws, the score function, and accuracy function have been developed. Then several distance measures for hesitant interval‐valued Pythagorean fuzzy values have been presented including the Hamming distance, Euclidean distance, and generalized distance, and so on. Based on the operational laws, a series of aggregation operators have been developed including the hesitant interval‐valued Pythagorean fuzzy weighted averaging (HIVPFWA) operator, the hesitant interval‐valued Pythagorean fuzzy geometric weighted averaging (HIVPFGWA) operator, the hesitant interval‐valued Pythagorean fuzzy ordered weighed averaging (HIVPFOWA) operator, and hesitant interval‐valued Pythagorean fuzzy ordered weighed geometric averaging (HIVPFOWGA) operator. By using the generalized mean operator, we also develop the generalized hesitant interval‐valued Pythagorean fuzzy weighed averaging (GHIVPFWA) operator, the generalized hesitant interval‐valued Pythagorean fuzzy weighed geometric averaging (GHIVPFWGA) operator, the generalized hesitant interval‐valued Pythagorean fuzzy ordered weighted averaging (GHIVPFOWA) operator, and generalized hesitant interval‐valued Pythagorean fuzzy ordered weighted geometric averaging (GHIVPFOWGA) operator operator. We further develop several hybrid aggregation operators including the hesitant interval‐valued Pythagorean fuzzy hybrid averaging (HIVPFHA) operator and the generalized hesitant interval‐valued Pythagorean fuzzy hybrid averaging (GHIVPFHA) operator. Based on the distance measures and the aggregation operators, we propose a hesitant interval‐valued Pythagorean fuzzy VIKOR method to solve multiple attribute decision problems with multiple periods. Finally, an illustrative example for evaluating the metro project risk is given to demonstrate the feasibility and effectiveness of the proposed method.

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