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The operations of t‐norm (TN) and t‐conorm (TCN), developed by Dombi, are generally known as Dombi operations, which have an advantage of flexibility within the working behavior of parameter. In this paper, we use Dombi operations to construct a few Q‐rung orthopair fuzzy Dombi aggregation operators: Q‐rung orthopair fuzzy Dombi weighted average operator, Q‐rung orthopair fuzzy Dombi order weighted average operator, Q‐rung orthopair fuzzy Dombi hybrid weighted average operator, Q‐rung orthopair fuzzy Dombi weighted geometric operator, Q‐rung orthopair fuzzy Dombi order weighted geometric operator, and Q‐rung orthopair fuzzy Dombi hybrid weighted geometric operator. The different features of these proposed operators are reviewed. At that point, we have used these operators to build up a model to solve the multiple‐attribute decision making issues under Q‐rung orthopair fuzzy environment. Ultimately, a realistic instance is stated to substantiate the created model and to exhibit its applicability and viability.

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The operations of t‐norm and t‐conorm, developed by Dombi, were generally known as Dombi operations, which may have a better expression of application if they are presented in a new form of flexibility within the general parameter. In this paper, we use Dombi operations to create a few Pythagorean fuzzy Dombi aggregation operators: Pythagorean fuzzy Dombi weighted average operator, Pythagorean fuzzy Dombi order weighted average operator, Pythagorean fuzzy Dombi hybrid weighted average operator, Pythagorean fuzzy Dombi weighted geometric operator, Pythagorean fuzzy Dombi order weighted geometric operator, and Pythagorean fuzzy Dombi hybrid weighted geometric operator. The distinguished feature of these proposed operators is examined. At that point, we have used these operators to build up a model to remedy the multiple attribute decision‐making issues under Pythagorean fuzzy environment. Ultimately, a realistic instance is stated to substantiate the created model and to exhibit its applicability and viability.

KEYWORDSInterval trapezoidal neutrosophic set; ITNNWAA operator; ITNNWG operator; Multi-attribute decision making.Abstract. The aim of this paper is to introduce an interval trapezoidal neutrosophic set, which is a combination of trapezoidal fuzzy numbers, and an interval neutrosophic set. This paper presents some operational rules and the score and accuracy functions of interval trapezoidal neutrosophic numbers. Then, some aggregation operators based on interval trapezoidal neutrosophic information are proposed: the Interval Trapezoidal Neutrosophic Number Weighted Arithmetic Averaging (ITNNWAA) operator and the Interval Trapezoidal Neutrosophic Number Weighted Geometric Averaging (ITNNWGA) operator; in addition, the properties of these operators are investigated in detail. Furthermore, a multi-attribute decision-making method is developed based on the operators. Finally, a numerical example is presented to illustrate the applicability and e ectiveness of the proposed method.

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