Aggregation and Interaction Aggregation Soft Operators on Interval-Valued q-Rung Orthopair Fuzzy Soft Environment and Application in Automation Company Evaluation

Abstract

In several practical decision procedures, it is not accessible to denote assessments by a single crisp number due to a lack of information. However, representing information by an interval number within [0, 1] is a more credible approach. In multi-criteria decision making (MCDM) such an interval number can significantly catch information. In addition, the combination of soft sets with intervalvalued q-rung orthopair fuzzy sets can be viewed as interval-valued q-rung orthopair fuzzy soft sets (IVq-ROFSs). It can be a reliable tool to cope with uncertainties. Usually, aggregation operators are functional in MCDM techniques; therefore, aggregation operators on IVq-ROFSs can significantly aggregate pieces of information in intervals with IVq-ROFSs. In this paper, we investigated some crucial properties of interval valued q-rung orthopair fuzzy soft sets (IVq-ROFSSs) and expressed a different representation of IVq-ROFSS in the form of IVq-ROFS number. Based on this representation, we investigated IVq-ROF weighted averaging, IVq-ROF weighted geometric operators and given their basic properties. Moreover, we consider interactions between non-memberships and memberships of different interval-valued q-rung orthopair fuzzy values and defined IVq-ROF weighted interaction averaging, IVq-ROF weighted interaction geometric aggregation operators in IVq-ROFS environments. A decision-making process is given, and an illustration is provided by tackling application in automation company evaluation.

Shared manufacturing service evaluation based on intuitionistic fuzzy VIKOR

“…The next step will be an in-depth study of "many-to-many" matching between shared manufacturing services and tasks. Secondly, we need to find new ways to adapt to complex situations, such as GGIFSSs proposed by Hayat K et al [ 33 ] and IVq-ROFSSs proposed by Yang et al [ 34 ]. Thirdly, another evaluation algorithm is developed to address the evaluation problem of shared manufacturing services.…”

Assessing indoor positioning system: A q-spherical fuzzy rough TOPSIS analysis

“…Moreover, Mathew and Thomas [ 63 ] explore interval-valued multi-criteria decision-making methods, which could potentially accommodate uncertainties in decision criteria. In a broader scope, the research by Hayat et al [ 64 ] on new aggregation operators for group-based generalized intuitionistic fuzzy soft sets, and Yang et al's [ 65 ] work on aggregation operators for interval-valued q-Rung Orthopair fuzzy soft environment with applications in automation company evaluation, underscore the importance of innovative aggregation techniques in handling complex and diverse data structures. Additionally, Mehmood et al [ 66 ] propose a multi-criteria decision-making method for cubic hesitant fuzzy sets based on Einstein's operational laws.…”

An integrated decision-making methodology for green supplier selection based on the improved IVIF-CPT-MABAC method

“…When α = 0.88, ρ = 2.25 and the parameter in weighting function are constant, and the overall distance and ranking results of alternatives with different value of parameter β are shown in Table 4. 25.…”

“…Besides, the latest research on IFSs and IVIFSs in MADM is enough to attract everyone's attention. For instance, Hayat et al proposed the new aggregation operators on group-based generalized intuitionistic fuzzy soft sets [20]; Karaaslan explored new aggregation operators on group-based generalized intuitionistic fuzzy soft sets from a new perspective [21]; Hayat et al applied the new group-based generalized interval-valued q-rung orthopair fuzzy soft aggregation operator to sports decision-making problems, and extended the generalized intuitionistic fuzzy soft set [22][23][24]; Yang et al explored the application of aggregation and interactive aggregation soft operators under the interval-valued q-order orthomorphic fuzzy soft environment in the evaluation of automation companies [25] and so on. Meanwhile, many MADM methods have been developed into IVIFSs to solve the practical MADM problems.…”