RESUMEN
The q-rung orthopair fuzzy set (q-ROPFS) is a kind of fuzzy framework that is capable of introducing significantly more fuzzy information than other fuzzy frameworks. The concept of combining information and aggregating it plays a significant part in the multi-criteria decision-making method. However, this new branch has recently attracted scholars from several domains. The goal of this study is to introduce some dynamic q-rung orthopair fuzzy aggregation operators (AOs) for solving multi-period decision-making issues in which all decision information is given by decision makers in the form of "q-rung orthopair fuzzy numbers" (q-ROPFNs) spanning diverse time periods. Einstein AOs are used to provide seamless information fusion, taking this advantage we proposed two new AOs namely, "dynamic q-rung orthopair fuzzy Einstein weighted averaging (DQROPFEWA) operator and dynamic q-rung orthopair fuzzy Einstein weighted geometric (DQROPFEWG) operator". Several attractive features of these AOs are addressed in depth. Additionally, we develop a method for addressing multi-period decision-making problems by using ideal solutions. To demonstrate the suggested approach's use, a numerical example is provided for calculating the impact of "coronavirus disease" 2019 (COVID-19) on everyday living. Finally, a comparison of the proposed and existing studies is performed to establish the efficacy of the proposed method. The given AOs and decision-making technique have broad use in real-world multi-stage decision analysis and dynamic decision analysis.
RESUMEN
This paper aims to give deeper insights into decision making problem based on interval-valued fuzzy soft set (IVFSS). Firstly, a new score function for interval-valued fuzzy number is proposed for tackling the comparison problem. Subsequently, the formulae of information measures (distance measure, similarity measure and entropy) are introduced and their transformation relations are pioneered. Then, the objective weights of various parameters are determined via new entropy method, meanwhile, we develop the combined weights, which can show both the subjective information and the objective information. Moreover, we propose three algorithms to solve interval-valued fuzzy soft decision making problem by Weighted Distance Based Approximation (WDBA), COmbinative Distance-based ASsessment (CODAS) and similarity measure. Finally, the effectiveness and feasibility of approaches are demonstrated by a mine emergency decision making problem. The salient features of the proposed methods, compared to the existing interval-valued fuzzy soft decision making methods, are (1) it can obtain the optimal alternative without counterintuitive phenomena; (2) it has a great power in distinguishing the optimal alternative; and (3) it can avoid the parameter selection problems.