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Rizwan Gul 한국지능시스템학회 2022 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.22 No.3
The rough set (RS) theory is a successful approach for studying the uncertainty in data. Incontrast, the bipolar soft sets (BSS) can deal with the uncertainty, as well as bipolarity ofthe data in many situations. In 2018, Karaaslan and C¸ agman proposed bipolar soft rough ˘sets (BSRSs), a hybridization of RS and BSS. However, certain shortcomings with BSRSviolate Pawlak’s RS theory. To overcome these shortcomings, the concept of the modifiedbipolar soft rough set (MBSRS) has been proposed in this study. Moreover, this idea hasbeen investigated through illustrative examples, where the important properties are inspecteddeeply. Furthermore, certain significant measures associated with MBSRS are also provided. Finally, an application of the MBSRS to multi-attribute group decision-making (MAGDM)problems is proposed. In addition, among various alternatives, an algorithm for decisionmaking accompanied by a practical example is presented as the optimal alternative . A briefcomparative analysis of the proposed approach with some existing techniques is also providedto indicate the validity, flexibility, and superiority of the suggested MAGDM model.
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( Junda Qiu ),( Lei Li ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.7
A number of effective methods for multiple-attribute group decision making (MAGDM) with interval-valued intuitionistic fuzzy numbers (IVIFNs) have been proposed in recent years. However, the different methods frequently yield different, even sometimes contradictory, results for the same problem. In this paper a novel criterion to determine the advantages and disadvantages of different methods is proposed. First, the decision-making process is divided into three parts: translation of experts’ preferences, aggregation of experts’ opinions, and comparison of the alternatives. Experts’ preferences aggregation is considered the core step, and the quality of the collective matrix is considered the most important evaluation index for the aggregation methods. Then, methods to calculate the similarity measure, correlation, correlation coefficient, and energy of the intuitionistic fuzzy matrices are proposed, which are employed to evaluate the collective matrix. Thus, the optimal method can be selected by comparing the collective matrices when all the methods yield different results. Finally, a novel approach for aggregating experts’ preferences with IVIFN is presented. In this approach, experts’ preferences are mapped as points into two-dimensional planes, with the plant growth simulation algorithm (PGSA) being employed to calculate the optimal rally points, which are inversely mapped to IVIFNs to establish the collective matrix. In the study, four different methods are used to address one example problem to illustrate the feasibility and effectiveness of the proposed approach.
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