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Intuitionistic Fuzzy Multi-Period Dynamic Assessment (MP-DAS) Method: Renewable Energy Selection Application
Nurşah Alkan
Real-world multi-criteria problems can be solved using multi-criteria decision-making (MCDM) techniques that allow a variety of qualitative and quantitative aspects to be taken into account simultaneously. There have been an increasing number of theories and approaches created for solving distinct MCDM situations with proportionate and conflicting aspects. Judgments about alternatives considered in MCDM methods may change according to the circumstances and conditions that may occur in the future. Many existing decision-making approaches do not take this situation into account, which may cause wrong decisions. Therefore, it is necessary to create adaptable decision models that make use of both current and future information in order to handle a dynamic decision system. The novelty of this study is to develop a novel MCDM method and its intuitionistic fuzzy set extension, Multi-Period Dynamic ASsessment (MP-DAS) and Intuitionistic Fuzzy Multi-Period Dynamic ASsessment (IF MP-DAS), to provide a dynamic decision approach based on present and future information simultaneously. The objective is to present a dynamic-based MCDM method with as many future time points as experts prefer and a stronger multi-measurement system. By concentrating on medium- and long-term judgments that take into account potential variations of the addressed components, the established method enables more informed decisions to be made for complicated problems. In the study, a multi-measurement system is developed by using both Euclidean distance and distance from average solution. The proposed dynamic decision method can be applied by managers and decision-makers as a decision support system. To show the efficiency and applicability of the developed method, a renewable energy source selection problem is handled, where the assessments include possible variations for both developed methods. A comprehensive sensitivity analysis is conducted to confirm the effectiveness and stability of the method for the developed methods. The results of the comparison analysis with distance-based MCDM and intuitionistic fuzzy distance-based MCDM methods showed that the developed method is superior to other MCDM methods.
Keywords: MCDM, intuitionistic fuzzy sets, dynamic data, multi-period, distance measures, renewable energy