A novel approach to extended fuzzy TOPSIS based on new divergence measures for renewable energy sources selection

In recent years, the selection of appropriate renewable energy sources is an extremely significant issue that affects the environmental development and economic growth. To tackle the concern, various authors have concentrated on preferring desirable energy source(s) adopting decision-making approaches under different fuzzy sets methods. In this regard, in the present study, a new divergence measure is proposed for ranking and choosing the renewable energy sources in multi-criteria decision-making problems based on fuzzy TOPSIS, and it is compared to some existing methods. Then, a set of experts related to renewable energy sources is selected to evaluate possible alternatives amongst conflicting criteria. Moreover, the fuzzy decision matrix and criteria weights are measured using linguistic values that are transformed into fuzzy values. Furthermore, the weight of each energy source decision expert is evaluated by the proposed method. Next, the importance of criteria is computed by an extensive maximizing deviation method inspired by fuzzy divergence measure. Finally, the problem of choosing a renewable energy source is considered to show the thorough execution process of the introduced method. The proposed method's strength lies in its capability of providing effective solutions where there is a shortage of quantitative information.