A double hierarchy fuzzy decision approach for solar farm ranking sites in India
针对太阳能农场选址中专家偏好模糊、权重不确定等问题，提出一种双层次犹豫模糊决策方法，结合后悔/欣喜度量、证据加权和CODAS-Copeland排序，在印度案例中识别出Vellore和Ramanathapuram为最佳选址。
Solar is seen as a potential clean energy solution as world leaders shift energy sources to meet sustainability goals and country demand. Site selection for solar farms is a crucial and complex decision problem involving multiple attributes. Previous studies on site selection (i) could not model natural language preferences; (ii) did not effectively capture expert hesitation; (iii) were prone to the subjective biases of experts; (iv) involved indeterminate weighting of hybrid attributes; and (v) lacked personalized ranking of farms. Motivated by these gaps, this study proposes an integrated decision-making approach using a double-hierarchy hesitant fuzzy context. Firstly, experts' weights are methodically derived via a regret/rejoice measure. Secondly, attributes' weights are determined via an evidence-based rank sum strategy. Thirdly, an algorithm to rank solar farms is formulated with a combinative distance-based assessment (CODAS) and the Copeland scheme. Lastly, a case example of sites from India exemplifies the approach's usefulness. Sensitivity analysis and comparison reveal the pros and cons of the developed approach. Results infer that Vellore and Ramanathapuram are two potential locations where a solar farm can be installed, and annual solar irradiation, technology, distance from river, sunshine hours, and land cover are the top five attributes that aid in solar farm location selection. • Vellore is a potential location for setting up a solar farm in Tamil Nadu. • Experts can rate flexibly using natural language form. • Expert weights are methodically determined by regret measure. • Attribute weights are determined objectively and subjectively. • A CODAS-Copeland ranking enables personalized and net ranking of sites.