Hesitant Fuzzy Multi-Granulation Rough Set Model Based on Similarity Assessment

A novel multiple attribute group decision-making (MAGDM) model is introduced in this study, utilizing a diversified hesitant fuzzy multi-granulation information system to address challenges in incomplete information settings. The analysis commences with an exploration of hesitant fuzzy sets and multi-granulation approximation. Subsequently, the integration of cumulative prospect theory into Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) within a hesitant fuzzy framework is discussed, emphasizing the incorporation of a risk preference coefficient in GDM to enhance individual assessments. The model proposes a systematic approach to address various MAGDM scenarios under hesitant fuzzy conditions. An illustrative case study on resource-sharing is provided to demonstrate the efficacy of the diversified MAGDM model, with evaluation outcomes expressed using hesitant fuzzy elements, offering valuable insights into group decision-making in hesitant fuzzy environments.