Assessing the Performance of Green Mines via a Hesitant Fuzzy ORESTE–QUALIFLEX Method

• Main Authors: Weizhang Liang, Bing Dai, Guoyan Zhao, Hao Wu
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Mathematics
• Subjects: hesitant fuzzy sets (HFSs); qualitative flexible (QUALIFLEX); Organísation, rangement et synthèse de données relationnelles (ORESTE); green mine; performance evaluation
• Online Access: https://www.mdpi.com/2227-7390/7/9/788

Due to various environmental issues caused by resource exploitation, establishing green mines is an essential measure to realize sustainable growth for mining companies. This research aimed to develop a novel methodology to evaluate the performance of green mines within hesitant fuzzy conditions. First, hesitant fuzzy sets (HFSs) were used to express original fuzzy assessment values. Then, the extended expert grading approach and the modified maximum deviation method with HFNs were combined to determine comprehensive importance degrees of criteria. Afterward, the traditional qualitative flexible (QUALIFLEX) method was integrated with the Organísation, rangement et synthèse de données relationnelles (ORESTE) model to achieve the rankings of mines. Finally, the proposed hesitant fuzzy ORESTE−QUALIFLEX approach was utilized to evaluate the performance of green mines. In addition, the robustness of the method was verified by a sensitivity analysis, while the effectiveness and strengths were certified by a comparison analysis. The results indicate that the proposed methodology has great robustness and advantages and that it is feasible and effective for the performance evaluation of green mines under hesitant fuzzy environment.