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

Author

Listed:
  • Weizhang Liang

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Bing Dai

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China
    Deep Mining Laboratory of Shandong Gold Group Co., Ltd., Laizhou 261400, China)

  • Guoyan Zhao

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Hao Wu

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

Abstract

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.

Suggested Citation

  • Weizhang Liang & Bing Dai & Guoyan Zhao & Hao Wu, 2019. "Assessing the Performance of Green Mines via a Hesitant Fuzzy ORESTE–QUALIFLEX Method," Mathematics, MDPI, vol. 7(9), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:9:p:788-:d:261503
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    References listed on IDEAS

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    Cited by:

    1. Shuai Li & Lifeng Yu & Wanjun Jiang & Haoxuan Yu & Xinmin Wang, 2022. "The Recent Progress China Has Made in Green Mine Construction, Part I: Mining Groundwater Pollution and Sustainable Mining," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    2. Víctor Revilla-Cuesta & Marta Skaf & Ana B. Espinosa & Amaia Santamaría & Vanesa Ortega-López, 2020. "Statistical Approach for the Design of Structural Self-Compacting Concrete with Fine Recycled Concrete Aggregate," Mathematics, MDPI, vol. 8(12), pages 1-24, December.
    3. Keyou Shi & Yong Liu & Weizhang Liang, 2022. "An Extended ORESTE Approach for Evaluating Rockburst Risk under Uncertain Environments," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    4. Suizhi Luo & Lining Xing, 2019. "Picture Fuzzy Interaction Partitioned Heronian Aggregation Operators for Hotel Selection," Mathematics, MDPI, vol. 8(1), pages 1-23, December.
    5. Sidong Xian & Ke Qing & Ling Tang & Huilan Pan, 2023. "A Multi-criteria Group Decision Making Based on Possibility Degree Matrix and $$\mathbb {ZPDHL}$$ ZPDHL -VIKOR Method," Group Decision and Negotiation, Springer, vol. 32(3), pages 633-666, June.
    6. Jinhui Chen & Izhar Mithal Jiskani & Aiguo Lin & Chaocheng Zhao & Peixing Jing & Fengjie Liu & Mingyin Lu, 2023. "A hybrid decision model and case study for comprehensive evaluation of green mine construction level," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(4), pages 3823-3842, April.

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