Evaluating the sustainable mining contractor selection problems: An imprecise last aggregation preference selection index method
The increasing complexity surrounding decision-making situations has made it inevitable for practitioners to apply ideas from a group of experts or decision makers (DMs) instead of individuals. In a large proportion of recent studies, not enough attention has been paid to considering uncertainty in practical ways. In this paper, a hesitant fuzzy preference selection index (HFPSI) method is proposed based on a new soft computing approach with risk preferences of DMs to deal with imprecise multi-criteria decision-making problems. Meanwhile, qualitative assessing criteria are considered in the process of the proposed method to help the DMs by providing suitable expressions of membership degrees for an element under a set. Moreover, the best alternative is selected based on considering the concepts of preference relation and hesitant fuzzy sets, simultaneously. Therefore, DMs' weights are determined according to the proposed hesitant fuzzy compromise solution technique to prevent judgment errors. Moreover, the proposed method has been extended based on the last aggregation method by aggregating the DMs' opinions during the last stage to avoid data loss. In this respect, a real case study about the mining contractor selection problem is provided to represent the effectiveness and efficiency of the proposed HFPSI method in practice. Then, a comparative analysis is performed to show the feasibility of the presented approach. Finally, sensitivity analysis is carried out to show the effect of considering the DMs' weights and last aggregation approach in a dispersion of the alternatives’ ranking values.
Borujeni, Mohammad Panahi and Gitinavard, Hossein
"Evaluating the sustainable mining contractor selection problems: An imprecise last aggregation preference selection index method,"
Journal of Sustainable Mining: Vol. 16
, Article 10.
Available at: https://doi.org/10.46873/2300-3960.1179
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.