Abstract
In large-scale group decision-making (LSGDM) for dry hot rock exploration location selection, decision-makers are limited by their professional fields and knowledge background, and it is difficult to provide complete evaluation information. However, brainstorming is the main advantage of LSGDM. To maximize the professional contributions of various decision-makers, a multi-attribute LSGDM method based on three-way decision (TWD) and intuitionistic fuzzy concept-oriented (IFC) is proposed. Firstly, according to the characteristics of IFC, a description of the LSGDM problem based on IFC is given; then, an LSGDM model based on TWD is proposed to classify and rank alternatives. Two algorithmic descriptions are given, namely consensus reaching process algorithm and algorithm for classifying and ranking alternatives. Then, taking dry hot rock exploration location selection as an example, the execution steps of this model were elaborated in detail, and the final classification and ranking results were obtained. Finally, the effectiveness and feasibility of this model were analyzed based on experimental results, and the influence of various parameters on the results was also studied.
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Acknowledgements
This research was funded by Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province (No. DRN2105, DRN19014); Scientific Research Innovation Team of Neijiang Normal University (No. 2021TD04); Scientific Research Project of Neijiang Normal University (No. 2021YB21); and Application basic research project of Sichuan Province (No. 2021YJ0108).
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by YL, JW, and ZZ. The first draft of the manuscript was written by FL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Liu, F., Zhou, Z., Wu, J. et al. Location Selection for Dry Hot Rock Exploration Based on Large-Scale Group Decision-Making with Three-way Decision. Int. J. Fuzzy Syst. (2024). https://doi.org/10.1007/s40815-024-01690-7
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DOI: https://doi.org/10.1007/s40815-024-01690-7