Quantum Spherical Fuzzy Decision Framework with Golden Cut and ARTASI for Sustainable Mining Method Selection in Geologically Complex Thin Seams

Abstract The selection of a suitable mining method for geologically complex and thin coal seams, particularly those with thicknesses ranging from 0.30 to 2.12 m, remains a critical challenge. In Meghalaya, this issue is of paramount importance, as conventional unscientific extraction practices have been associated with safety hazards, resource losses, and environmental degradation. This study proposes a Quantum Spherical Fuzzy Large-Scale Decision-Making (QSF-LSDM) framework to support the systematic selection of mining methods under uncertain and conflicting conditions. The framework employs Quantum Spherical Fuzzy Sets (QSFS) to represent vagueness and hesitation in expert judgments through membership, non-membership, and hesitancy amplitudes with associated phase information. To reduce subjectivity in fuzzy parameter selection, a Golden Cut procedure based on the Golden Ratio is incorporated. Expert importance is determined using Consensus-Based Expert Weighting (CBEW), which reflects the degree of agreement among experts. Their assessments are subsequently aggregated using the Quantum Spherical Fuzzy Weighted Bonferroni Mean (QSFWBM), which captures interactions among criteria. Objective criterion weights are obtained through continuous Ant Colony Optimization (ACO), formulated to maximize the overall pairwise score deviation among alternatives. The final ranking is established using the Alternative Ranking Technique based on Adaptive Standardized Intervals (ARTASI). The framework is applied to 12 candidate mining methods evaluated across 12 criteria covering technical, economic, safety, environmental, and operational aspects.. The results identify automated 3D-trajectory shearer mining (\(\:{M}_{10}\); \(\:{\Omega\:}=0.674\)) as the highest-ranked method, followed by shearer-based thin-seam longwall mining (\(\:{M}_{5}\); \(\:{\Omega\:}=0.662\)) and continuous-miner bord-and-pillar mining (\(\:{M}_{3}\); \(\:{\Omega\:}=0.632\)). Sensitivity and comparative analyses further demonstrate the stability and robustness of the obtained ranking.