Document Type : Review Paper
Authors
Faculty of Mining, Petroleum & Geophysics Eng., Shahrood University of Technology, Shahrood, Iran
10.22044/jme.2025.16381.3193
Abstract
The selection of an appropriate mining method is a complex decision-making problem influenced by a multitude of geological, technical, economic, environmental, and safety-related parameters. This study presents a comprehensive review of multi-criteria decision-making (MCDM) approaches applied to mining method selection, with a focus on their historical evolution, integration with fuzzy logic, artificial intelligence, and machine learning, as well as bibliometric trends and parameter analysis. The findings reveal a growing tendency toward hybrid and intelligent MCDM models that enhance decision accuracy and adaptability under uncertainty. A bibliometric analysis of key authors, countries, journals, and citation patterns highlights the global scope and scientific impact of research in this area. Furthermore, the study categorizes influencing parameters into intrinsic and extrinsic groups, identifying ore geometry, grade distribution, and rock mass properties as dominant intrinsic factors, while economic, environmental, and operational considerations represent significant extrinsic influences. This review emphasizes the vital role of MCDM techniques in optimizing mining operations, and advocates for further development of dynamic, data-driven models to meet the evolving challenges of modern mining.
Keywords
Main Subjects
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