Mining Method Selection (MMS) is a complex, multi-criteria decision-making problem influenced by geological, technical, economic, environmental, and safety factors. Traditional methods, such as Nicholas, University of British Columbia (UBC), and reformed UBC approaches, rely on expert judgment and fixed decision matrices, often failing to account for uncertainty, interdependencies, and conflicting criteria. To address these limitations, this study proposes the Flexible Mining Method Selection (FMMS) framework, which integrates Fuzzy Entropy (FE), Fuzzy Analytic Network Process (FANP), and Modified Fuzzy TOPSIS. FMMS dynamically adjusts criteria weights based on entropy-driven uncertainty measures, enabling adaptive, data-driven decisions while capturing interdependencies among selection criteria. Applied to the Gol Gohar iron ore deposit in Iran, FMMS identified Top Slicing as the most reliable alternative, achieving the highest crisp score (0.1066) with the lowest variance (0.000052) and standard deviation (0.0063), compared to the lowest-ranked score of 0.0932 for Stopping and Pillar. This contrasts with conventional Nicholas, UBC, and reformed UBC evaluations, which favored Cut and Fill but produced inconsistent rankings across weighting adjustments. Sensitivity analysis over a weighting parameter range of p = 0–10 confirmed stable rankings under varying weight scenarios, with Block Caving, Sublevel Stoping, and Sublevel Caving showing particularly robust behavior once internal criterion relationships were incorporated. These results demonstrate that FMMS effectively balances objective factors (geology, cost, safety, environmental impact) with subjective expert evaluations, offering a flexible, scalable, and systematic approach that enhances efficiency, accuracy, and sustainability in mining method selection. Furthermore, it lays a foundation for future AI-based decision-support systems in mining.
- Open Access
- Article
A Fuzzy Multi-Criteria Decision-Making Framework for Optimal Mining Method Selection in Complex Geological Conditions
- Radman Pishgahzadeh 1,
- Mojtaba Rezakhah 1,
- Amir Batarbiat 1,
- Masoud Monjezi 1,
- Moshood Onifade 2,3,
- Manoj Khandelwal 2,4,5,*
Author Information
Received: 08 Jun 2026 | Revised: 11 Aug 2026 | Accepted: 24 Aug 2026 | Published: 08 Oct 2026
Abstract
Keywords
mining method selection (MMS) | fuzzy multy-criteria decision-making (MCDM) | flexible mining method selection (FMMS) | fuzzy entropy
References
- 1.
Kazemi, M.M.K.; Nabavi, Z.; Rezakhah, M.; et al. Application of XGB-Based Metaheuristic Techniques for Prediction Time-to-Failure of Mining Machinery. Syst. Soft Comput. 2023, 5, 200061.
- 2.
Moreno, E.; Ferreira, F.; Goycoolea, M.; et al. Linear Programming Approximations for Modeling Instant-Mixing Stockpiles. In Proceedings of the 37th International Symposium on Application of Computers and Operations Research in the Mineral Industry (APCOM), Fairbanks, AK, USA, 23–27 May 2015; pp. 582–587.
- 3.
Mirzehi, M.; Rezakhah, M.; Mousavi, A.; et al. New MIP Model for Short-Term Planning in Open-Pit Mines Considering Loading Machine Performance: A Case Study in Iran. Int. J. Min. Miner. Eng. 2023, 14, 341–364. https://doi.org/10.1504/ijmme.2023.137375.
- 4.
Pouresmaieli, M.; Ataei, M.; Nouri Qarahasanlou, A.; et al. Multi-Criteria Decision-Making Methods for Sustainable Decision-Making in the Mining Industry (a Comprehensive Study). J. Min. Environ. 2024, 15, 683–706. https://doi.org/10.22044/jme.2023.13662.2528.
- 5.
Boshkov, S.H.; Wright, F.D. Basic and Parametric Criteria in the Selection, Design and Development of Underground Mining Systems. In SME Mining Engineering Handbook; SME-AIME: New York, NY, USA, 1973; Volume 1, pp. 12.2–12.13.
- 6.
Morrison, R.G.K. A Philosophy of Ground Control: A Bridge between Theory and Practice; Department of Mining and Metallurgical Engineering, McGill University: Montreal, QC, Canada, 1976.
- 7.
Laubscher, D.H. Selection of Mass Underground Mining Methods. In Design and Operation of Caving and Sublevel Stoping Mines; Society of Mining Engineers of AIME: New York, NY, USA, 1981; pp. 23–38.
- 8.
Nicholas, D.E. Method Selection-a Numerical Approach. In Design and Operation of Caving and Sublevel Stoping Mines; Society of Mining Engineers of AIME: New York, NY, USA, 1981; pp. 39–53.
- 9.
Hartman, H.L.; Mutmansky, J.M. Introductory Mining Engineering; John Wiley & Sons: Hoboken, NJ, USA, 1987.
- 10.
Miller-Tait, L.; Pakalnis, R.; Poulin, R. UBC Mining Method Selection. In Proceedings of the International Symposium on Mine Planning and Equipment Selection, Calgary, AB, Canada, 31 October–3 November 1995; pp. 163–168.
- 11.
Singh, G.; Chu, L.; Wang, L.; et al. Methods and Systems for Mining Minority-Class Data Samples for Training a Neural Network. U.S. Patent No. 11816183, 14 November 2023.
- 12.
Zadeh, L.A. Fuzzy Sets. Inf. Control 1965, 8, 338–353. https://doi.org/10.1016/s0019-9958(65)90241-x.
- 13.
Batarbiat, A.; Rezakhah, M. Hybrid DES and HWOA–NSGA-II for Pareto-Optimal Open-Pit Truck-and-Shovel Fleet Design. Min. Metall. Explor. 2026, 43, 2549–2564. https://doi.org/10.1007/s42461-026-01496-1.
- 14.
Dubois, D.; Prade, H. (Eds.) Fundamentals of Fuzzy Sets; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012.
- 15.
Sarabi, E.P.; Darestani, S.A. Developing a Decision Support System for Logistics Service Provider Selection Employing Fuzzy MULTIMOORA & BWM in Mining Equipment Manufacturing. Appl. Soft Comput. 2021, 98, 106849. https://doi.org/10.1016/j.asoc.2020.106849.
- 16.
Monjezi, M.; Moezinia, S.; Hamidi, J.; et al. Determining the Appropriate Rehabilitation Method in Open-Pit Mines Using Decision-Making Methods: A Case Study of the Zarshuran Gold Mine. J. Min. Environ. 2026, 17, 1015–1033.
- 17.
Aghajani Bazzazi, A.; Osanloo, M.; Karimi, B. A New Fuzzy Multi Criteria Decision Making Model for Open Pit Mines Equipment Selection. Asia-Pac. J. Oper. Res. 2011, 28, 279–300. https://doi.org/10.1142/s0217595911003247.
- 18.
Palanikkumar, D.; Upreti, K.; Venkatraman, S.; et al. Fuzzy Logic for Underground Mining Method Selection. Intell. Autom. Soft Comput. 2022, 32, 1843–1854. https://doi.org/10.32604/iasc.2022.023350.
- 19.
Ortiz-Barrios, M.; Cabarcas-Reyes, J.; Ishizaka, A.; et al. A Hybrid Fuzzy Multi-Criteria Decision Making Model for Selecting a Sustainable Supplier of Forklift Filters: A Case Study from the Mining Industry. Ann. Oper. Res. 2021, 307, 443–481.
- 20.
Wang, Q.; Cheng, T.; Lu, Y.; et al. Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor. Sensors 2024, 24, 1285. https://doi.org/10.3390/s24041285.
- 21.
Hosseini, S.; Jasimi, N.; Mehdizadeh, B.; et al. An Integrated Multi-Criteria Risk-Assessment Framework for Rockburst in Underground Construction. Bull. Comput. Intell. 2026, 2, 181–195. https://doi.org/10.53941/bci.2026.100010.
- 22.
Subedi, A.; Subedi, S.; Adhikari, S.; et al. Explainable Hybridized Machine Learning for Prediction of Compressive Strength of Fly-Ash based Geopolymer Concrete. Bull. Comput. Intell. 2026, 2, 213–234. https://doi.org/10.53941/bci.2026.100012.
- 23.
Bascetin, A.; Kesimal, A. The Study of a Fuzzy Set Theory for the Selection of an Optimum Coal Transportation System from Pit to the Power Plant. Int. J. Surf. Min. Reclam. Environ. 1999, 13, 97–101. https://doi.org/10.1080/09208119908944224.
- 24.
Karadogan, A.; Bascetin, A.; Kahriman, A. A New Approach in Selection of Underground Mining Method. In Proceedings of the International Conference Modern Management of Mine Producing, Varna, Bulgaria, 3–9 June 2001.
- 25.
Kesimal, A.; Bascetin, A. Application of Fuzzy Multiple Attribute Decision Making in Mining Operations. Miner. Resour. Eng. 2002, 11, 59–72. https://doi.org/10.1142/s0950609802000835.
- 26.
Bitarafan, M.; Ataei, M. Mining Method Selection by Multiple Criteria Decision Making Tools. J. S. Afr. Inst. Min. Metall. 2004, 104, 493–498.
- 27.
Elevli, B.; Demirci, A. Multicriteria Choice of Ore Transport System for an Underground Mine: Application of PTOMETHEE Methods. J. S. Afr. Inst. Min. Metall. 2004, 104, 251–256.
- 28.
Kazakidis, V.N.; Mayer, Z.; Scoble, M.J. Decision Making Using the Analytic Hierarchy Process in Mining Engineering. Min. Technol. 2004, 113, 30–42. https://doi.org/10.1179/037178404225004274.
- 29.
Ataei, M. Multicriteria Selection for an Alumina-Cement Plant Location in East Azerbaijan Province of Iran. J. S. Afr. Inst. Min. Metall. 2005, 105, 507–514.
- 30.
Acaroglu, O.; Ergin, H.; Eskikaya, S. Analytical Hierarchy Process for Selection of Roadheaders. J. S. Afr. Inst. Min. Metall. 2006, 106, 569–575.
- 31.
Acaroglu, O.; Feridunoglu, C.; Tumac, D. Selection of Roadheaders by Fuzzy Multiple Attribute Decision Making Method. Min. Technol. 2006, 115, 91–98.
- 32.
Öztaş, O.; Başçetin, A.; Kanli, A.I. EQS: A Computer Software Using Fuzzy Logic for Equipment Selection in Mining Engineering. J. S. Afr. Inst. Min. Metall. 2006, 106, 63–70.
- 33.
Samimi Namin, F.; Shahriar, K.; Ataee-Pour, M.; et al. A New Model for Mining Method Selection of Mineral Deposit Based on Fuzzy Decision Making. J. S. Afr. Inst. Min. Metall. 2008, 108, 385–395.
- 34.
Sitorus, F.; Cilliers, J.J.; Brito-Parada, P.R. Multi-Criteria Decision Making for the Choice Problem in Mining and Mineral Processing: Applications and Trends. Expert Syst. Appl. 2019, 121, 393–417. https://doi.org/10.1016/j.eswa.2018.12.001.
- 35.
Ekmekçioğlu, M.; Kaya, T.; Kahraman, C. Fuzzy Multicriteria Disposal Method and Site Selection for Municipal Solid Waste. Waste Manag. 2010, 30, 1729–1736. https://doi.org/10.1016/j.wasman.2010.02.031.
- 36.
Shannon, C.E. A Mathematical Theory of Communication. Bell Syst. Tech. J. 1948, 27, 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.
- 37.
Zadeh, L.A. Probability Measures of Fuzzy Events. J. Math. Anal. Appl. 1968, 23, 421–427. https://doi.org/10.1016/0022-247x(68)90078-4.
- 38.
De Luca, A.; Termini, S. Algebraic Properties of Fuzzy Sets. J. Math. Anal. Appl. 1972, 40, 373–386. https://doi.org/10.1016/0022-247x(72)90057-1.
- 39.
Saaty, T.L.; Vargas, L.G. Diagnosis with Dependent Symptoms: Bayes Theorem and the Analytic Hierarchy Process. Oper. Res. 1998, 46, 491–502. https://doi.org/10.1287/opre.46.4.491.
- 40.
Tavana, M. A Priority Assessment Multi-Criteria Decision Model for Human Spaceflight Mission Planning at NASA. J. Oper. Res. Soc. 2006, 57, 1197–1215. https://doi.org/10.1057/palgrave.jors.2602107.
- 41.
Chen, C.T. Extensions of the TOPSIS for Group Decision-Making under Fuzzy Environment. Fuzzy Sets Syst. 2000, 114, 1–9. https://doi.org/10.1016/s0165-0114(97)00377-1.
- 42.
Chu, T.C. Selecting Plant Location via a Fuzzy TOPSIS Approach. Int. J. Adv. Manuf. Technol. 2002, 20, 859–864. https://doi.org/10.1007/s001700200227.
- 43.
Chu, T.C.; Lin, Y.C. Improved Extensions of the TOPSIS for Group Decisionmaking under Fuzzy Environment. J. Inf. Optim. Sci. 2002, 23, 273–286. https://doi.org/10.1080/02522667.2002.10698996.
- 44.
Kaya, T.; Kahraman, C. Multicriteria Decision Making in Energy Planning Using a Modified Fuzzy TOPSIS Methodology. Expert Syst. Appl. 2011, 38, 6577–6585. https://doi.org/10.1016/j.eswa.2010.11.081.
- 45.
Kim, S.; Lee, K.; Cho, J.K.; et al. Agent-Based Diffusion Model for an Automobile Market with Fuzzy TOPSIS-Based Product Adoption Process. Expert Syst. Appl. 2011, 38, 7270–7276. https://doi.org/10.1016/j.eswa.2010.12.024.
- 46.
Nicholas, D.E. Selection Procedure. In SME Mining Engineering Handbook, 2nd ed.; Society for Mining, Metallurgy, and Exploration: Littleton, CO, USA, 1992; pp. 2090–2106.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com