Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Mine planning is an inherently uncertain process influenced by variable geological, economic, technical, and environmental conditions. Traditional deterministic models often fail to capture these uncertainties, resulting in suboptimal decisions and inaccurate project valuations. This paper presents a comprehensive review of probabilistic and stochastic methods developed to address uncertainty in mine planning and improve decision-making under risk. The study systematically examines probabilistic modeling techniques such as Monte Carlo simulation, Bayesian updating, and geostatistical simulation, alongside stochastic optimization approaches including two-stage and multi-stage stochastic programming, robust optimization, and real options analysis. These methods are evaluated based on their ability to quantify, propagate, and mitigate uncertainties across the mine life cycle from resource estimation and pit optimization to production scheduling and financial forecasting. Findings reveal a growing shift from static deterministic models toward hybrid frameworks that integrate simulation, optimization, and machine learning to achieve adaptive, risk-aware planning. Probabilistic models enhance the reliability of resource classification and economic forecasting, while stochastic programming improves operational flexibility and scenario-based scheduling. Despite these advances, gaps remain in model integration, computational efficiency, and real-time adaptability, particularly in linking geological, economic, and operational uncertainties. The paper emphasizes the need for interdisciplinary approaches combining data analytics, artificial intelligence, and digital twin technologies to strengthen the predictive and adaptive capacity of mine planning systems. By advancing quantitative modeling in mine design and management, the mining industry can better balance profitability with sustainability and risk control.