2063 USE OF DISCRETE-EVENT SIMULATION FOR DECISION-MAKING IN UNDERGROUND MINING PhD. R. Castro1, Fernando Caballero2, Msc. Lenin Arancibia3 1University of Chile, Chile 2BCTEC Engineering and Technology, Chile 3BCTEC Engineering and Technology, Chile ABSTRACT Underground mining operations planning faces the challenge of making high-impact decisions in complex systems, where the stochastic interaction between equipment, infrastructure, and geological conditions renders traditional analytical methods insufficient. This paper presents a methodological framework based on discrete-event simulation (DES) to support decision-making in underground mining, with emphasis on the integrated representation of the processes that determine the actual productive capacity of an operation. The framework addresses three fundamental dimensions: verification of productive capacities under realistic stochastic conditions, generation and evaluation of alternative operating scenarios, and identification of dominant system constraints. Through explicit incorporation of the variables affecting production — hang-ups, scheduled and unscheduled stoppages, operational interferences, and equipment variability — the model delivers distributions of outcomes rather than point estimates, enabling quantification of the probability of meeting committed production plans. The methodology has been applied and validated in real underground mining operations, demonstrating its ability to faithfully reproduce historical system behavior and to rigorously project the impact of technological, operational, and investment decisions prior to their implementation. KEYWORDS Discrete-event simulation, underground mining, decision-making, productive capacity, scenario analysis, production planning, risk management 1. INTRODUCTION Large-scale underground mining faces investment decisions that span decades and are difficult to reverse. Traditional analytical methods — based on average rates and deterministic calculations — are insufficient to capture the inherent variability of these systems: equipment failures, hang-ups, operational interferences, and human factors generate emergent behavior that cannot be predicted from the isolated analysis of each component. Discrete-event simulation (DES) offers a response to this challenge, representing the system as a network of interconnected processes calibrated from real data. This paper presents a methodological framework for its use in decision-making, structured around three capabilities: verification of productive capacities, generation and evaluation of alternative scenarios, and probabilistic validation of production plans. 2. STATE OF THE ART 2.1 Limitations of Traditional Analytical Approaches Analytical methods — based on nominal productivity, average utilization factors, and theoretical cycle times — present three fundamental limitations: implicit linearity, which ignores saturation and congestion effects when multiple resources share limited infrastructure; the treatment of averages as a
RkJQdWJsaXNoZXIy MTM0Mzk2