Optimization Math Models and Simulation Systems

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This study presents a comprehensive analysis of optimizing mining haulage operations through the development and application of mathematical models and simulation techniques. Motivated by the need to enhance efficiency, reduce operational costs, and maximize productivity in the mining sector, this research introduces a dynamic truck allocation system that is informed by real-time operational data. The core of the investigation revolves around achieving an optimal Match Factor (MF) of 1, which indicates a perfect balance between the number of trucks and shovel availability, thus minimizing wait times and maximizing operational throughput.

The conclusions drawn from the simulation results demonstrate the effectiveness of the proposed models in identifying the optimal fleet size that minimizes mining costs and maximizes productivity. Through detailed analysis, it was revealed that maintaining the equilibrium of truck deployment not only reduces unit mining costs but also significantly enhances production output. The study highlights the critical importance of understanding queue dynamics and their impact on operational efficiency, providing actionable insights for mining operations to optimize their haulage systems.

By employing rigorous mathematical modeling and simulation, this research offers a novel approach to shovel-truck allocation, contributing valuable perspectives to the field of mining engineering. Based on Jorge Lozano's Mining Engineering thesis at the Universidad Nacional de Ingeniería , this paper stands as a testament to the potential of mathematical and simulation methodologies in addressing complex operational challenges in the mining industry, paving the way for future advancements in mining logistics optimization.