The implementation of fleet management software in mining operations poses challenges, including high initial costs and the need for skilled personnel. Additionally, integrating new software with existing systems can be complex, requiring significant time and resources. This study aims to mitigate these challenges by leveraging advanced technologies to reduce initial costs and minimize reliance on highly trained employees. Through the integration of computer vision and multi-objective optimization, it seeks to enhance operational efficiency and optimize fleet management in open-pit mining. The objective is to optimize truck-to-excavator assignments, thereby reducing excavator idle time and deviations from production targets. A YOLO v8 model, trained on six hours of mine video footage, identifies vehicles at excavators and dump sites for real-time monitoring. Extracted data—including truck assignments and excavator ready times—is incorporated into a multi-objective binary integer programming model that aims to minimize excavator waiting times and discrepancies in target truck assignments. The epsilon-constraint method generates a Pareto frontier, illustrating trade-offs between these objectives. Integrating real-time image analysis with optimization significantly improves operational efficiency, enabling adaptive truck-excavator allocation. This study highlights the potential of advanced computer vision and optimization techniques to enhance fleet management in mining, leading to more cost-effective and data-driven decision-making.

The importance of strategic mine planning to ensure the long-term viability of mining projects has increased significantly because of the limited availability of high-grade ore deposits. Given its high-dimensional and combinatorial nature, developing a precise mathematical optimization technique to solve the entire problem remains challenging, particularly for real-size block models. The primary objective of this study was to propose a method that combines a nested pit strategy with integer programming (IP) models to overcome computational limitations by reducing the problem’s complexity, decreasing solution times, and providing insights into alternative production schedules for large-scale open-pit mines. The proposed algorithm strategically fixes the decision variables based on parametric analysis of the ultimate pit limit to simplify the IP model. The approach was applied to various block models from MineLib, and the results were compared with standard IP solutions and findings from related studies using alternative algorithms. Applying the proposed method demonstrated significant reductions in the solution time (up to 95%) and the ability to solve intractable models.

Purpose. To investigate the mineralogical, geochemical, and technological characteristics of the Köprüalan (Aydın, SW Turkey) feldspar deposit. The study aims to determine its suitability for industrial ceramic applications and to evaluate how quality-based resource management influences both the economic viability and environmental sustainability of the mining operation. Methods. Geochemical analysis of 222 samples was conducted together with X-ray diffraction (XRD) to determine mineral assemblages. Technological tests, including water absorption and shrinkage measurements, were performed to assess industrial performance. The obtained data were integrated into 3D geological models and geostatistical simulations to evaluate various mine design scenarios, with a focus on optimizing stripping ratios and reducing material handling. Findings. Results indicate that the deposit consists predominantly of felsic metamorphic rocks with high SiO2 (64.76-74.87%), elevated alkali oxides (Na2O + K2O), and low Fe2O3 (≤ 1.72%). XRD analysis confirmed a feldspar-rich composition with an average content of 62%. Integration of a multi-tier quality classification into the production sequence significantly increases the project’s economic value. Optimized extraction sequences also reduce the stripping ratio, thereby lowering the operational carbon footprint by minimizing waste haulage and energy consumption. Originality. A novel framework is proposed that transforms static mineralogical classification into a dynamic decision-support system. The approach integrates 3D geological modelling with geostatistical simulations to quantify trade-offs between quality-driven extraction, economic performance, and environmental sustainability in industrial mineral deposits. Practical implications. Implementation of the proposed quality-tier system enables operators to optimize production planning, reduce waste management costs, and improve the environmental sustainability of industrial mineral extraction through lower green-house gas emissions.

Comparison of travel route optimization algorithms performance for fuel truck routing problem in open pit mines | Hasozdemir | Mining Science and Technology (Russia) Сравнение эффективности алгоритмов оптимизации маршрутов движения при решении задачи маршрутизации бензовозов при открытых горных работах | Хасоздемир | Горные науки и технологии

Accurate near-real-time granulometry is critical for optimizing energy efficiency in comminution circuits, yet standard optical monitoring systems fail under the harsh conditions of industrial mining. High conveyor velocities induce motion blur, while heavy dust loads occlude particle boundaries, leading to severe under-counting of fine fractions and an overestimation of product coarseness. This paper proposes a novel tracking framework to overcome these limitations. The method integrates a five-frame temporal rolling buffer with a multi-stream augmentation architecture that decouples motion and texture features. Experimental validation on the feed of a secondary conical crusher at an operational facility demonstrates that the proposed framework recovers significant particle data lost by baseline models, correcting the measured D₈₀ and D₅₀ values of the crushed material. This correction reveals a finer true load, enabling more precise closed-loop control of crusher settings and reducing unnecessary energy consumption.