Open-pit production scheduling (OPPS) is a large-scale, NP-hard optimisation problem characterised by complex precedence, capacity, and uncertainty, rendering exact methods impractical for real applications. Metaheuristic algorithms have emerged as effective alternatives for generating high-quality solutions within reasonable computational time. This study synthesises their application across deterministic and stochastic formulations, covering block sequencing, integrated material flow, and risk-aware scheduling. Key challenges include scalability, high-dimensional constraint handling, and limited integration of uncertainty and environmental objectives. Significant gaps remain in large-scale validation and real-time optimisation, motivating future research on parallel, adaptive, and sustainable metaheuristic frameworks.
Traditional geostatistical methods such as ordinary kriging often generate biased and overly smoothed grade estimates. Simulation-based techniques overcome this limitation by producing multiple orebody realisations, allowing improved representation of grade uncertainty in mine planning. This study extends an existing MILP model to a Stochastic Mixed Integer Linear Programming (SMILP) framework to maximise net present value (NPV) under uncertainty. Two case studies with six scenarios were analysed. Results show that the SMILP model incorporating stockpile management achieved the highest NPV, increasing NPV by 4% and 0.28% compared to corresponding MILP models, demonstrating the practical benefits of accounting for grade uncertainty.
Maintenance and reliability of mining equipment are critical for meeting production targets, managing costs, and supporting safe operations. This paper provides a structured review of maintenance and reliability research in mining, focusing on single-unit (SU) and multi-unit (MU) modelling approaches. Using this distinction as an analytical framework, 87 peer-reviewed studies published between 2000 and 2024 are analysed in terms of objectives, solution methods, and consideration of workforce and spare-parts constraints. The review shows a shift towards data-driven and simulation-based methods and identifies gaps in decision-oriented modelling, uncertainty handling, and coordination with production planning.
The stability of mine overburden (OB) dump slopes is critical for safe and sustainable mining. This study examines fly ash (FA) as a stabilizing agent for OB from the Korba Coalfield, Chhattisgarh. FA-OB mixtures were evaluated through geotechnical, mineralogical, and microstructural analyses using XRD, FESEM, and EDX. Numerical modelling with LEM and FEM assessed stability and optimal dump height. A mixture of 30% FA and 70% OB showed the highest strength based on uncured laboratory samples. Stability results indicate safe dump heights of 50 m at 33 degrees and 45 m at 35 degrees, supporting sustainable dump management.
This study evaluates genetic algorithm (GA), adaptive genetic algorithm (AGA), and differential evolution (DE) for optimising underground mining ramps. Using a 177-segment baseline, the algorithms were compared for cost efficiency. DE demonstrated superior performance, achieving an 11.4% cost reduction (580.9s runtime), significantly outperforming AGA (7.15%) and GA (5.5%). The analysis highlights the critical value of location optimisation, where refining ramp paths minimises fault encounters and support requirements. These findings validate that integrating geometric refinements with site-specific geotechnical constraints substantially enhances the financial viability and safety of underground operations.
The reclamation of post-mining areas in open-pit mining is a critical focus of modern environmental research due to its ecological, social, and economic implications. This study presents a bibliometric analysis of global research trends from 2000 to 2023, based on the Web of Science Core Collection. Key findings highlight a significant increase in publications, with China, the United States, and Canada leading the field. The analysis identifies dominant journals, influential authors, and interdisciplinary approaches emphasising ecological restoration, soil improvement, and biodiversity enhancement. Future research should prioritise climate change impacts, innovative technologies, and global collaboration to advance sustainable post-mining land management.
Effective waste rock management is crucial for long-term mining planning. Ignoring the role of potentially acid-generating (PAG) waste rock requires significant treatment costs incurred to mitigate acid rock drainage (ARD). Encapsulation of PAG material can prevent or mitigate ARD by limiting exposure. Traditional practices don't optimise production schedules while addressing this risk. This work integrates waste management and reclamation using encapsulation into a simultaneous stochastic optimisation framework. Uncertainties in acid generation are addressed using geostatistical simulations of the rock's geochemical properties. A case study at a copper-gold mining complex increases the encapsulation by 41.3% with 1.6% decrease in NPV.
Effective dust suppression on mining haul roads is essential for reducing health risks, ensuring operational safety, and addressing environmental concerns. This study presents a decision-making framework combining the Fuzzy Analytic Hierarchy Process (FAHP) and PROMETHEE II to evaluate eight dust suppressant alternatives used in Chilean mining. The alternatives were assessed under economic, environmental, health, and technical criteria based on expert judgement. Results highlighted Bischofite, Asphalt Emulsion, and Vegetable Oils and Molasses as preferred options for the studied mines. The results demonstrate how locally relevant alternatives can be prioritised using a structured decision-making framework rather than identifying universally optimal dust suppressants.
Mining complexes face increasing pressure to reduce environmental impacts, yet most short-term stochastic optimisation frameworks in mining complexes neglect environmental objectives, focusing solely on economic and operational performance. This work extends the framework by incorporating carbon footprint and pricing schemes under uncertainty, explicitly accounting for variability in emission factors and truck trip distributions. A parallelised GRASP metaheuristic solution approach is adapted and used for its scalability, flexibility, and effectiveness in navigating large, nonlinear search spaces. Experimental results for a gold mining complex demonstrate up to 4% emission reductions from trucks and shovels operation with only a 0.4% loss in profit.
This study evaluates hyperspectral imaging (HSI) as a cost-effective alternative to the Mineral Liberation Analyser (MLA), which is constrained by high cost, operational complexity, and time-intensive sample preparation. The research aimed to classify minerals and estimate their proportions using deep learning and regression techniques. Standard mineral samples (Magnetite, Ilmenite, Quartz) with particle sizes of 100-300 mu m were prepared under controlled illumination for model development. Mineral classification was performed using a one-dimensional convolutional neural network (1D CNN), achieving an accuracy of 87.4%. However, mixed minerals within individual pixels reduced classification precision. To address this, the concept of spectral unmixing was applied, and regression analysis was identified as more suitable for predicting mineral proportions. The regression model demonstrated strong performance (RMSE = 9.90, R2 = 0.91, R = 0.96), explaining over 91.3% of data variability and enabling estimation of intermediate proportions for mixed pixels (e.g. 20-50% Ilmenite). These findings highlight HSI combined with regression as a reliable and efficient approach for mineral composition assessment, overcoming key limitations of MLA.
Overbreak and underbreak are common challenges in underground mining, impacting ore recovery, dilution, and operational costs. As mining becomes more complex, modelling approaches have evolved from empirical methods to advanced machine learning and hybrid techniques. This paper reviews recent developments in overbreak/underbreak prediction, including regression, classification, and management systems. Key trends include ensemble models, explainability, and symbolic regression for pattern recognition. This review highlights strengths and limitations of current methods, identifies research gaps, and outlines future directions such as real-time modelling, multi-site validation, and improved integration of geological and geotechnical knowledge into design processes.
Manual surveying for blasthole marking in underground mining is inefficient and hazardous for face drilling rigs. A LiDAR-based 3D mapping method reconstructs drift maps, with beacon centroids dynamically maintaining drift centerlines during blasting. By segmenting the heading face and integrating a predefined template, the system auto-generates 3D blasthole coordinates. A positioning algorithm fuses boom kinematics with LiDAR-SLAM localisation to align the boom within a unified mine frame. Experimental results show that the method attains high-precision positioning with an error below 10 cm. This significantly enhances drilling efficiency, reduces labour risks, and contributes to the automated and intelligent development of mining.
This study presents an improved 2D constitutive model that incorporates time and maximum shear strain effects to simulate the time-dependent behaviours of rocks. The modification successfully replicates full-range axial and lateral creep curves, and strain rate effects, with parameters calibrated against triaxial test data from granite, tuff, and sandstone. Its implementation in finite element analysis demonstrates enhanced prediction of time-dependent plastic zone expansion, stress redistribution, and significant inward displacement around underground openings, effectively capturing squeezing phenomena observed in real tunnels.
This study develops a novel data-driven framework to minimise energy consumption and CO2 emissions in copper mine crushing systems. Unlike previous works, it compares single-unit and multi-unit configurations from an environmental perspective. Using a Random Forest model, key design parameters are identified and ranked. K-means clustering then groups operational data into four representative clusters, and Particle Swarm Optimization determines the optimal variable combination, reducing the objective function. Results show stable convergence and demonstrate that the proposed hybrid approach significantly enhances energy efficiency and reduces the carbon footprint in crushing operations.
A novel CO2-carbonated coal-based solid waste (CBSW) backfill material was developed to manage coal-based solid waste and sequester CO2. The absorption saturation threshold and reaction mechanisms of materials were investigated through mechanical tests and microscopic analysis. The maximum absorption capacity reached 2.485 mg-CO2/g-CBSW under 20-min ventilation, which enhanced the uniaxial compressive strength (UCS) by 116% post-carbonation. Microanalysis revealed that: 1) strength enhancement originated from CaCO3 and silica gel formation; 2) pore-filling by CaCO3 and C-A-S-H gel bonding occurred after saturation; 3) efficiency declined due to soluble Ca(HCO3)(2) formation with excess CO2. The technology enables in-situ CBSW reuse and carbon sequestration.
This study presents a hybrid framework combining a multi-objective mixed-integer nonlinear programming model with a two-layer reinforcement learning (RL) system for real-time truck dispatching. The upper Relaxation-Induced Neighbourhood Search (RINS) layer optimises material destinations, while the lower RL layer dynamically assigns trucks based on system states. Applied to a copper mine, the approach outperformed a random base case representing uncoordinated dispatching and achieved consistent improvements over the RINS benchmark. Relative to the base case, truck waiting time decreased by 46.3%, processing capacity utilisation increased by 38.8%, constraint violations dropped by 73.1%, and haulage costs were reduced by 34.8%.
Considering time-varying factors like weather and road conditions is critical for open-pit mine route planning, yet it has rarely been addressed in existing studies. This gap leaves transportation solutions unable to guide actual ore production, so data-driven truck scheduling optimisation for open-pit mines is developed in this study. First, a multi-objective truck routing optimisation model with time windows is presented according to a real application scenario. Subsequently, a data-driven optimisation method is introduced to construct a dynamic random forest (RF) surrogate model using truck trajectory data to assess the quality of the new solutions. Moreover, a novel multi-objective evolutionary algorithm is proposed to obtain a set of Pareto-optimal alternatives. It is composed of an improved multi-objective ant colony optimiser and a fitness mechanism, which can strike a balance between diversity and convergence of the solutions. The proposed algorithm is simulated for routing optimisation of an open-pit mine in China. Under the first decision objective, Optimal Truck Route Scheme (OTRS)'s LowerBound outperforms Multi-Objective Ant Colony Optimisation (MOACO) by 15%-40% in most instances and averages better than Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2); under the second, its upper-lower bound range is approximately 3.7 times that of MOACO and about 4.27% larger than that of NSGA-II, while SPEA2 has outliers in many cases. The experimental results confirm that this approach has robust performance, with faster hauling efficiency and lower cost.
A trade-off exists in mine ventilation systems to balance the ventilation needs of multiple underground stopes while minimizing energy consumption to the greatest extent possible. However, the intricate network structure, nonlinear constraint optimization models, and variable regulator positions render existing ventilation control technologies inadequate. This paper therefore proposes a multi-branch joint on-demand optimization control strategy. To address the challenges posed by complex network structures and nonlinear optimization models, we employ the Competitive Swarm Optimization algorithm with an epsilon-constrained method (epsilon-CSO), which efficiently tackles the ventilation network optimization problem with the aim of minimizing energy consumption. Notably, the strong interconnectivity between branches in complex networks means that the number and positioning of regulators significantly influence the minimization energy consumption strategy. This study introduces the second-order sensitivity theory to describe the sensitivity changes between branches during the airflow regulation process and determines the position of the regulator. Ultimately, an indirect optimization method was implemented to achieve multi-branch joint on-demand control of mine airflow. Case analysis indicates that, compared to existing algorithms, the epsilon-CSO algorithm yields the lowest optimized and regulated energy consumption, amounting to 123663.82W. The constraint values for each circuit are all below 3.723E-12. The optimal regulator positions were validated through solving the optimal control strategies for four ventilation requirements. This demonstrates that the epsilon-CSO algorithm possesses a competitive edge in addressing complex ventilation network optimization challenges. This study offers an effective strategy for reducing ventilation costs in mines with complex ventilation networks and Intelligent Ventilation on Demand (IVOD).
Open pit mine planning is a complex process shaped by multiple stages, ranging from exploration and development to exploitation and reclamation, and influenced by many uncertainties. Geological, price, and operational risks all contribute to deviations between planned and actual production and financial projections. Traditional models often assume fixed inputs, overlooking the inherent variabilities and uncertainties. Consequently, modern methodologies have shifted towards uncertainty-based modelling approaches. This paper reviews and discusses these uncertainty-based modelling approaches in mine planning, presents their advantages and disadvantages, and outlines gaps and opportunities for future work to integrate the uncertainties that are key components of mine planning.
