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A mathematical model for production scheduling of open pit mines maximises the net present value and satisfies the reserves, pit slope angle, and production capacity constraints. A solution to this model aims to deliver an extraction sequence and the movement of materials across various stages within the operation over a defined planning horizon. However, given that the mineral reserves delineated into thousands of mining blocks form the geological input, the model falls in the category of large-scale optimisation problems, i.e. it is computationally intractable, and exact methods cannot solve realistic scenarios of the problem. Therefore, this paper contributes an alternative to the conventional mathematical model along with a corresponding heuristic method to solve this proposed model. An implementation of the proposed method at various realistic instances reveals better performance in terms of net present value, computation time and optimality gap as compared to the traditional methods. [ABSTRACT FROM AUTHOR]

This research presents a new Simulated Annealing based stochastic optimisation algorithm to integrate geological uncertainty into the optimization process through multiple equiprobable simulated realisations of an orebody while considering stockpiling options and other relevant constraints. The stockpiling option is included, increasing the chances of processing high-grade and most certain ore blocks in early periods. The efficiency of the proposed algorithm in creating a single good enough production schedule that minimises the risk of deviation from production targets while maximising the net present value of the operation is demonstrated through three case studies, i.e. case A with 2448 blocks, B with 6,578 and C with 10,810 blocks. The comparison of results with the two-stage stochastic model reveals that the proposed methodology reduces the risk of production deviation to a minimal and provides a near-optimal solution with an optimality gap of 3.53, −0.87, and 8.19% for cases A, B, and C within a reasonable amount of time. [ABSTRACT FROM AUTHOR]

An optimal cut-off grade policy maximises the net present value of an open-pit mining operation and defines a schedule of cut-off grades and the corresponding supply of materials to various destinations. Generally, the cut-off grades are dynamic and decline with the exhaustion of reserves from one year to the next. Thus, stockpiling of the low-grade uneconomic material during earlier years becomes an option to process or blend stockpiled material during later years of mine life. Stockpiling is an established practice in the mining industry and given its significance; as an alternative to traditional cut-off grade models, this article presents a mixed integer programming-based mathematical model that offers an optimal cut-off grade policy with an option to stockpile using three different strategies. The implementation of the new formulation using a case study mining operation available in literature reveals the value of the new model. [ABSTRACT FROM AUTHOR]

Cut-off grade classifies the available supply of ore (valuable) and waste material within a mineralised deposit. Given the mining, processing and refining limitations of a mining operation, an optimal cut-off grade policy ensures that the flow of ore from the mine to the processing and refining facilities is maintained at the maximum possible throughput. This policy defines a schedule of cut-off grades along with corresponding quantities of mineralised material to be mined, processed and metal refined in each period of the scheduling horizon. The criteria that controls the development of cut-off grade policy aligns with the strategic objectives of an operation in order to maximise the discounted value (net present value or NPV) over the life of operation. This paper proposes a new mixed integer linear programming (MILP) based model that maximises NPV subject to the mining, processing, refining capacity constraints and develops an optimal cut-off grade policy for an open pit mining operation with multiple processing streams. An implementation of the proposed method on hypothetical and realistic data promises a relatively higher NPV as compared to the traditional Lane's approach practiced in the mining industry. [ABSTRACT FROM AUTHOR]

Production scheduling of open pit mines seeks to define such a temporal flow of ore and waste materials from a mine that maximizes a project’s Net Present value (NPV), while satisfying different physical and operational constraints. To achieve this objective, different mathematical formulations have been proposed in the technical literature. However, solving these formulations for a real sized open pit mine could be extremely difficult and computationally challenging job. In order to make this job computationally tractable, different heuristic and metaheuristic techniques are commonly used. This paper presents the results of a study where a real valued population based metaheuristic technique known as Differential Evolution (DE) algorithm has been used with the aim to solve the production scheduling problem of open pit mines with low to moderate computational cost with or without the condition of grade uncertainty. Three different case studies revealed the capabilities and efficiency of DE algorithm by producing sufficiently good solutions of the said with moderate computational cost. [ABSTRACT FROM AUTHOR]

Determining an optimum long term production schedule is an important part of the planning process of any open pit mine; however, the associated optimization problem is demanding and hard to deal with, as it involves large datasets and multiple hard and soft constraints which makes it a large combinatorial optimization problem. In this paper a procedure has been proposed to apply a relatively new and computationally less expensive metaheuristic technique known as particle swarm optimization (PSO) algorithm to this computationally challenging problem of the open pit mines. The performance of different variants of the PSO algorithm has been studied and the results are presented. [ABSTRACT FROM AUTHOR]

Clustering approaches are widely used to group similar objects and facilitate problem analysis and decision-making in many fields. During short-term planning of open-pit mines, clustering aims to aggregate similar blocks based on their attributes (e.g., geochemical grades, rock types, geometallurgical parameters) while honoring various constraints: i.e., cluster shapes, size, alignment with mining direction, destination, and rock type homogeneity. This approach helps to reduce the computational cost of optimizing short-term mine plans. Previous studies have presented ways to perform clustering without honoring constraints specific to mining. This paper presents a novel block clustering heuristic capable of considering and honoring a set of mining block aggregation requirements and constraints. Constraints can relate to the clustering adjacent blocks, achieving higher destination homogeneities, controlled cluster size, consistency with mining direction, and achieving clusters with mineable shapes and rock types' homogeneity. The proposed algorithm's application on two different datasets demonstrates its efficiency and capability in generating reasonable block clusters while meeting different predefined aggregation requirements and constraints. [ABSTRACT FROM AUTHOR]