Short-term planning optimization model for underground mines

2018

This article describes a new model aiming at optimizing shortand medium-term underground mine scheduling. The complexity of the problem to solve and the frequency at which planners have to revise these schedules are among the main motivation for developing such a model. In order to address this problem, a Mixed Integer Programming model was developed with a variable time discretization to accurately represent both shortand medium-term operational constraints in a single model. Results of a preliminary model are presented with explanations an in-depth analysis. An improved formulation is also described with it’s associated results and benefits. Further testing with scenarios similar to long-term planning show very promising results for the possible application of our modified formulation to existing long-term model.

2018

For the past few years, the mining industry has seen a lot of operational changes. Digitalization and automation of many processes have paved the way for an increase in its general productivity. In keeping with this trend, this article presents a novel approach for optimizing underground mine scheduling for the shortand medium-term. This problem is similar to the Resource-Constrained Project Scheduling Problem, with some particularities. The model uses Constraint Programming principles to maximize the Net Present Value of a mining project. It plans work shifts for up to a year in advance, considering specialized equipment, backfilling and operational constraints. Results from its applications to datasets based on a Canadian gold mine demonstrate its ability to find optimal solutions in a reasonable time. A comparison with an equivalent Mixed Integer Programing model proves that the Constraint Programming approach offers clear gains in terms of computability and readability of the co...

Optimization and Engineering, 2020

Mine operations are supported by a short-term production schedule, which defines where and when mining activities are performed. However, deviations can be observed in this short-term production schedule because of several sources of uncertainty and their inherent complexity. Therefore, schedules that are more likely to be reproduced in reality should be generated so that they will have a high adherence when executed. Unfortunately, prior estimation of the schedule adherence is difficult. To overcome this problem, we propose a generic simulation-optimization framework to generate short-term production schedules for improving the schedule adherence using an iterative approach. In each iteration of this framework, a shortterm schedule is generated using a mixed-integer linear programming model that is simulated later using a discrete-event simulation model. As a case study, we apply this approach to a real Bench and Fill mine, wherein we measure the discrepancies among the level of movement of material with respect to the schedule obtained from the optimization model and the average of the simulated schedule using the mine schedule material's adherence index. The values of this index decreased with the iterations, from 13.1% in the first iteration to 4.8% in the last iteration. This improvement is explained because the effects of the operational uncertainty within the optimization model can be considered by integrating the simulation. As a conclusion, the proposed framework increases the adherence of the short-term schedules generated over iterations. Moreover, these increases in the adherence of schedules are not obtained at the expense of the Net Present Value.

It is common practice for underground mine plans to be created sequentially, where results from one planning process form the input data for another. While this is practical for manual methods, computerized optimization techniques should consider an integrated approach to creating the global mine plan. This is because optimizing an individual mine planning process, such as stope layouts, introduces a likelihood of increasing costs or decreasing revenues associated with other areas, such as production scheduling, as harmful decisions must be balanced. Considering the interaction and influence that individual underground mine planning processes have on each other during optimization will provide more profitable results than if these are ignored. Optimization techniques for stope layouts and production scheduling are reviewed. An integer programming model is proposed that allows for either integrated or isolated optimization. Both approaches are separately applied to a block model. The results demonstrate the model's ability to produce optimal long-term sublevel stoping mine plans and the benefits of using an integrated approach.

Journal of The South African Institute of Mining and Metallurgy, 2020

Operational mine planning is a fundamental activity in mine operations and should take into account various characteristics of the material, the available mining faces, the requirements of discharge points, and production hiatuses due to reduced equipment operational efficiency, in order to efficiently allocate shovels and trucks and deliver the required tonnage and quality to the proper destinations. This paper presents an approach for optimizing short-term day-today mining operations using simulation. A mathematical model based on integer linear programming is developed. The solution is obtained through two different software packages using discrete event simulation (Arena) and a mathematical optimization model (Lingo). The two integrated models search an efficient solution to optimize a set of criteria by applying goal programming to hierarchically optimize five objective functions in a logical priority order under the operator's standpoint and by simulating mining operations and unproductive events to evaluate how closely the optimized results are actually achieved. The integrated models are applied to a real large-scale iron ore mine in southeastern Brazil. A decision support system (DSS) prototype that meets the production requirements is also applied. The results show that an increase in the available loading equipment will not result necessarily in increased production, as expected. The models show satisfactory results and applicability to real and complex mining situations, and the formulation allows for easy adaptation to other mine situations.

Journal of the Southern African Institute of Mining and Metallurgy, 2020

SYNOPSIS Long-term production scheduling is a major step in open pit mine planning and design. It aims to maximize the net present value (NPV) of the cash flows from a mining project while satisfying all the operational constraints, such as grade blending, ore production, mining capacity, and pit slope during each scheduling period. Long-term plans not only determine the cash flow generated over the mine life, but are also the basis for medium- and short-term production scheduling. Mathematical programming methods, such as linear programming, mixed integer linear programming, dynamic programming, and graph theory, have shown to be well suited for optimization of mine production scheduling. However, the long-term plans generated by the mathematical formulations mostly create a scattered block extraction order on several benches that cannot be implemented in practice. The reason is the excessive movement of mining equipment between benches in a single scheduling period. In this paper,...

International Journal of Mining, Reclamation and Environment, 2020

Medium-term development planning of underground mines requires scheduling multiple activities to comply with long-term milestones, and to obtain a time span as short as possible. However, the planning must also respect the availability of construction resources and precedence constraints, which in our case can be disjunctive, that is with more than one alternative predecessor. In this paper, we present an optimisation model to find the schedule of minimum length, satisfying all the constraints mentioned. We develop a heuristic approach to solve it and show that it can be used to produce feasible development plans in a real mine.

Mining Technology, 2010

Maximising value is the main objective when developing long term mine production schedules. These results provide input for the development of a short term schedule that aims to meet process plant feed requirements so as to produce a quality saleable product. This paper reviews previous work on optimised short-and long term production scheduling and real time fleet management systems. A new dynamic mathematical model using mixed integer programming is proposed to optimise short term production scheduling and machine allocation for application in sublevel stoping operations. The objective of the model is to minimise deviation from targeted metal production. The dynamic nature of the model not only optimises the shift based schedule but also allows rapid equipment reassignment to take place as underground operating conditions change. Optimal results are generated in less than 1 min when trialled on a conceptual sublevel stoping dataset.

IEEE Access

Conventional mine planning approaches use an estimated orebody model as input to generate optimal production schedules. The smoothing effect of some geostatistical estimation methods cause most of the mine plans and production forecasts to be unrealistic and incomplete. With the development of simulation methods, the risks from grade uncertainty in ore reserves can be measured and managed through a set of equally probable orebody realizations. In order to incorporate grade uncertainty into the strategic mine plan, a stochastic mixed integer programming (SMIP) formulation is presented to optimize an underground cut-and-fill mining production schedule. The objective function of the SMIP model is to maximize the net present value (NPV) of the mining project and minimize the risk of deviation from the production targets. To demonstrate the applicability of the SMIP model, a case study on a cut-and-fill underground gold mining operation is implemented.

Natural Resources Research, 2019

During the last few decades, open pit mines have been deepened to the remote depths of the ground such that removing great volumes of waste rocks may jeopardize their profitability. In such circumstances, an early transition from open pit to underground mining may prove more profitable. This is the case in many large-scale open pit mines, where considerable amount of mineral reserve remains beneath the pit bottom. This paper develops a set of classified integer programming models for determining an optimum transition depth (OTD) between open pit and various underground mining methods. The models lie within the scope of long-term production scheduling because the OTD is revealed through scheduling of all mineral reserve. To provide a quantitative analysis, two optimization models with distinct solution strategies are executed on a three-dimensional sector of a real orebody. The results indicate that the integrated models increase the total NPV of the mining operations by up to 8.62%. However, the scenario-based models enhance the primitive solution (the base scenario) by up to 3.42%. It is also shown that the integrated models present solutions that are practically more realistic.