A new risk-based optimisation method for the iron ore production scheduling using stochastic integer programming

Stochastic integer programming (SIP) has recently been studied to manage the risk caused by geological uncertainty when solving mine planning and production scheduling problems of open pit mines. However, similar to other mathematical programming techniques that deploy integer variables, the main ob...

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Veröffentlicht in:Resources policy Jg. 62; S. 571 - 579
Hauptverfasser: Mai, Ngoc Luan, Topal, Erkan, Erten, Oktay, Sommerville, Bruce
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.08.2019
Elsevier Science Ltd
Schlagworte:
Aggregate data
Algorithms
Case studies
Computer simulation
Datasets
Integer programming
Integers
Iron and steel making
Iron compounds
Iron ore production scheduling
Iron ores
Large-scale optimisation
Mathematical programming
Mineral deposits
Mining
Mining industry
Open pit mining
Optimization
Ores
Present value
Production
Production scheduling
Risk
Risk management
Stochastic integer programming
TopCone algorithm
Uncertainty
Variables
Large-scale optimisation
Open pit mining
TopCone algorithm
Iron ore production scheduling
Stochastic integer programming
ISSN:0301-4207, 1873-7641
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Zusammenfassung:Stochastic integer programming (SIP) has recently been studied to manage the risk caused by geological uncertainty when solving mine planning and production scheduling problems of open pit mines. However, similar to other mathematical programming techniques that deploy integer variables, the main obstacle of applying SIP on real-life datasets stems from the enormous number of integer variables required by its mathematical formulation, which is a function of number of mining blocks being processed and lifespan of the mining project. In this paper, a new framework is proposed for stochastic mine planning process which makes the application of SIP on large-scale datasets tractable. Firstly, mining blocks of simulated orebody models are clustered using TopCone algorithm to significantly reduce the scale of the data. A new SIP model is then developed to work on aggregated blocks so not only the net present value (NPV) is maximised and the risk of not meeting production targets is minimised, but also solution can be obtained in a practical timeframe. The scheduling result of the new SIP model is also compared to an integer programming (IP) model to highlight the ability to manage risk and generating higher NPV on a case study of a large-scale multi-element iron ore deposit in Pilbara region, Western Australia. •We develop a new risk-based optimisation method for iron ore production scheduling.•We use stochastic integer programming and a block aggregation technique called TopCone algorithm.•A case study in an iron ore deposit in Pilbara region, Western Australia shows a considerable increase of NPV and risk reduction.•The solution is achieved in a practical timeframe using a standard office computer.
Bibliographie:ObjectType-Article-1
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ISSN:0301-4207
1873-7641
DOI:10.1016/j.resourpol.2018.11.004