Conditional draw control system in block-cave production scheduling using mathematical programming

Nezhadshahmohammad, Farshad; Aghababaei, H.; Pourrahimian, Yashar

doi:10.1080/17480930.2017.1385155

Cited by 6 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The formulation for optimising block caving scheduling presented here is based on a simplified formulation of the work by Nezhadshahmohammad et al (2017). In that formulation, which uses a deterministic model instead of simulated scenarios, the objective function is the maximisation of the NPV.…”

Section: Methodsmentioning

confidence: 99%

“…The reliability index, defined as an indicator of the deviation of planned tonnage to be drawn from a draw-point, could be used as a measure of risk. Nezhadshahmohammad et al (2017) presented a MILP formulation to optimise the scheduling of draw columns in a block caving operation subject to the depletion rates of all adjacent draw-points. They applied the model to a case study of 325 draw-points over 15 production periods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The optimisation of block caving production scheduling with geometallurgical uncertainty – a multi-objective approach

Sepúlveda

Dowd

2018

Mining Technology

View full text Add to dashboard Cite

The incorporation of the uncertainties of geometallurgical variables in mine planning provides new opportunities for decision-makers to analyse, compare and choose among many scenarios to achieve an optimal balance between economic and operational objectives. This research focuses on maximising economic return and minimising the risk arising from the uncertainty associated with geometallurgical variables. We formulate two-objective optimisation problems based on an underground mining operation. The first objective is the maximisation of the Net Smelter Return, which includes penalties associated with deleterious elements in concentrates. Four different objectives were tested as candidates for the second objective: Volatility, Value at Risk, Conditional Value at Risk and deviation from the planned production target. The first three are measures of economic risk and the fourth is a measure of operational risk. Uncertainties in metal grades, geometallurgical recoveries and grades in concentrate are included in the proposed model. Our results demonstrate that geometallurgical uncertainties can be successfully integrated into production scheduling optimisation in a multi-objective approach and the problem can be solved using genetic algorithms to yield useful conclusions to support the decision-making process.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The optimisation of block caving production scheduling with geometallurgical uncertainty – a multi-objective approach

Sepúlveda

Dowd

2018

Mining Technology

View full text Add to dashboard Cite

show abstract

“…Many researchers have used mathematical programming to optimize the production schedule in block-cave mining. They have been trying to either maximize the NPV (Guest et al 2000;Rubio Enrique 2002;Diering 2004;Rubio and Diering 2004;Weintraub et al 2008;Smoljanovic et al 2011;Epstein et al 2012;Pourrahimian et al 2013;Alonso-Ayuso et al 2014;Khodayari and Pourrahimian 2014;Nezhadshahmohammad et al 2017;Khodayari and Pourrahimian 2017), to minimize the mining cost (Song 1989), or to minimize deviations from operating goals (Chanda 1990;Rahal 2008;Diering 2012;Khodayari and Pourrahimian 2016;Khodayari and Pourrahimian 2017;Sepúlveda et al 2018). Mixed integer linear programming (MILP) is the most common methodology that has been used for the purpose of production scheduling optimization.…”

Section: Introductionmentioning

confidence: 99%

“…2014; Khodayari and Pourrahimian 2014; Nezhadshahmohammad et al. 2017; Khodayari and Pourrahimian 2017), to minimize the mining cost (Song 1989), or to minimize deviations from operating goals (Chanda 1990; Rahal 2008; Diering 2012; Khodayari and Pourrahimian 2016; Khodayari and Pourrahimian 2017; Sepúlveda et al. 2018).…”

Section: Introductionmentioning

confidence: 99%

Long-term production scheduling optimization and 3D material mixing analysis for block caving mines

Khodayari

Pourrahimian

2019

Mining Technology

View full text Add to dashboard Cite

Although some scheduling optimization models can be found in the literature, few of them include material flow and the resulting dilution. In this paper, a 3D mixing methodology is proposed to be incorporated into the production schedule model. To capture horizontal and vertical mixing, different scenarios are generated based on the particles that fall into a 3D cone of movement, CoM. The proposed model is a block caving scheduling optimizer, BCSO, which includes mixing in the optimization. The BCSO was tested on a real-case block caving mine with 424 drawpoints; also, a number of production schedules were generated for the same mine using PCBC GEOVIA software. Resulting production schedules show that the BCSO can improve the NPV of the project by 2% to 4% compared to the best case generated by PCBC.

show abstract

Production Scheduling with Horizontal Mixing Simulation in Block Cave Mining

2019

View full text Add to dashboard Cite

Conditional draw control system in block-cave production scheduling using mathematical programming

Cited by 6 publications

References 8 publications

The optimisation of block caving production scheduling with geometallurgical uncertainty – a multi-objective approach

The optimisation of block caving production scheduling with geometallurgical uncertainty – a multi-objective approach

Long-term production scheduling optimization and 3D material mixing analysis for block caving mines

Production Scheduling with Horizontal Mixing Simulation in Block Cave Mining

Contact Info

Product

Resources

About