A simulated annealing based approach for open pit mine production scheduling with stockpiling option

Danish, Abid Ali Khan; Khan, Asif; Muhammad, Khan; Ahmad, Waqas; Salman, Saad

doi:10.1016/j.resourpol.2021.102016

Cited by 13 publications

(11 citation statements)

References 27 publications

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“…1), making it a complex problem that is hard to solve and demands a continuous financial risk assessment. Open-pit scheduling intends to find the best schedule of calendar programming across the mineral value cycle (Ali et al, 2021;Fathollahzadeh et al, 2021;Rezakhah & Newman, 2020). This is because the value of a mining project is usually affected by various underlying economic and physical variables, such as commodity prices, grades, expenses, timetables, volumes, and environmental concerns, which are unknown with confidence at the project's inception.…”

Section: Manuscriptmentioning

confidence: 99%

A new global optimum solution model based on multi-value dynamic programming for the cut-off grade optimization in a surface mining complex

Biswas

Sinha

Sen

2021

Preprint

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In techno-economic concern, cut-off grade (COG) optimization is the key for efficient mineral liquidation from thehuge metalliferous surface mining sector. In this paper, a sequentially advancing algorithm based on discretemulti-value dynamic programming (MDP) has been developed to calculate the global optimum COG of alarge-scale open-pit metalliferous deposit. The proposed COG optimization algorithm aims to overcome thelimitations of straightforward classical techniques in determining the optimum COG. This discrete COG-MDPmodel is the first of its kind and has the novelty of dealing with the simulation of eight dynamic possibilities toachieve the maximal global Net Present Value (NPV). A high-level programming language (Python) has been usedto develop the computer model to deal with the complexity of handling a minimum of 500 series of dynamicvariables. This model can generate results in polynomial-time from the complex of mining, milling, and smeltingand refining system corresponding to various limiting conditions. The prime objective considered in the model isto optimize the COG of a metalliferous deposit. A working open-pit copper mining complex from India has beenused to validate the model. In this study, the optimum COG for the Malanjkhand copper deposit has been found tobe (0.33%, 0.23%, 0.52%, 0.26%, 0.27%, 0.22%, 0.24%) with a maximum NPV of ₹ (12204, 14653, 16948, 14609,21454, 26717, 38821) million corresponding to various scenarios. The findings also show that the present valuegradually hits zero after the project’s life cycle, confirming the typical pattern of other mining firms.

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Section: Manuscriptmentioning

confidence: 99%

A new global optimum solution model based on multi-value dynamic programming for the cut-off grade optimization in a surface mining complex

Biswas

Sinha

Sen

2021

Preprint

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“…The algorithm moves towards a final (optimum) solution by accepting or rejecting solutions based on an acceptance probability (Kirkpatrick, Gelatt, and Vecchi, 1983). SA has been used widely in the mining industry for various optimization problems, for example, for resource modelling (Deutsch, 1992), optimization of blasting costs (Bakhshandeh Amnieh et al, 2019), strategic openpit mine production scheduling (Dimitrakopoulos, 2011;Kumral and Dowd, 2005) for multiple destinations (Jamshidi and Osanloo, 2018) with and without stockpile considerations (Danish et al, 2021) or blast movements (Hmoud and Kumral, 2022). These applications focus on maximizing the profit for mining of each mining block by considering revenues, penalties, dilution factors, or mining constraints.…”

Section: Introductionmentioning

confidence: 99%

Stochastic analysis of dig limit optimization using simulated annealing

Duijvenbode

Shishvan

2023

J. S. Afr. Inst. Min. Metall.

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The results of dig limit delineation in open pit mining are never truly optimized due to gaps in the underlying data, such as insufficient sampling. Aside from the data uncertainty, there is also an influence on the final dig limit by either humans or by the heuristic character of an optimization method like simulated annealing. Several dig limit optimizers have been published, which can replace the manual dig-limits designing process. However, these dig limit designs are generally not adapted to account for this heuristic character. In this paper we present a stochastic analysis tool that can be used with the results of heuristic dig-limit optimization to increase confidence in the obtained results. First, an enhanced simulated annealing algorithm for dig limit optimization is presented. Then, this algorithm is tested on ten different blasts at the Marigold mine, Nevada, USA, as a case study. Finally, the results are analysed with a destination-based ensemble probability map and an analysis conducted of the final solution data distribution. The generated dig-limit designs of the algorithm include high revenue areas that are excluded in comparable manual designs and show improved objective and revenue values. The analysis tool provides block destination probabilities and box plots with the distribution of opportunity value for the dig limit. Furthermore, with the analysis tool, it is possible to make well-informed design decisions in areas of uncertainty.

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“…In recent years, different researchers have tried applying these approaches to the OPMPS problem. These approaches can find the optimum or near-optimum solutions for large combinatorial problems within a reasonable time (Danish et al 2021). The presented researches about each of these methods are stated in the following.…”

Section: Introductionmentioning

confidence: 99%

A Genetic algorithm scheme for large scale open-pit mine production scheduling

Azadi

Mirzaei-Nasirabad

Mousavi

2023

Mining Technology: Transactions of the Institutions of Mining a

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Due to the large size of open-pit mines' long-term production scheduling (OPMPS) problem in large-scale deposits, it is challenging to solve that problem as the mixed integer linear programming (MILP) model. This study used an approach of the genetic algorithm (GA) to tackle this challenge. So, in a small hypothetical deposit, based on the blocks in the ultimate pit limit and scenarios with 2-6 phases, net present values (NPV) and computational times obtained from the GA and MILP model were compared to evaluate the GA. Also, the GA was applied to a large-scale deposit to determine the efficiency of the GA in real deposits. The maximum NPV was obtained for the four-phase scenario in the hypothetical deposit and the six-phase scenario in the large-scale deposit. Although the GA's NPV decreased slightly compared to the global optimum solution from the MILP model, the computational time was significantly reduced.

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A simulated annealing based approach for open pit mine production scheduling with stockpiling option

Cited by 13 publications

References 27 publications

A new global optimum solution model based on multi-value dynamic programming for the cut-off grade optimization in a surface mining complex

A new global optimum solution model based on multi-value dynamic programming for the cut-off grade optimization in a surface mining complex

Stochastic analysis of dig limit optimization using simulated annealing

A Genetic algorithm scheme for large scale open-pit mine production scheduling

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