A simulated annealing based stochastic long-term production scheduling of open-pit mines with stockpiling under grade uncertainty

被引:3
作者
Danish, Abid Ali Khan [1 ]
Khan, Asif [1 ,2 ]
Muhammad, Khan [1 ,3 ]
机构
[1] Univ Engn & Technol, Natl Ctr Artificial Intelligence, Intelligent Informat Proc Lab, Peshawar, Khyber Pakhtunk, Pakistan
[2] Pak Austria Fachhsch Inst Appl Sci & Technol, Mineral Resource Engn, Haripur, Khyber Pakhtunk, Pakistan
[3] Univ Engn & Technol Peshawar, Dept Min Engn, Peshawar 25000, Khyber Pakhtunk, Pakistan
关键词
Metaheuristics (simulated annealing); long-term production scheduling; geological uncertainty; stockpile option; stochastic optimization; two-stage stochastic optimisation; GEOLOGICAL UNCERTAINTY; PARTICLE SWARM; OPTIMIZATION; ALGORITHM; MODEL;
D O I
10.1080/17480930.2022.2140543
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:43 / 65
页数:23
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