Optimization of open-pit-mining operational planning by using a particle swarm algorithm
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摘要:
從露天礦采掘和運輸成本的最小化角度出發,構建露天礦生產作業計劃模型.基于群體智能優化理論,提出了用粒子群算法對露天礦生產作業計劃模型進行解算的方法,并在求解過程中設計了帶核粒子及雙吸引子的粒子搜索策略.以MATLAB軟件為平臺進行求解運算最佳作業計劃.以某露天鐵礦為工程背景進行實例研究,將研究結果與露天礦實際生產指標和非線性規劃解算結果進行比較驗證.結果表明,粒子群算法可用于露天礦生產作業計劃的優化編制.
Abstract:A open-pit-mining operational planning model was constructed from the view point of minimizing the mining and transportation cost. Based on the theory of swarm intelligence optimization, a method was proposed that uses a particle swarm optimization (PSO) algorithm to optimize the open-pit mining operation plan, and a search strategy with the core particle and double attractor was designed for particles in the calculation process. The optimal operation plan was calculated by using MATLAB software as a computation platform. A case study was performed by taking an open-pit iron mine as an engineering background. By comparing the optimization results of the PSO algorithm with the actual planning results and the calculated results of nonlinear programming, it is proved that the PSO algorithm is feasible and reliable for optimizing the open-pit mining operation plan.
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Key words:
- open pit mining /
- production planning /
- particle swarm optimization
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