Optimization for Mining Material Loading Based on Genetic Algorithm

Abstract:

In order to improve management level of mining materials, optimum loading scheme is important. Based on the analysis of bulk cargo loading problem, taking carrying capacity and effective volume as constraint conditions, maximizing transport benefits as target, mathematical model on the base of optimization method is established. And genetic algorithm is introduced to case study. The result shows that genetic algorithm in solving the optimum loading scheme of mining materials has quick convergence, short term, and higher precision. The better satisfactory answer can be obtained after 100 generations. Before 600 generations optimum loading scheme can be educed. Genetic algorithm, with good adaptability and powerful search performance, is very suitable for optimization calculation of multiple constraints problem. Genetic algorithm can make full use of carrying capacity and volume in the process of bulk cargo loading transport, that promot mining enterprise’s operation efficiency. The study is useful for management work of mining material warehousing, scheduling, transportation etc.

• Periodical: Advanced Materials Research (Volumes 457-458)
• Pages: 1342-1346
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.457-458.1342
• Online since: January 2012
• Authors: Tong Bin Zhao, Shan Shan Liu, Fan Wei Bu
• Keywords: Constraint Condition, Genetic Algorithm (GA), Mining Materials, Optimal Loading

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