合金收得率预测及配料方案优化问题的建模与求解
摘要
随着人工智能的发展,各国钢铁企业都在探索最优的自动配料系统,合金收得率预测及成本优化算法的研究成为热点。本文建立了基于遗传算法优化的多层神经网络BP算法的收得率预测模型、基于改进单纯形法的配料成本优化模型,利用MATLAB、LINGO软件对算例进行了求解,检验出本文模型的实用性良好。
引文
[1]于鹏,战东平,姜周华,李大量,尹小东,马志刚.LF精炼终点成分预报模型开发[J].材料与冶金学报,2006(1):20-22.
[2]史春薇,田强,葛骞,鲁传涛.基于SPSS统计软件在多元线性回归校验数据中的应用[J].当代化工,2014,43(6):1112-1113.
[3]曹玉茹.基于SPSS加权回归的回归分析条件适用性研究[J].统计与决策,2019,35(4):89-92.
[4]冷建飞,高旭,朱嘉平.多元线性回归统计预测模型的应用[J].统计与决策,2016(7):82-85.
[5] Wei Fei, Zhou Yanfei. Optimize combined in/out tasks of AS/RS[J]. Hoisting and Conveying Machinery, 2007(07):70-72.
[6] Malmborg C J. Interleaving dynamics in the autonomous vehicle storage and retrieval systems[J]. International Journal of Production Research, 2003, 41(5):1057-1069.
[7]王和杰.基于遗传算法优化的BP神经网络的汽车油耗计算模型[D].江苏科技大学, 2016.
[8] Fukunarim. A network queuing approach for evaluation of performance measures in autonomous vehicle storage and retrieval systems[J]. European Journal of Operational Research,2009, 193(1):152-167.
[9] Dotoli M, Fanti M P. A colored Petri net model for automated storage and retrieval systems serviced by rail-guided vehicles:a control perspective[J]. International Journal of Computer Integrated Manufacturing, 2005, 18(2):122-136.
[2]史春薇,田强,葛骞,鲁传涛.基于SPSS统计软件在多元线性回归校验数据中的应用[J].当代化工,2014,43(6):1112-1113.
[3]曹玉茹.基于SPSS加权回归的回归分析条件适用性研究[J].统计与决策,2019,35(4):89-92.
[4]冷建飞,高旭,朱嘉平.多元线性回归统计预测模型的应用[J].统计与决策,2016(7):82-85.
[5] Wei Fei, Zhou Yanfei. Optimize combined in/out tasks of AS/RS[J]. Hoisting and Conveying Machinery, 2007(07):70-72.
[6] Malmborg C J. Interleaving dynamics in the autonomous vehicle storage and retrieval systems[J]. International Journal of Production Research, 2003, 41(5):1057-1069.
[7]王和杰.基于遗传算法优化的BP神经网络的汽车油耗计算模型[D].江苏科技大学, 2016.
[8] Fukunarim. A network queuing approach for evaluation of performance measures in autonomous vehicle storage and retrieval systems[J]. European Journal of Operational Research,2009, 193(1):152-167.
[9] Dotoli M, Fanti M P. A colored Petri net model for automated storage and retrieval systems serviced by rail-guided vehicles:a control perspective[J]. International Journal of Computer Integrated Manufacturing, 2005, 18(2):122-136.