摘要
为解决FMS(柔性制造系统)中工序流与刀具流集成优化调度问题,以生产总时间最小为优化目标,提出了一种基于改进贝叶斯算法的优化方法,构建了基于变量取值的概率描述模型——改进贝叶斯网络,以历史运行经验为最初解群,然后以所构建的模型产生新的可行解用以组成下一代解群。经测试表明:该模型与传统遗传算法和贝叶斯算法想比,刀具整体利用率和机床整体利用率9%、11%和4%、7%。
To solve the integration scheduling problem of process flow and tool flow in FMS (flexible manufacturing system),aiming at the objective of minimizing the total production time,an optimization method based on improved Bayesian algorithm is proposed.A probabilistic description model based on variable values is constructed,which is to improve the Bayesian network.The historical operation experience is the initial solution group,and then the new feasible solution is generated by the constructed model to form the next generation solution group.The results show that this model is better than the traditional genetic algorithm and Bayesian algorithm in that the overall tool utilization rate and overall machine tool utilization rate are 9%,11%and 4%,7%.
作者
李丽娟
郭天赐
曹岩
刘菊花
孙夏辉
Li Lijuan;Guo Tianci;Cao Yan;Liu Juhua;Sun Xiahui(School of Mechanical and Electrical Engineering,Xi'an University of Technology,Xi'an 710021,China)
出处
《工具技术》
2018年第12期98-101,共4页
Tool Engineering
基金
智能制造测量过程控制方法研究(XAGXJJ17006)
