云制造模式下基于变邻域动态烟花算法的柔性车间调度
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摘要
针对云制造模式下多目标柔性车间调度问题,考虑柔性车间零件加工的优化及云任务与自生产任务的协同调度,建立了以最大完工时间最小、机器利用率最大、碳排放量最小为最优指标的多目标柔性车间调度模型。在无需预先生成工件的加工路线的前提下,提出了一种基于变邻域搜索的动态烟花算法。通过变邻域搜索有效提高局部最优解的精度。引入进化速度的概念,利用进化速度计算算法迭代过程中需要更新的维度,实时调整相关参数,加速算法收敛,避免陷入局部最优。最后,通过对比仿真验证模型和算法的有效性和鲁棒性。
Aiming at the multi-objective flexible job shop scheduling problem in cloud manufacturing mode, considering the optimization of flexible job shop parts processing and the coordinated scheduling of cloud tasks and self-production tasks, multi-objective flexible shop scheduling model are established, which regards the minimum completion time, maximum machine utilization and minimum carbon emission as indicators. A dynamic fireworks algorithm that is based on variable neighborhood search is proposed without pre-generating the machining route of the workpiece. The accuracy of the local optimal solution is effectively improved by the variable neighborhood search. Introduce the concept of evolutionary speed, use the dimension of the evolutionary speed calculation algorithm in the iterative process, adjust the relevant parameters in real time, accelerate the convergence of the algorithm, and avoid falling into local optimum. Finally, the effectiveness and robustness of the model and algorithm are verified by comparative simulation.
Aiming at the multi-objective flexible job shop scheduling problem in cloud manufacturing mode, considering the optimization of flexible job shop parts processing and the coordinated scheduling of cloud tasks and self-production tasks, multi-objective flexible shop scheduling model are established, which regards the minimum completion time, maximum machine utilization and minimum carbon emission as indicators. A dynamic fireworks algorithm that is based on variable neighborhood search is proposed without pre-generating the machining route of the workpiece. The accuracy of the local optimal solution is effectively improved by the variable neighborhood search. Introduce the concept of evolutionary speed, use the dimension of the evolutionary speed calculation algorithm in the iterative process, adjust the relevant parameters in real time, accelerate the convergence of the algorithm, and avoid falling into local optimum. Finally, the effectiveness and robustness of the model and algorithm are verified by comparative simulation.
引文
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[2] 黄辉,宁涛,云计算环境下的模糊柔性作业车间调度方法[J].信息与电脑,2015,12(8):56-59.
[3] 黄伟建,郭芳.基于烟花算法的云计算多目标任务调度[J].计算机应用研究,2016,34(6):1718-1720.
[4] 李伯虎,张霖,王时龙,等.云制造-面向服务的网络化制造新模式[J].计算机集成制造系统,2010,16(1):1-7.
[5] 王军强,崔福东,张承武,等.面向云制造作业车间的机器能力界定方法[J].计算机集成制造系统,2014,20(9):2146-2163.
[6] 何林燕.云制造环境下柔性作业车间调度算法的研究[D].哈尔滨:哈尔滨理工大学,2017.
[7] 鲁建厦,胡庆辉,董巧英,等.面向云制造的混流混合车问调度问题[J].中国机械工程,2017,28(2):191-198.
[8] 谭营.烟花算法引论[M].北京:科学出版社,2015.
[9] Zheng yujun,Songqin,Chen Shengyong,Multiobjective fireworks optimization for variable-rate fertilization in oil crop production [J].Applied Soft Computing,2013,13(11):4253-4263.
[10] 钱晓雯.求解作业车间调度问题的变邻域动态烟花算法[J].实验室研究与探索,2018,37(1):19-21.