基于改进灰狼算法的云制造服务随机调度优化研究

Research on Random Scheduling Optimization of Cloud Manufacturing Service Based on Improved Grey Wolf Algorithm

为研究云制造背景下分散的制造资源随机调度问题,将服务时间设置为不确定生产参数,建立随机调度模型,并使用与强化学习结合的改进灰狼算法进行求解以优化调度方案。从云平台、用户以及服务提供商三方出发,提出了基于不确定环境的多目标数学模型,同时,将灰狼优化算法与强化学习结合,使用Q-learning调整参数,并利用双种群分别进行探索和挖掘,防止陷入局部最优,并用于解决中大规模实例。通过对比实验评估算法性能,该算法在16组算例中性能均显示最优,能够提升加工效率并增加相关者收益,对算法参数进行了敏感性分析,验证了参数的合理性和适用性。

To study the random scheduling problem of dispersed manufacturing resources in the context of cloud manufacturing,the service time is set as an uncertain production parameter,a random scheduling model is established,and an improved grey wolf algorithm combined with reinforcement learning is used to solve it to optimize the scheduling scheme.Starting from the perspectives of cloud platforms,users,and service providers,a multi-objective mathematical model based on uncertain environments is proposed.At the same time,the Grey Wolf Algorithm is combined with reinforcement learning,Qlearning is used to adjust parameters,and dual populations are explored and mined separately to prevent falling into local optima and to solve medium to large-scale instances.By conducting comparative experiments to evaluate the performance of the algorithm,it was found that the algorithm showed the best performance in all 16 cases,which can improve processing efficiency and increase stakeholder benefits.Sensitivity analysis was also conducted on the algorithm parameters to verify their rationality and applicability.