Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

作     者：Chuyuan Wang Linxuan Zhang Chongdang Liu 

作者机构：National CIMS Engineering Research Center Tsinghua UniversityBeijing 100084P.R.China 

出 版 物：《International Journal of Modeling, Simulation, and Scientific Computing》 (建模、仿真和科学计算国际期刊（英文）)

年 卷 期：2018年第9卷第5期

页      面：74-91页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：Robotic cell adaptive scheduling simulation particle swarm optimization extreme gradient boosting. 

摘      要：In order to deal with the dynamic production environment with frequent fluctuation of processing time,robotic cell needs an efficient scheduling strategy which meets the real-time *** paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling *** method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler,which can adjust the scheduling rules according to the current production *** the process of establishing scheduler,how to choose essential attributes is the main *** order to solve the low performance and low efficiency problem of embedded feature selection method,based on the application of Extreme Gradient Boosting model(XGBoost)to obtain the adaptive scheduler,an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization(PSO)is employed to acquire the optimal subset of *** results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching *** the same time,it can meet the demand of real-time scheduling.