用于动态柔性作业车间调度的实时调度方法  

Real-time Scheduling Method for Dynamic Flexible Job Shop Scheduling

作　　者：蒋权 魏静萱[1] Jiang Quan;Wei Jingxuan(School of Computer Science and Technology,Xidian University,Xi'an 710065,China)

机构地区：[1]西安电子科技大学计算机科学与技术学院,陕西西安710065

出　　处：《系统仿真学报》2024年第7期1609-1620,共12页Journal of System Simulation

基　　金：国家自然科学基金(62272367)。

摘　　要：针对制造加工中的动态事件对调度方案的干扰,构建了带有机器故障和随机工件到达的多目标动态柔性作业车间调度问题模型,提出多目标近端策略优化(multi-objective proximal policy optimization,MPPO)的实时调度方法。MPPO算法训练了RA(routing agent)和SA(sequencing agent)两个智能体以实现实时调度并实时处理动态事件;采用权重向量与奖励向量线性组合作为奖励信号,并保存每个权重向量的智能体参数以优化多个目标;结合目标函数为两个智能体定义了所需的状态信息、调度规则、奖励信号。在不同规模的动态调度问题下与9种调度规则组合进行对比,验证了MPPO算法训练的智能体学习到了合适的调度策略,能够保证实时调度的表现且能优化所有目标。A multi-objective dynamic flexible job shop scheduling problem model with machine breakdown and random jobs arrival is constructed to address the interference of dynamic events in manufacturing processing on the scheduling scheme,and a real-time scheduling method with multiobjective proximal policy optimization(MPPO)algorithm is proposed.The MPPO algorithm trains two agents,routing agent(RA)and sequencing agent(SA),for real-time scheduling and real-time processing of dynamic events.It employs a linear combination of weight vectors and reward vectors as reward signals and stores the agents'parameters for each weight vector to optimize multiple objectives.The required state information,scheduling rules,and reward signals are defined for the two agents in conjunction with the objective functions.A comparison with nine combinations of scheduling rules for dynamic scheduling problems of different scales verifies that the MPPO algorithm-trained agents have learned an appropriate scheduling policy,which can guarantee the performance of real-time scheduling and optimize all objectives.

关 键 词：动态调度 柔性作业车间调度 强化学习 多智能体 多目标优化 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]