摘要
为解决复杂、繁琐的染色车间调度问题,根据印染生产过程的工艺特点和约束条件,建立了染色车间作业调度问题模型。为了提高调度系统对生产环境经常发生变化的自适应能力和全局优化能力,提出了一种基于蚂蚁智能与强化学习相结合的协商策略的多Agent动态调度方法。在该方法中,智能Agent能根据行为的历史反馈和立即反馈来选择相应的行为,也能根据算法的历史奖励来选择相应的智能调度算法,从而把一小部分工序任务的实时局部优化和大部分工序任务的全局优化结合起来。调度实例的求解结果验证了该方法的有效性。
Based on the process characteristics and restrictions of production process in dyeing industry,the dyeing workshop scheduling model was presented to solve the complicated scheudling problems.To adapt to the dynamic production environment changes and improve the global optimization performance of the scheduling system,a multi-agent dynamic scheduling method of coordination mechanism based on ant intelligence and reinforcement learning was proposed.In this method,intelligent agent selected the appropriate behavior in accordance with the historical feedback and immediate feedback,and also selected the appropriate intelligent scheduling algorithm based on the historical incentives of the algorithms so that the local optimization of a small portion of the real-time tasks was combined with the global optimization of other tasks.The scheduling examples results showed that the optimization assignment of dyeing jobs was dynamically realized via the intelligent decision-making of the agent.As compared with the contract net protocol,the communication amount was decreased and the efficiency of the system was improved.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2010年第3期611-620,共10页
Computer Integrated Manufacturing Systems
基金
国家863计划资助项目(2007AA04Z155)
国家自然科学基金资助项目(60874074)
浙江省自然科学基金资助项目(Y1090592)~~
关键词
调度
多AGENT系统
染色车间
蚂蚁智能
强化学习
scheduling
multi-agent system
dyeing shops
ant intelligence
reinforcement learning
