Research on Distributed Flow Shop Scheduling for Intelligent Production
In order to make the traditional flow shop scheduling model more flexible and intelligent to adapt to different production environments,scheduling strategy of distributed flow shop based on deep learning was proposed.By learning and analyzing a large amount of data in the distributed shop floor system,the strategy gradient method was used to obtain the approximate optimal solution after several iterations of optimization,and a more intelligent and adaptable production planning and scheduling strategy was obtained.It was verified by experiments and simulation.The results show that this method can improve production efficiency and resource utilization,and has potential in cost control.The research provides an advanced scheduling strategy for distributed production environment of manufactur industry,and provides more accurate and intelligent decision reference for shop floor managers.
production schedulingdistributed flow shopdeep learningscheduling strategystrategy gradient method
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