Practical of Reinforcement Learning Techniques in Industrial Product Quality Inspection Scheduling
In addressing the problem of industrial product quality inspection scheduling,this paper discusses the practical process of using both the simulated annealing algorithm and the Q-Learning reinforcement learning algorithm.It starts with a description of the problem at hand,and the abstraction of quality inspection order matrix and inspection time matrix for problem solving.In practical applications,the choice between the simulated annealing algorithm and Q-Learning depends on the characteristics and requirements of the problem.If the problem necessitates global search,the simulated annealing algorithm may be more suitable.If the problem can be modeled as a reinforcement learning problem,Q-Learning might be the better choice.This paper presents the practical application of Q-Learning to solve the problem,resulting in a Gantt chart for industrial product quality inspection scheduling,which provides reference for real-world industrial product quality scheduling.
NETL
NSTL
万方数据
维普
