强化学习技术在工业产品质检调度中的实践
Practical of Reinforcement Learning Techniques in Industrial Product Quality Inspection Scheduling
任靖辉1
作者信息
- 1. 云南永昌硅业股份有限公司,云南昆明 650500
- 折叠
摘要
针对工业产品质检调度问题,讨论了模拟退火算法和Q-Leaning强化学习算法的实践过程.首先描述了本次研究存在的问题,其次抽象出了质检顺序矩阵和质检时间矩阵并进行问题求解.在实际应用中,选择模拟退火算法还是Q-Learning算法取决于问题的特性和需求.若问题具有全局搜索需求,则模拟退火算法可能更适合;若问题可以建模为强化学习问题,则Q-Learning可能是更好的选择.以Q-Leaning对问题进行实践求解,得到工业产品质检调度甘特图,可为实际工业产品质检排程提供参考.
Abstract
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.
关键词
调度算法/Q-Leaning/产品质检/模拟退火算法Key words
scheduling algorithm/Q-Leaning/product quality inspection/simulated annealing algorithm引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据