Open AccessResearch on optimization method of routing buffer linkage based on Q-learning
Author(s) -
Zhonghua Han,
Hongfeng Qi,
Daliang Chang,
Wenjuan Gong,
Shilong Dai
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2187/1/012058
Subject(s) - computer science , routing (electronic design automation) , linkage (software) , process (computing) , basis (linear algebra) , path (computing) , mathematical optimization , buffer (optical fiber) , q learning , algorithm , artificial intelligence , reinforcement learning , mathematics , computer network , telecommunications , biochemistry , chemistry , geometry , gene , operating system
According to the characteristics of the painting process of passenger car manufacturing enterprises, by formulating the routing buffer linkage rules based on the total renewal cost, the linkage process of the bus in the routing buffer is controlled, and an improved Q-learning (Q- The routing buffer of learning) algorithm quickly finds the optimal path method. According to the actual production situation, this method improves the dynamic parameters of the algorithm on the basis of the traditional Q-learning algorithm, and improves the optimization speed and accuracy of the algorithm by establishing the correlation between the work-in-process and its neighboring work-in-process in the current state. Through multiple sets of example simulation tests, the effectiveness of the Q-learning algorithm in solving the optimization problem of routing buffer linkage is verified.