Open AccessA Reinforcement Learning Method to Scheduling Problem of Steel Production Process
Author(s) -
Fang Guo,
Yongqiang Li,
Ao Liu,
Zhan Li
Publication year - 2020
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 - 10.1088/1742-6596/1486/7/072035
Subject(s) - reinforcement learning , computer science , scheduling (production processes) , flow shop scheduling , markov decision process , reinforcement , job shop scheduling , mathematical optimization , markov process , artificial intelligence , engineering , schedule , mathematics , statistics , structural engineering , operating system
In this paper, a reinforcement learning method is utilized to solve the steel production scheduling problem. Based on characteristics of steel production processing, the model of hybrid flow-shop scheduling problem is constructed. Then the model is attributed to a Markov Decision Process, and corresponding states, actions, reward function are put forward. When trading off the exploration and exploitation, an improved ε -greedy policy is designed. Finally, this hybrid flow-shop scheduling model based on reinforcement learning is applied to the scheduling example of steel production processing. Compared to genetic algorithm, the reinforcement learning method gets the better result.