A Reinforcement Learning Method to Scheduling Problem of Steel Production Process. Issue 7 (April 2020)

• Record Type: Journal Article
• Authors: Guo, Fang; Li, Yongqiang; Liu, Ao; Liu, Zhan
• Is Part Of: Journal of physics. Volume 1486:Issue 7(2020)
• Publication Date: 2020-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1486/7/072035
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: 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.