A reinforcement learning method to scheduling problem of steel production process

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.