A reinforcement learning based decision support system in textile manufacturing process

TL;DR

This paper presents a reinforcement learning-based decision support system for textile manufacturing, modeling a color fading ozonation process as an MDP and demonstrating its effectiveness in optimizing manufacturing decisions.

Contribution

It introduces a novel application of reinforcement learning to textile manufacturing, specifically modeling and optimizing the color fading ozonation process as an MDP.

Findings

The MDP model effectively represents the optimization problem.

Reinforcement learning successfully supports decision making in textile manufacturing.

The approach shows promising prospects for industrial application.

Abstract

This paper introduced a reinforcement learning based decision support system in textile manufacturing process. A solution optimization problem of color fading ozonation is discussed and set up as a Markov Decision Process (MDP) in terms of tuple {S, A, P, R}. Q-learning is used to train an agent in the interaction with the setup environment by accumulating the reward R. According to the application result, it is found that the proposed MDP model has well expressed the optimization problem of textile manufacturing process discussed in this paper, therefore the use of reinforcement learning to support decision making in this sector is conducted and proven that is applicable with promising prospects.