A study on a Q-Learning algorithm application to a manufacturing assembly problem. (April 2021)

• Record Type: Journal Article
• Authors: Neves, Miguel; Vieira, Miguel; Neto, Pedro
• Is Part Of: Journal of manufacturing systems. Volume 59(2021)
• Page Start: 426
• Page End: 440
• Publication Date: 2021-04
• Subjects: Reinforcement learning -- Q-Learning -- Assembly sequence -- Optimisation; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.02.014
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Reinforcement learning applied to manufacturing assembly. Modelling complex manufacturing decision-making problems. The system learns along time with actual operation. Optimisation of the assembly process time. Accuracy of 98.3% to achieve the optimal assembly sequence. Abstract: The development of machine learning algorithms has been gathering relevance to address the increasing modelling complexity of manufacturing decision-making problems. Reinforcement learning is a methodology with great potential due to the reduced need for previous training data, i.e. the system learns along time with actual operation. This study focuses on the implementation of a reinforcement learning algorithm in an assembly problem of a given object, aiming to identify the effectiveness of the proposed approach in the optimisation of the assembly process time. A model-free Q-Learning algorithm is applied, considering the learning of a matrix of Q-values (Q-table) from the successive interactions with the environment to suggest an assembly sequence solution. This implementation explores three scenarios with increasing complexity so that the impact of the Q-Learning's parameters and rewards is assessed to improve the reinforcement learning agent performance. The optimisation approach achieved very promising results by learning the optimal assembly sequence 98.3% of the times.