Integrating Robot Assignment and Maintenance Management: A Multi-Agent Reinforcement Learning Approach for Holistic Control

Kshitij Bhatta; Qing Chang

RA-L 20235 citations

Integrating Robot Assignment and Maintenance Management: A Multi-Agent Reinforcement Learning Approach for Holistic Control

Abstract

Modern manufacturing requires effective integration of production control and maintenance scheduling to improve productivity and quality. However, there have been few studies on this integrated control due to a lack of a comprehensive manufacturing system model. In response to this challenge, this letter presents a mathematical model framework for a mobile multi-skilled robot-operated manufacturing system that integrates three essential control aspects: robot assignment, maintenance scheduling, and product quality. Furthermore, an integrated control scheme is formulated in the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework to showcase the proposed method's efficacy in control. Results show that the proposed integrated model outperforms models that consider only system-level parameters, as well as those that only address maintenance scheduling and quality-related parameters.

BibTeX

@inproceedings{ral2023_integratingrobot,
  title = {Integrating Robot Assignment and Maintenance Management: A Multi-Agent Reinforcement Learning Approach for Holistic Control},
  author = {Kshitij Bhatta and Qing Chang},
  booktitle = {RA-L 2023},
  year = {2023}
}