Title: A Q-learning-based adaptive grouping policy for condition-based maintenance of a flow line manufacturing system
Authors: Yusuf Ozbek; Abe Zeid; Sagar Kamarthi
Addresses: Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA. ' Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA. ' Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA
Abstract: There has been a considerable progress in condition-based maintenance (CBM) in which maintenance actions are carried out as warranted by the condition of machines to reduce the associated maintenance costs and increase the availability of machines. If the maintenance activities are carried out individually, setup costs would be higher and the system downtime would be longer than if the maintenance activities are carried out together on a group of machines. So, finding an optimal grouping policy is an important problem in itself. This paper investigates a Q-learning algorithm to come up with a grouping policy that would reduce set up costs and increase the uptime efficiency of a flow line manufacturing system. The breakdown of even a single machine in a flow line system could affect the availability of the entire system, particularly when there are no storage buffers in between successive machines. The results reported here show that proposed Q-learning-based grouping policy is capable of reducing the number of repair or maintenance interruptions considerably.
Keywords: Q-learning; reinforcement learning; condition-based maintenance; grouping policies; group maintenance; adaptive groupings; flow line manufacturing; maintenance actions; machine conditions; maintenance costs; machine availability; maintenance activities; setup costs; system downtimes; algorithms; uptime efficiency; machine breakdowns; system availability; storage buffers; successive machines; repair interruptions; maintenance interruptions; collaborative enterprises; collaboration; maintenance modelling; maintenance management.
International Journal of Collaborative Enterprise, 2011 Vol.2 No.4, pp.302 - 321
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 23 Nov 2011 *