Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks

In this paper, we model preventive maintenance strategies for equipment composed of multi-non-identical components which have different time-to-failure probability distribution, by using a Markov decision process (MDP). The originality of this paper resides in the fact that a Monte Carlo reinforceme...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2019-01, Vol.30 (1), p.147-161
Main Authors: Barde, Stephane R. A., Yacout, Soumaya, Shin, Hayong
Format: Article
Language:English
Subjects:
Business and Management
Computer simulation
Control
Downtime
Local optimization
Machine learning
Machines
Manufacturing
Markov analysis
Markov processes
Mathematical models
Mean time to failure
Mechatronics
Military
Parameter estimation
Preventive maintenance
Probability distribution
Processes
Production
Robotics
Transition probabilities
Trucks
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Summary:In this paper, we model preventive maintenance strategies for equipment composed of multi-non-identical components which have different time-to-failure probability distribution, by using a Markov decision process (MDP). The originality of this paper resides in the fact that a Monte Carlo reinforcement learning (MCRL) approach is used to find the optimal policy for each different strategy. The approach is applied to an already existing published application which deals with a fleet of military trucks. The fleet consists of a group of similar trucks that are composed of non-identical components. The problem is formulated as a MDP and solved by a MCRL technique. The advantage of this modeling technique when compared to the published one is that there is no need to estimate the main parameters of the model, for example the estimation of the transition probabilities. These parameters are treated as variables and they are found by the modeling technique, while searching for the optimal solution. Moreover, the technique is not bounded by any explicit mathematical formula, and it converges to the optimal solution whereas the previous model optimizes the replacement policy of each component separately, which leads to a local optimization. The results show that by using the reinforcement learning approach, we are able of getting a 36.44 % better solution that is less downtime.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-016-1237-7