Optimal Age-based Maintenance Under Population HeterogeneitySee the Tool


“We consider a system with a finite lifespan and a single critical component that is subject to random failures. An age-based replacement policy is applied to preventively replace the component before its failure. The components used for replacement come from either a weak population or a strong population, referred to as population heterogeneity. Even though the true type of the population is unknown, the decision maker has a belief on the probability of components coming from a weak population. We build a partially observable Markov decision process model that optimizes the age-based replacement decisions with the objective of minimizing the total cost over the lifespan of the system. The resulting optimal policy updates the belief variable in a Bayesian way by using the data obtained throughout the system lifespan. It optimally balances the trade-off between the cost of learning the true population type (via deliberately delaying the preventive replacement time to better learn the population type) and the cost of maintenance activities.” 

“To the best of our knowledge, this model is the first to study optimally balancing the exploration and exploitation in a finite lifespan problem for a critical component under population heterogeneity.” 



A tool is developed based on the mathematical model mentioned above. The model is developed in R and the user interface is programmed as an R shiny app. A shiny dashboard is created to build a user interface. The codes for the model and a user interface to view the output of the model are shared in a Github repository.


Link to tool:  https://git.io/JzEc1

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