Online Maintenance Prioritization Via Monte Carlo Tree Search and Case-Based Reasoning

Author:

Hoffman Michael1,Song Eunhye1,Brundage Michael2,Kumara Soundar1

Affiliation:

1. Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802

2. National Institute of Standards and Technology, Gaithersburg, MD 20899

Abstract

AbstractWhen maintenance resources in a manufacturing system are limited, a challenge arises in determining how to allocate these resources among multiple competing maintenance jobs. This work formulates an online prioritization problem to tackle this challenge using a Markov decision process (MDP) to model the system behavior and Monte Carlo tree search (MCTS) to seek optimal maintenance actions in various states of the system. Further, case-based reasoning (CBR) is adopted to retain and reuse search experience gathered from MCTS to reduce the computational effort needed over time and to improve decision-making efficiency. The proposed method results in increased system throughput when compared to existing methods of maintenance prioritization while also reducing the computation time needed to identify optimal maintenance actions as more information is gathered. This is especially beneficial in manufacturing settings where maintenance decisions must be made quickly to minimize the negative performance impact of machine downtime.

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications,Software

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