Journal of Computing and Information Science in Engineering

September 2022 Paper Spotlight: Online Maintenance Prioritization Via Monte Carlo Tree Search and Case-Based Reasoning

Share This Post

On Friday, September 30, 2022, 11:00 AM – 11:30 AM (Eastern US and Canada), Prof. Soundar Kumara and Michael Hoffman, (Penn State University, USA) presented their paper: Hoffman, M., Song, E., Brundage, M., and Kumara, S. (2022). Online Maintenance Prioritization Via Monte Carlo Tree Search and Case-Based Reasoning. ASME. J. Comput. Inf. Sci. Eng. August 2022; 22(4): 041005.

In case you missed it, a recording of the event is available through the link below.

Abstract: When 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.


Visit the ASME Digital Collection archives for JCISE

More To Explore


Special Issue on Geometric Data Processing and Analysis for Advanced Manufacturing

Geometric information, such as three-dimensional (3D) shapes and network topologies, has been increasingly explored in manufacturing research. For example, characterizing geometric information in 3D-printed parts, in-situ or ex-situ, opens opportunities for defect detection, quality improvement, and product customization. However, geometric data mining remains critically challenging. Geometric information is embedded in complex data structures, such as 3D point clouds, graphs, meshes, voxels, high-dimensional images, and tensors, which possess challenges for analysis due to their high-dimensionality, high-volume, unstructured, multimodality characteristics. Additional challenges stem from compromised data quality (e.g., noisy and incomplete data), the need for registration, etc.


2023 Reviewer’s Recognition

The Editor and Editorial Board of the Journal of Computing and Information Science in Engineering would like to thank all of the reviewers for volunteering their expertise and time reviewing manuscripts in 2023. Serving as reviewers for the journal is a critical service necessary to maintain the quality of our publication and to provide the authors with a valuable peer review of their work.