Maintenance schedules by conditional inference trees

Wang, Shixuan, Syntetos, Aris A. ORCID: https://orcid.org/0000-0003-4639-0756, Flapper, Simme Douwe P., Cairano-Gilfedder, Carla Di and Naim, Mohamed M. ORCID: https://orcid.org/0000-0003-3361-9400 2026. Maintenance schedules by conditional inference trees. European Journal of Operational Research 10.1016/j.ejor.2026.08.022

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Abstract

Maintenance represents a large part of the total operational costs of vehicle fleets. We perform reliability analysis based on a dataset of maintenance records from the fleet of light commercial vehicles used by BT Fleet Solutions and their customers (Post Office, National Grid, etc.). In our preliminary analysis, we first verify that reliability deteriorates with age, mileage, and the number of historical maintenance activities, as we also find heterogeneous behavior associated with different geographic locations and vehicle makes. Drawing on this background, we propose a dynamic maintenance policy. We build it on a data-driven method, Conditional Inference Trees (CIT), which provides a good balance between applicability (with loose assumptions) and interpretability (being a “white-box” solution). Since there is no closed-form solution for maintenance costs based on tree-structured reliability, we develop a simulation-based method to estimate the maintenance cost for the CIT-based maintenance policy, along with a new backward optimization procedure for the operational parameters. Based on the cost analysis, we demonstrate the effectiveness of the CIT-based maintenance policy in comparison to two prevalent maintenance policies. Finally, we identify opportunities for further improvements in the operations of the company, and for maintenance operations in general.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Business (Including Economics)
Publisher:	Elsevier
ISSN:	0377-2217
Date of First Compliant Deposit:	18 August 2026
Date of Acceptance:	11 August 2026
Last Modified:	18 Aug 2026 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/189058

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