Integrating machine learning techniques into optimal maintenance scheduling

Yeardley, A.S. orcid.org/0000-0001-7996-0589, Ejeh, J.O. orcid.org/0000-0003-2542-1496, Allen, L. orcid.org/0000-0001-7669-3534 et al. (2 more authors) (2022) Integrating machine learning techniques into optimal maintenance scheduling. Computers & Chemical Engineering, 166. 107958. ISSN: 0098-1354

Abstract

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2022 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keywords: Predictive maintenance; Optimisation; Maintenance scheduling; Machine learning; Time estimation model
Dates:
  • Accepted: 10 August 2022
  • Published (online): 22 August 2022
  • Published: October 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > School of Chemical, Materials and Biological Engineering
The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield)
Funding Information:
Funder
Grant number
EUROPEAN COMMISSION - HORIZON 2020
884418
Depositing User: Symplectic Sheffield
Date Deposited: 29 Aug 2025 10:53
Last Modified: 29 Aug 2025 10:53
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: 10.1016/j.compchemeng.2022.107958
Related URLs:
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 9: Industry, Innovation, and Infrastructure
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