Data-driven preventive maintenance for a heterogeneous machine portfolio
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Journal article with impact factorPublication Year
2023Journal
Operations Research LettersPublication Volume
51Publication Issue
2Publication Begin page
163Publication End page
170
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We describe a data-driven approach to optimize periodic maintenance policies for a heterogeneous portfolio with different machine profiles. When insufficient data are available per profile to assess failure intensities and costs accurately, we pool the data of all machine profiles and evaluate the effect of (observable) machine characteristics by calibrating appropriate statistical models. This reduces maintenance costs compared to a stratified approach that splits the data into subsets per profile and a uniform approach that treats all profiles the same.Knowledge Domain/Industry
Operations & Supply Chain Managementae974a485f413a2113503eed53cd6c53
10.1016/j.orl.2023.01.006
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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International