Proceedings of the

The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK

Dependencies and Resource Constraints in Opportunistic Maintenance Modeling: A Systematic Literature Review

Lucas Equeter1,a, Phuc Do2,c, Pierre Dehombreux1,b and Benoit Iung2,d

1Department of Machine Design and Production Engineering, University of Mons, Belgium.

2CRAN, UMR CNRS 7039, Lorraine University, France.


Opportunistic maintenance (OM) allows for reducing downtime and reducing costs through performing several maintenance actions together thanks to the dependencies between components. Indeed, dependencies (economic, stochastic, structural, etc.) between system components impact the benefits of OM, and the number of variables in dependence modeling they create induce strong but scattered hypotheses in the literature. These hypotheses are assumptions made at the point of creating the model, of which the effects are discussed throughout the papers and often lead to the optimization of parameters. Examples of hypotheses are dependencies (structural, stochastic, geographical, etc.), human factors (skills, errors, etc.), and resources (tools, spare parts, etc.). Existing reviews either do not explore the variety of hypotheses, including dependencies [1], [2], or are not specific to OM modeling [3]-[5]. The present work reviews the current advancement in hypotheses related to dependencies and resource constraints, including human resources and workers' skills, in OM modeling and optimization, using a systematic literature review protocol [6]. The review is based on four relevant research questions that allow the selection of publications pertinent to the topic. The questions pertain to how workers' skills, dependences, and resource constraints are taken into account in OM modeling; how OM is defined in the corresponding literature; how economic dependency is modeled; and what the optimization objectives of the corresponding literature are. The results show the predominance of the structural and stochastic dependence in the corpus, contrasting with the scarcity of workers' skills modeling. The current approaches, therefore, tend to lack a global view of possible hypotheses, which may deter industrial applications due to limited hypotheses. Further research could focus on more comprehensive models that could better adjust to the varieties of the industrial world.

Keywords: Opportunistic maintenance, Dependency modeling, Resource constraints, Structural dependence, Stochastic dependence, Human factor.

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