A Proactive Model for Joint Maintenance and Logistics Optimization in the Frame of Industrial Internet of Things

Bousdekis, Alexandros; Mentzas, Gregoris

doi:10.1007/978-3-319-95666-4_3

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…[38,42,45,50,51,53] Similar to the previous area, most of the joint scheduling and planning algorithms rely on the assumption of perfect maintenance or replacement. From the reviewed papers, only [38,39] deal with decision methods capable of recommending both perfect and imperfect maintenance actions. Moreover, apart from [40], the rest of the reviewed papers were involved with single-component systems.…”

Section: Joint Scheduling and Planningmentioning

confidence: 99%

“…However, system integration aspects of decision-making algorithms in terms of interoperability, service communication, modularity, scalability, and flexibility are not discussed. On the other hand, architectures that include decision-making functionalities for maintenance have started to emerge [39,[76][77][78][79][80]. Following the trend of diagnostic and prognostic algorithms, decision-making algorithms need to be integrated horizontally and vertically according to the Industry 4.0 principles.…”

Section: System Integrationmentioning

confidence: 99%

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A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

et al. 2021

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Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. The current paper reviews the literature on data-driven decision-making in maintenance and outlines directions for future research towards data-driven decision-making for Industry 4.0 maintenance applications. The main research directions include the coupling of decision-making with augmented reality for seamless interfacing that combines the real and virtual worlds of manufacturing operators; methods and techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices; integration of maintenance decision-making with other operations such as scheduling and planning; utilization of the cloud continuum for optimal deployment of decision-making services; capability of decision-making methods to cope with big data; incorporation of advanced security mechanisms; and coupling decision-making with simulation software, autonomous robots, and other additive manufacturing initiatives.

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