Digital-twin-based decision support of dynamic maintenance task prioritization using simulation-based optimization and genetic programming

https://doi.org/10.1016/j.dajour.2022.100039Get rights and content
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Highlights

  • A Digital-Twin-based Decision Support System (DT-DSS) for conducting short-term corrective maintenance task prioritization.

  • DT-DSS involves a heuristic generation method to generate optimal composite dispatching rules (CDRs).

  • GPSO-HGM uses multiple criteria, such as operator competence, utilization, walking distances, work-in-process, etc.

  • A real-world application study has been performed to evaluate the data-driven CDRs generated.

  • Results show improved productivity using the CDRs generated by GPSO-HGM when compared to ordinary priority dispatching rules.

Abstract

Modern decision support systems need to be connected online to equipment so that the large amount of data available can be used to guide the decisions of shop floor operators, making full use of the potential of industrial manufacturing systems. This paper investigates a novel optimization and data analytic method to implement such a decision support system, based on heuristic generation using genetic programming and simulation-based optimization running on a digital twin. Such a digital-twin-based decision support system allows the proactively searching of the best attribute combinations to be used in a data-driven composite dispatching rule for the short-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities use multiple criteria, including competence, utilization, operator walking distances on the shop floor, bottlenecks, work-in-process, and parallel resource availability. The data-driven composite dispatching rules are evaluated using a digital twin, built for a real-world machining line, which simulates the effects of decisions regarding disruptions. Experimental results show improved productivity because of using the composite dispatching rules generated by such heuristic generation method compared to the priority dispatching rules based on similar attributes and methods. The improvement is more pronounced when the number of operators is reduced. This paper thus offers new insights about how shop floor data can be transformed into useful knowledge with a digital-twin-based decision support system to enhance resource efficiency.

Keywords

Decision support systems
Digital Twin
Short-term corrective maintenance priority
Genetic programming
Simulation-based optimization
Bottleneck

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