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

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.CC BY-NC-ND 4.0Received 30 December 2021, Revised 9 March 2022, Accepted 15 March 2022, Available online 18 March 2022, Version of Record 2 April 2022.The co-authors would like to acknowledge the Knowledge Foundation (KKS), Sweden, for their funding through the research profile Virtual Factories with Knowledge-Driven Optimization at the University of Skövde which is partially related to this work.