نبذة مختصرة : Modern decision support systems need to be connected online to equipment so that the large amount of dataavailable can be used to guide the decisions of shop floor operators, making full use of the potential of industrialmanufacturing systems. This paper investigates a novel optimization and data analytic method to implementsuch a decision support system, based on heuristic generation using genetic programming and simulation-basedoptimization running on a digital twin. Such a digital-twin-based decision support system allows the proactivelysearching of the best attribute combinations to be used in a data-driven composite dispatching rule for theshort-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities usemultiple 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 evaluatedusing a digital twin, built for a real-world machining line, which simulates the effects of decisions regardingdisruptions. Experimental results show improved productivity because of using the composite dispatching rulesgenerated by such heuristic generation method compared to the priority dispatching rules based on similarattributes and methods. The improvement is more pronounced when the number of operators is reduced. Thispaper thus offers new insights about how shop floor data can be transformed into useful knowledge with adigital-twin-based decision support system to enhance resource efficiency.
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