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Automation and optimization are two key concepts for smart factories in industry 4.0. The production planning and scheduling optimization models are widely used to support the aforementioned key concepts. Due to the wide availability of various Industrial Internet of Things (IIoT) devices and predictive models, the factories of the future would be self-learned and capable to act in monetary situations. To achieve this, the planning optimization, dynamic rescheduling and production line balancing solutions should be available to todays’ production lines. In this paper, we are introducing a real-world example of a solution for production scheduling optimization and production line balancing based on genetic algorithm. The introduced application was developed over an established Cognitive Analytics Platform for Anomaly Detection. The application uses deployment, security and visualization services available by the platform. Therefore, this work presents alongside the optimization solution, the way the platform can be modified for serving other use cases besides the anomaly detection, in order to provide a complete tool for factory automation and optimization.
Georgiadis et al. (Sat,) studied this question.
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