| Περίληψη: | Due to the massive growth in available data to consider, decision making within the factory setting has become ever more complex. Also the increasing market demands concerning product quality and timely delivery have further hindered the ability of having critical judgement and being decisive when dealing with manufacturing problems. Thus using only human resources to form decisions and achieve manufacturing goals has become insufficient. New emerging technologies like the Internet of Things (IoT) and Cyber-Physical systems (CPS), which exist in the core of Industry 4.0 enabled factories, can help achieve decision making goals. The use of Industry 4.0 technologies in order to extract and handle useful data can be used to support human decision making. This can enable insight of the current and future status of a given production system, resulting in more accurate predictions and enhanced critical decisions. Production scheduling is one of the main problems production engineers have to tackle. The decisions taken considering production scheduling can greatly affect the whole production process. Industry 4.0 enabled decision support tools can help making production scheduling effective, while considering more data and parameters than ever before. As such this thesis proposes a methodology for production scheduling, based on historical and near real time data for checking resource and task status. The developed framework aims at providing useful insight to production engineers, assisting them in the decision-making process. Finally, the applicability of the framework was tested in a real-life case study of a structural steel manufacturing industry.
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