(Data-driven) knowledge representation in Industry 4.0 scheduling problems

Abstract

Industry 4.0 raises the need for a closer integration of management systems in manufacturing companies. Such process is driven by Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Starting from the potential of these technologies, a knowledge architecture aimed at addressing scheduling problems is proposed. Scheduling-support systems generally do not solve real-world scheduling problems, being instead only capable of solving simplified versions, producing solutions that human schedulers adapt to real problems. The architecture aims to record and consolidate the empirical knowledge generated by the solutions of actual scheduling problems. In this way, it summarizes the implicit criteria used by human schedulers. The architecture presented here records this knowledge in data structures compatible with the structure of scheduling problems. In further iterations this knowledge crystallizes into a sound and smart structure.