Data-driven solutions to enhance planning, operation and design tools in Industry 4.0 context

This thesis proposes three different data-driven solutions to be combined to state-of-the-art solvers and tools in order to primarily enhance their computational performances. The problem of efficiently designing the open sea floating platforms on which wind turbines can be mount on will be tackled, as well as the tuning of a data-driven engine's monitoring tool for maritime transportation. Finally, the activities of SAT and ASP solvers will be thoroughly studied and a deep learning architecture will be proposed to enhance the heuristics-based solving approach adopted by such software. The covered domains are different and the same is true for their respective targets. Nonetheless, the proposed Artificial Intelligence and Machine Learning algorithms are shared as well as the overall picture: promote Industrial AI and meet the constraints imposed by Industry 4.0 vision. The lesser presence of human-in-the-loop, a data-driven approach to discover causalities otherwise ignored, a special attention to the environmental impact of industries' emissions, a real and efficient exploitation of the Big Data available today are just a subset of the latter. Hence, from a broader perspective, the experiments carried out within this thesis are driven towards the aforementioned targets and the resulting outcomes are satisfactory enough to potentially convince the research community and industrialists that they are not just "visions" but they can be actually put into practice. However, it is still an introduction to the topic and the developed models are at what can be defined a "pilot" stage. Nonetheless, the results are promising and they pave the way towards further improvements and the consolidation of the dictates of Industry 4.0.