Project title: Machine learning for optimisation of baggage handling and sorter systems for logistics
Big data and Machine Learning is currently a very popular field of research. In collaboration with the company BEUMER Group, I will utilise their large amounts of data and their emulator environments to optimise their Baggage Handling System (BHS) in airports.
I will compare their routing algorithms with a self-taught Reinforcement Learning (RL) system not unlike the system that in resent years have been able to beat professional players in games such as chess, backgammon and go, and play at superhuman level in Atari games.
Besides finding the shortest path through the BHS, the RL system might find patterns which could prevent deadlocks and other unwanted events. Currently such events are manually avoided by software developers.
One of the challenges is to describe exactly what such a system should optimise towards. Is it shortest path, lowest delay, highest throughput, etc.
Another very important part of such a system is the transparency, i.e. how well can we explain why the system does what it does. To address this part, I intend to use methods from the field of Explainable Artificial Intelligence.
Main supervisor: Prof. (Docent) Henrik Karstoft
Co-supervisors: Peter Gorm Larsen, Michael Nielsen, Morten Granum
