The optimisation and productivity gains promised by Industry 4.0 digital transformation is not currently possible for many intralogistics sectors using existing infrastructure. This project leverages consortium expertise in material handling vehicle design & manufacture, robot fleet modelling, route-optimisation and machine learning to propose a disruptive new step in blended automated and semi-autonomous intralogistics fleet coordination. The project will develop interconnected intralogistics vehicle fleet technology coupled with machine learning to automatically optimise and manage mixed fleets of both manually operated forklifts and material handling AGV vehicles into existing digital and physical infrastructure. The aim is to design, develop and demonstrate an AI-based system to augment the capabilities of the supervisor in their job creating the picking orders and continuously managing the fleet vehicles assigned to these orders, while adapting to the dynamics in order requests and fleet availability to meet all deadlines and other constraints.