Despite the hype around Artificial Intelligence (AI), successful AI applications in real industrial environments are still scarce. This especially applies to a fundamental component in many Supply Chains – the Warehouse. The need for AI solutions to support warehouse operations becomes even more pressing considering the emergence of “hybrid” warehouse models where manual pickers and automated guided vehicles (AGVs) or autonomous mobile robots (AMRs) co-work in a shared floorspace.
The co-ordination of both these hybrid fleets within the warehouse relies exclusively on the experience of the shift Supervisor or Warehouse Manager, often utilising commercial Warehouse Management Systems (WMS). Co-ordination issues involve different aspects of order picking operations which range from order batching and sorting, vehicle dispatching and traffic management. Communication issues also exist as multi-vendor AGVs and forklifts fleet management systems must interface with the WMS. Through ongoing digitization of warehouses, there are now readily available communication channels between the vehicle elements and the warehouse management systems. These channels, however, are often exclusive by vendor and are often bespoke to a given facility application. Bespoke middleware solutions are usually required to make warehouse systems work.
This 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 AGVs into existing digital and physical infrastructures. The ultimate target of FleetInt is to create an overarching picking management system that is vendor-agnostic and can easily integrate with different FMSs and WMSs while including AI elements for intelligent traffic management. While the data communication objectives will aim at standardising communication protocols between various warehouse management systems, the optimisation objectives will focus on selecting the best possible candidate route for a pick task so as to minimise the risk of traffic congestion and vehicle collisions or deadlocks. This will also impact alternative KPIs, such as order pick time, deadlock time and energy per pick.
Challenges in this project reflect the challenges deriving from the mixed fleet warehouse model, which are multi-faceted:
The main break-through capabilities of the foreseen system include:
A project funded under the SMART EUREKA CLUSTER on Advanced Manufacturing programme through Enterprise Ireland – Grant IR 2021 0057