autonomous guided vehicles

Planning traveling routes for vehicles in the setting of a Vehicle Routing Problem (VRP) is an old and well-known NP-hard algorithmic challenge. When studied in the context of warehouse picking processes there are various issues that compound the problem such as rack layouts, traffic bottlenecks, shipping time-windows, pick item precedences and vehicle capacities. There are also opportunities in that the warehouse is a relatively closed environment with short operational horizons as opposed to e.g. truck delivery and waterway shipping where forecasting of traffic through time is more difficult. The warehouse is hence a good testing ground for new traffic control systems that include the state of the art in artificial intelligence. New systems can also be live tested for autonomous ground vehicles (AGV) with relative ease and safety.

Planning traveling routes for vehicles in the setting of a Vehicle Routing Problem (VRP) is an old and well-known NP-hard algorithmic challenge. When studied in the context of warehouse picking processes there are various issues that compound the problem such as rack layouts, traffic bottlenecks, shipping time-windows, pick item precedences and vehicle capacities. There are also opportunities in that the warehouse is a relatively closed environment with short operational horizons as opposed to e.g. truck delivery and waterway shipping where forecasting of traffic through time is more difficult. The warehouse is hence a good testing ground for new traffic control systems that include the state of the art in artificial intelligence. New systems can also be live tested for autonomous ground vehicles (AGV) with relative ease and safety.