Routing Driverless Transport Vehicles in Car Assembly with Answer Set
  Programming
  Automated storage and retrieval systems are principal components of modern
production and warehouse facilities. In particular, automated guided vehicles
nowadays substitute human-operated pallet trucks in transporting production
materials between storage locations and assembly stations. While low-level
control systems take care of navigating such driverless vehicles along
programmed routes and avoid collisions even under unforeseen circumstances, in
the common case of multiple vehicles sharing the same operation area, the
problem remains how to set up routes such that a collection of transport tasks
is accomplished most effectively. We address this prevalent problem in the
context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale
producer of commercial vehicles, where routes for automated guided vehicles
used in the production process have traditionally been hand-coded by human
engineers. Such ad-hoc methods may suffice as long as a running production
process remains in place, while any change in the factory layout or production
targets necessitates tedious manual reconfiguration, not to mention the missing
portability between different production plants. Unlike this, we propose a
declarative approach based on Answer Set Programming to optimize the routes
taken by automated guided vehicles for accomplishing transport tasks. The
advantages include a transparent and executable problem formalization, provable
optimality of routes relative to objective criteria, as well as elaboration
tolerance towards particular factory layouts and production targets. Moreover,
we demonstrate that our approach is efficient enough to deal with the transport
tasks evolving in realistic production processes at the car factory of
Mercedes-Benz Ludwigsfelde GmbH.
