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Automated storage and retrieval systems are principal components of modernproduction and warehouse facilities. In particular, automated guided vehiclesnowadays substitute human-operated pallet trucks in transporting productionmaterials between storage locations and assembly stations. While low-levelcontrol systems take care of navigating such driverless vehicles alongprogrammed routes and avoid collisions even under unforeseen circumstances, inthe common case of multiple vehicles sharing the same operation area, theproblem remains how to set up routes such that a collection of transport tasksis accomplished most effectively. We address this prevalent problem in thecontext of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scaleproducer of commercial vehicles, where routes for automated guided vehiclesused in the production process have traditionally been hand-coded by humanengineers. Such ad-hoc methods may suffice as long as a running productionprocess remains in place, while any change in the factory layout or productiontargets necessitates tedious manual reconfiguration, not to mention the missingportability between different production plants. Unlike this, we propose adeclarative approach based on Answer Set Programming to optimize the routestaken by automated guided vehicles for accomplishing transport tasks. Theadvantages include a transparent and executable problem formalization, provableoptimality of routes relative to objective criteria, as well as elaborationtolerance towards particular factory layouts and production targets. Moreover,we demonstrate that our approach is efficient enough to deal with the transporttasks evolving in realistic production processes at the car factory ofMercedes-Benz Ludwigsfelde GmbH.

Routing Driverless Transport Vehicles in Car Assembly with Answer Set Programming