# Routing Driverless Transport Vehicles in Car Assembly with Answer Set Programming

arXiv: https://arxiv.org/abs/1804.10437 · HTML: https://mlsanity.com/paper/1804.10437/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 7 of 7)

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- [Operating Room (Re)Scheduling with Bed Management via ASP](https://mlsanity.com/paper/2105.02283/) (2021)
- [Specifying and Exploiting Non-Monotonic Domain-Specific Declarative Heuristics in Answer Set Programming](https://mlsanity.com/paper/2209.09066/) (2022)
- [Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration](https://mlsanity.com/paper/2308.06411/) (2023)
- [Argumentation for Explainable Workforce Optimisation (with Appendix)](https://mlsanity.com/paper/2508.15118/) (2025)

## Abstract (arXiv)

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.