KR2021Proceedings of the 18th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning

Online event. November 3-12, 2021.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-99-7

Sponsored by
Published by

Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization

Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling

  1. Thomas Eiter(Vienna University of Technology)
  2. Tobias Geibinger(Vienna University of Technology)
  3. Nysret Musliu(Vienna University of Technology)
  4. Johannes Oetsch(Vienna University of Technology)
  5. Peter Skočovský(Bosch Center for Artificial Intelligence, Vienna University of Technology)
  6. Daria Stepanova(Bosch Center for Artificial Intelligence)


  1. Applications of KR
  2. Logic programming, answer set programming


We deal with a challenging scheduling problem on parallel-machines with sequence-dependent setup times and release dates from a real-world application of semiconductor work-shop production. There, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. This causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. This achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of Answer-Set Programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. The former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising KRR paradigm for this problem and is competitive with state-of-the-art CP and MIP solvers.