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Open timetabling instances in MIPLIB 2017

Find better course, school or public-transport timetables, or prove optimality, on seven open MIPLIB 2017 timetabling instances.

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Where it stands

  1. OpenedOct 21, 2026
  2. Exploring0 routes
  3. Computing0 jobs
  4. Verified0 routes passing
  5. Adopted1 merged

Each route is a branch; a job is one computation on it; a check is the verdict of the project’s verifier; adoption is a merged pull request. They are separate steps: a finished job is not a passing check, and a passing check is not yet adopted.

Seven open MIPLIB 2017 instances come from timetabling: course and school timetables at real universities and schools, and periodic or energy-aware timetables for public transport. Four of their best solutions improved during 2026, yet none has been proved optimal.

Problem

Each instance is a mixed-integer linear program, given as an MPS file, that minimises a weighted penalty or cost over timetables satisfying hard constraints such as room capacities, clashes between courses or students, and timing windows. For each instance below, find a feasible solution with a lower objective value than the best known, or prove that the best known value is optimal.

Instance Application Variables Best known Last improved
comp12-2idx curriculum-based course timetabling (ITC2007 instance comp12, time assignment stage) 11,863 277 July 2024
kottenpark09 educational timetabling in the XHSTT format 2,893,026 1,715 July 2020
rwth-timetable university course timetabling, RWTH Aachen 923,564 772,347 July 2026
tpl-tub-ws1617 post-enrolment course timetabling, TU Berlin 747,601 121,065 July 2026
splan1 university course timetabling 1,317,382 −194,385.04 June 2026
r4l4-02-tree-bounds-50 periodic timetabling in public transport (PESP) 11,468 499,061,405 September 2026
zeil shifting train departures to minimise peak power use 70,116 1,081.3585 October 2024

Background

Timetabling is a classic application of integer programming in which real instances are large and heavily constrained. comp12-2idx is the first stage of a decomposition of an instance from the 2007 International Timetabling Competition (Lach and Lübbecke, 2012; Bonutti et al., 2012). kottenpark09 comes from the XHSTT format for real educational timetabling problems, for which Fonseca, Santos, Carrano and Stidsen (2017) studied integer programming techniques. r4l4-02-tree-bounds-50 is a Periodic Event Scheduling Problem built from data of the LinTim project and the PESPlib collection (Goerigk, Schachtebeck and Schöbel, 2013), and zeil adapts a draft railway timetable for a small part of the German network so that the peak power consumption, averaged over 15-minute intervals, is as small as possible.

What is known

  • The values above are MIPLIB's best known objectives (solution file version 37, 24 September 2026); MIPLIB publishes no dual bounds for them.
  • rwth-timetable and tpl-tub-ws1617 were improved on 21 July 2026 by Michael Winkler and Ed Klotz with Gurobi 13.0 at default settings apart from a zero MIP gap, and splan1 on 9 June 2026 by Yuji Shinano and Stefan Vigerske.
  • The current best for r4l4-02-tree-bounds-50 was found on 22 September 2026 by Yicheng Huang, Wenzhi Gao, Dongdong Ge, Madeleine Udell and Yinyu Ye.
  • For kottenpark09, Ed Klotz reports that CPLEX needed about 10 hours on 12 threads to reach solutions in the 4,000 to 5,000 range; the best value has stood since 2020.

What would count as progress

  • A solution file in MIPLIB's SOL format with a lower objective than the table. MIPLIB's solution checker verifies integrality, bounds and every constraint in exact arithmetic and recomputes the objective within seconds.
  • A proof that a listed value is optimal, for example a matching dual bound with a certificate that can be checked independently. This is a separate and much harder kind of progress than a better solution.

References

  1. A. Gleixner et al. (2021). MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library. Mathematical Programming Computation 13, 443–490. https://doi.org/10.1007/s12532-020-00194-3
  2. G. Lach and M. E. Lübbecke (2012). Curriculum based course timetabling: new solutions to Udine benchmark instances. Annals of Operations Research 194, 255–272. https://doi.org/10.1007/s10479-010-0700-7
  3. A. Bonutti, F. De Cesco, L. Di Gaspero and A. Schaerf (2012). Benchmarking curriculum-based course timetabling: formulations, data formats, instances, validation, visualization, and results. Annals of Operations Research 194, 59–70. https://doi.org/10.1007/s10479-010-0707-0
  4. G. H. G. Fonseca, H. G. Santos, E. G. Carrano and T. J. R. Stidsen (2017). Integer programming techniques for educational timetabling. European Journal of Operational Research 262, 28–39. https://doi.org/10.1016/j.ejor.2017.03.020
  5. M. Goerigk, M. Schachtebeck and A. Schöbel (2013). Evaluating line concepts using travel times and robustness. Public Transport 5, 267–284. https://doi.org/10.1007/s12469-013-0072-x

Source

Imported from MIPLIB 2017, Open tag, curated by Zuse Institute Berlin. Open according to the source as of 2026-10-20. The statement above is written in our own words; see the source for the original wording, history and discussion.

README.md