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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1	# Open timetabling instances in MIPLIB 2017
2
3	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.
4
5	## Problem
6
7	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.
8
9	\| Instance \| Application \| Variables \| Best known \| Last improved \|
10	\|---\|---\|---\|---\|---\|
11	\| `comp12-2idx` \| curriculum-based course timetabling (ITC2007 instance comp12, time assignment stage) \| 11,863 \| 277 \| July 2024 \|
12	\| `kottenpark09` \| educational timetabling in the XHSTT format \| 2,893,026 \| 1,715 \| July 2020 \|
13	\| `rwth-timetable` \| university course timetabling, RWTH Aachen \| 923,564 \| 772,347 \| July 2026 \|
14	\| `tpl-tub-ws1617` \| post-enrolment course timetabling, TU Berlin \| 747,601 \| 121,065 \| July 2026 \|
15	\| `splan1` \| university course timetabling \| 1,317,382 \| −194,385.04 \| June 2026 \|
16	\| `r4l4-02-tree-bounds-50` \| periodic timetabling in public transport (PESP) \| 11,468 \| 499,061,405 \| September 2026 \|
17	\| `zeil` \| shifting train departures to minimise peak power use \| 70,116 \| 1,081.3585 \| October 2024 \|
18
19	## Background
20
21	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.
22
23	## What is known
24
25	- The values above are MIPLIB's best known objectives (solution file version 37, 24 September 2026); MIPLIB publishes no dual bounds for them.
26	- `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.
27	- 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.
28	- 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.
29
30	## What would count as progress
31
32	- 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.
33	- 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.
34
35	## References
36
37	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
38	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
39	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
40	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
41	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
42
43	## Source
44
45	Imported from [MIPLIB 2017, Open tag](https://miplib.zib.de/tag_open.html), 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.