Although the IHTC-2024 has concluded, research on the Integrated Healthcare Timetabling Problem (IHTP) is ongoing. This page collects all information related to the IHTP that extends beyond the scope of the competition.
We have two additional datasets for the IHTP, one comprised of 9 small instances with known optimal solutions and another with 24 instances with longer decision horizons (between 5-8 weeks). The small dataset is available here and was designed that with the intention of evaluating the performance of non-exact methods. The larger dataset is available here.
The IHTP instance generator is available as a zip here.
The generator can be run with Python 3.2+ and invoked as follows:
python generate.py [arguments]If no arguments are specified, a mix of random and fixed values are used and the generated instance is printed to `stdout`.
For the full list of parameters and more details about the generator, please refer to the readme.
Below, we report the current upper bounds for the IHTP instances. These bounds are based on the best solutions found by both participants of the IHTC-2024 competition as well as other researchers. You can report a new upper bound by submitting a JSON-formatted solution to Eugenia's email address.
| Instance | Cost | Solution |
|---|---|---|
| i01 | 3842 | sol_i01.json |
| i02 | 1264 | sol_i02.json |
| i03 | 10490 | sol_i03.json |
| i04 | 1884 | sol_i04.json |
| i05 | 12760 | sol_i05.json |
| i06 | 10671 | sol_i06.json |
| i07 | 4985 | sol_i07.json |
| i08 | 6249 | sol_i08.json |
| i09 | 6611 | sol_i09.json |
| i10 | 20705 | sol_i10.json |
| Instance | Cost | Solution |
|---|---|---|
| i11 | 25938 | sol_i11.json |
| i12 | 12375 | sol_i12.json |
| i13 | 17328 | sol_i13.json |
| i14 | 9591 | sol_i14.json |
| i15 | 12486 | sol_i15.json |
| i16 | 10139 | sol_i16.json |
| i17 | 40535 | sol_i17.json |
| i18 | 37660 | sol_i18.json |
| i19 | 43857 | sol_i19.json |
| i20 | 29098 | sol_i20.json |
| Instance | Cost | Solution |
|---|---|---|
| i21 | 24526 | sol_i21.json |
| i22 | 47861 | sol_i22.json |
| i23 | 37550 | sol_i23.json |
| i24 | 33221 | sol_i24.json |
| i25 | 11517 | sol_i25.json |
| i26 | 64352 | sol_i26.json |
| i27 | 50976 | sol_i27.json |
| i28 | 75172 | sol_i28.json |
| i29 | 12199 | sol_i29.json |
| i30 | 37387 | sol_i30.json |
| Instance | Cost | Solution |
|---|---|---|
| m01 | 3384 | sol_m01.json |
| m02 | 12211 | sol_m02.json |
| m03 | 6697 | sol_m03.json |
| m04 | 3318 | sol_m04.json |
| m05 | 11956 | sol_m05.json |
| m06 | 28250 | sol_m06.json |
| m07 | 7329 | sol_m07.json |
| m08 | 14976 | sol_m08.json |
| m09 | 32967 | sol_m09.json |
| m10 | 26015 | sol_m10.json |
| Instance | Cost | Solution |
|---|---|---|
| m11 | 35030 | sol_m11.json |
| m12 | 11665 | sol_m12.json |
| m13 | 31189 | sol_m13.json |
| m14 | 13368 | sol_m14.json |
| m15 | 28080 | sol_m15.json |
| m16 | 17271 | sol_m16.json |
| m17 | 22365 | sol_m17.json |
| m18 | 7778 | sol_m18.json |
| m19 | 21790 | sol_m19.json |
| m20 | 5285 | sol_m20.json |
| Instance | Cost | Solution |
|---|---|---|
| m21 | 25170 | sol_m21.json |
| m22 | 21330 | sol_m22.json |
| m23 | 18301 | sol_m23.json |
| m24 | 30873 | sol_m24.json |
| m25 | 33985 | sol_m25.json |
| m26 | 69730 | sol_m26.json |
| m27 | 28028 | sol_m27.json |
| m28 | 45913 | sol_m28.json |
| m29 | 48082 | sol_m29.json |
| m30 | 31649 | sol_m30.json |
A research group at the Universidad de La Laguna (Spain) has kindly provided a tool for visualizing solutions to the IHTP.
Detailed information about the tool’s functionalities, along with installation instructions, is available on the tool’s GitHub repository