kobe parses different file format describing scheduling problems such as RCPSP and its variants. We give a list of the supported formats and data sets available in kobe-rcpsp, and credit the owners of these data sets. We have clean these data sets of all the meta-information that was uneccessary to us, but feel free to consult the references given for the full data set.

We formatted known optimum results in a CSV format that is common to every data set (e.g. data/rcpsp/j30.sm/optimum/optimum.csv).

Format originally used to evaluate RCPSP in Patterson (1984). It is the format used by the RanGen1 and RanGen2 generators.

Data sets:

1. Patterson: 110 instances.
2. RG300[1]: 480 instances, 300 activities, 4 resources.
3. RG30[2]: 900, 180, 240, 240, 240 instances, 30 activities, 4 resources. Resource data a bit "restricted" (in the way it was generated) on the RG30 sets.
4. MT[3]: 900, 800, 1200, 1200 instances, 30 activities, 0 resource. ResSet[4]: Can be merged with MT for resources specifications.

[1] Debels, Dieter and Vanhoucke, Mario. "A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem", 2007.

[2] Mario Vanhoucke, José Coelho, Dieter Debels, Broos Maenhout, Luís V. Tavares, "An evaluation of the adequacy of project network generators with systematically sampled networks", European Journal of Operational Research, Volume 187, Issue 2, 2008.

[3] Vanhoucke, M., 2010, "Measuring time: Improving project performance using earned value management" 1st ed., Springer: 164 pages.

[4] Testing and validating algorithms for resource-constrained project scheduling (in submission).

Format of the PSPLIB, a well-known library for RCPSP with sets of instances of 30, 60, 90 and 120 activities.

Data sets:

1. j30: 480 instances, 30 activities and 4 resources.
2. j60: 480 instances, 60 activities and 4 resources.
3. j90: 480 instances, 90 activities and 4 resources.
4. j120: 600 instances, 120 activities and 4 resources.

R. Kolisch and A. Sprecher, “PSPLIB-a project scheduling problem library: OR software-ORSEP operations research software exchange program,” European journal of operational research, vol. 96, no. 1, pp. 205–216, 1997.

http://www.om-db.wi.tum.de/psplib/getdata.cgi?mode=sm

Format of the sets of instances produced by the ProGenMax instance generator. It concerns the RCPSP/max problem.

Data sets:

1. C, D: each 540 instances, 100 activities, 5 resources.
2. UBO_N: each 90 instances, N activities, 5 resources. N in 10, 20, 50, 100, 200, 500 and 1000.
3. J_N: 270 instances, N activities, 5 resources. N in 10, 20, 30.

http://www.wiwi.tu-clausthal.de/de/abteilungen/produktion/forschung/schwerpunkte/project-generator/rcpspmax/

Similar to RCPSP. With nonrenewable resources.

Data sets:

1. J_N: 3840 instances in total, N activities, N in 10, 12, 14, 16, 18, 20, 30

Similar to SM format but without some headers.

Data sets:

1. MMLIB50: 540 instances, 50 activities, 3 modes.
2. MMLIB100: 540 instances, 100 activities, 3 modes.
3. MMLIB+: 3240 instances, 50 or 100 activities, 3, 6 or 9 modes.

[1] V. Van Peteghem and M. Vanhoucke, “An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances,” European Journal of Operational Research, vol. 235, no. 1, pp. 62–72, May 2014.

The data sets and optimum are extracted and formatted from the following sources:

1. optimizizer.com
2. Yasumasa Tamura's Github

Data sets:

1. ABZ: 5 instances, due to Adams et al. [1].
2. FT: 3 instances, due to Fisher and Thompson [2].
3. LA: 40 instances due to Lawrence [3].
4. ORB: 10 instances due to Applegate and Cook [4].
5. SWV: 20 instances due to Storer et al. [5].
6. YN: 4 instances due to Yamada and Nakano [6].
7. TA: 80 instances due to Taillard [7].
8. Demirkol: 80 instances due to Demirkol et al. [8].
9. CAR: 8 instances due to Carlier and Pinson [9].

All data sets are formatted according to the following format:

# Comments start with '#'
<jobs> <machines>
Each line has N operations where an operation is a couple <machine_idx> <duration>.

[1] J. Adams, E. Balas, D. Zawack. "The shifting bottleneck procedure for job shop scheduling.", Management Science, Vol. 34, Issue 3, pp. 391-401, 1988.

[2] J.F. Muth, G.L. Thompson. "Industrial scheduling.", Englewood Cliffs, NJ, Prentice-Hall, 1963.

[3] S. Lawrence. "Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement).", Graduate School of Industrial Administration. Pittsburgh, Pennsylvania, Carnegie-Mellon University, 1984.

[4] D. Applegate, W. Cook. "A computational study of job-shop scheduling.", ORSA Journal on Computer, Vol. 3, Isuue 2, pp. 149-156, 1991.

[5] R.H. Storer, S.D. Wu, R. Vaccari. "New search spaces for sequencing problems with applications to job-shop scheduling.", Management Science Vol. 38, Issue 10, pp. 1495-1509, 1992.

[6] T. Yamada, R. Nakano. "A genetic algorithm applicable to large-scale job-shop problems.", Proceedings of the Second international workshop on parallel problem solving from Nature (PPSN'2). Brussels (Belgium), pp. 281-290, 1992.

[7] E. Taillard. "Benchmarks for basic scheduling problems", European Journal of Operational Research, Vol. 64, Issue 2, pp. 278-285, 1993.

[8] Ebru Demirkol, Sanjay Mehta, Reha Uzsoy. "Benchmarks for shop scheduling problems", European Journal of Operational Research, 109(1), 1998, pp. 137-141.

[9] J. Carlier and E. Pinson. An Algorithm for Solving the Job-Shop Problem. Management Science, 35(2):164–176, 1989.

The information about the data sets have been taken from [5], and optimum bounds from [5,6].

Data sets:

1. Barnes: 21 instances, due to Chambers and Barnes [1].
2. Brandimarte: 10 instances, due to Brandimarte [2].
3. Dauzere: 18 instances, due to Dauzère-Pérès and Paulli [3].
4. Hurink: 3 data sets of 66 instances each modifying the ABZ, FT, LA, ORB data sets of the job shop scheduling, due to Hurink [4].
   1. edata: Few operations may be assigned to more than one machine.
   2. rdata: Most of the operations may be assigned to some machines.
   3. vdata: All operations may be assigned to several machines.

All data sets have the following format (taken from the file DataSetExplanation.txt coming with the benchmarks):

1. In the first line there are (at least) 2 numbers: the first is the number of jobs and the second the number of machines (the 3rd is not necessary, it is the average number of machines per operation)
2. Every row represents one job: the first number is the number of operations of that job, the second number (let's say k>=1) is the number of machines that can process the first operation; then according to k, there are k pairs of numbers (machine,processing time) that specify which are the machines and the processing times; then the data for the second operation and so on...

[1] J. B. Chambers and J. W. Barnes. Flexible Job Shop Scheduling by Tabu Search. The University of Texas, Austin, TX, Technical Report Series ORP96-09, Graduate Program in Operations Research and Industrial Engineering, 1996.

[2] P. Brandimarte. Routing and Scheduling in a Flexible Job Shop by Tabu Search. Annals of Operations Research, 41(3):157–183, 1993.

[3] S. Dauzère-Pérès and J. Paulli. Solving the General Multiprocessor Job-Shop Scheduling Problem. Technical report, Rotterdam School of Management, Erasmus Universiteit Rotterdam, 1994.

[4] J. Hurink, B. Jurisch, and M. Thole, “Tabu search for the job-shop scheduling problem with multi-purpose machines,” Operations-Research-Spektrum, vol. 15, no. 4, pp. 205–215, 1994.

[5] Behnke, D., & Geiger, M. J. (2012). Test instances for the flexible job shop scheduling problem with work centers. Arbeitspapier/Research Paper/Helmut-Schmidt-Universität, Lehrstuhl für Betriebswirtschaftslehre, insbes. Logistik-Management.

[6] A. Schutt, T. Feydy, and P. J. Stuckey, “Scheduling Optional Tasks with Explanation,” in Principles and Practice of Constraint Programming, Berlin, Heidelberg, 2013, vol. 8124, pp. 628–644.