Dataset File
Description
Dataset proposed in the paper Kolisch, R., & Sprecher, A. (1997). PSPLIB - A project scheduling problem library. European Journal of Operational Research, 96(1), 205-216. https://doi.org/10.1016/S0377-2217(96)00170-1
Number of instances
600
Format
sm
Statistics
Set statistics:
Number of instances: 600
- Solved (exact proc.): 318 (53.00%)
- Closed (LB=UB): 328 (54.67%)
- Open (LB<UB): 272 (45.33%)
Average deviation over CPM:
- Lower bound: 25.93%
- Upper bound: 28.92%
Sum of lower bounds: 71471
Sum of upper bounds: 73175
Open time units: 1704 (2.38%)
Avg CPU lower bounds: 584.853s (max 37758.000s)
Avg CPU upper bounds: 260.587s (max 37758.000s)
Avg CPU optimal sol.: 411.648s (max 37758.000s)
Reference 10:
- Lower bounds: 147 (24.50%)
Reference 3:
- Upper bounds: 252 (42.00%)
Reference 11:
- Lower bounds: 88 (14.67%)
- Upper bounds: 42 (7.00%)
- Optimal solutions: 82 (13.67%)
Reference 4:
- Lower bounds: 29 (4.83%)
- Upper bounds: 4 (0.67%)
- Optimal solutions: 28 (4.67%)
Reference 2:
- Lower bounds: 43 (7.17%)
Reference 7:
- Lower bounds: 41 (6.83%)
- Upper bounds: 135 (22.50%)
- Optimal solutions: 136 (22.67%)
Reference 1:
- Upper bounds: 56 (9.33%)
- Optimal solutions: 55 (9.17%)
Reference 12:
- Lower bounds: 28 (4.67%)
- Upper bounds: 99 (16.50%)
- Optimal solutions: 17 (2.83%)
Reference 5:
- Lower bounds: 30 (5.00%)
Reference 9:
- Upper bounds: 10 (1.67%)
Reference 8:
- Upper bounds: 2 (0.33%)
Reference 6:
- Lower bounds: 1 (0.17%)
Number of instances: 600
- Solved (exact proc.): 318 (53.00%)
- Closed (LB=UB): 328 (54.67%)
- Open (LB<UB): 272 (45.33%)
Average deviation over CPM:
- Lower bound: 25.93%
- Upper bound: 28.92%
Sum of lower bounds: 71471
Sum of upper bounds: 73175
Open time units: 1704 (2.38%)
Avg CPU lower bounds: 584.853s (max 37758.000s)
Avg CPU upper bounds: 260.587s (max 37758.000s)
Avg CPU optimal sol.: 411.648s (max 37758.000s)
Reference 10:
- Lower bounds: 147 (24.50%)
Reference 3:
- Upper bounds: 252 (42.00%)
Reference 11:
- Lower bounds: 88 (14.67%)
- Upper bounds: 42 (7.00%)
- Optimal solutions: 82 (13.67%)
Reference 4:
- Lower bounds: 29 (4.83%)
- Upper bounds: 4 (0.67%)
- Optimal solutions: 28 (4.67%)
Reference 2:
- Lower bounds: 43 (7.17%)
Reference 7:
- Lower bounds: 41 (6.83%)
- Upper bounds: 135 (22.50%)
- Optimal solutions: 136 (22.67%)
Reference 1:
- Upper bounds: 56 (9.33%)
- Optimal solutions: 55 (9.17%)
Reference 12:
- Lower bounds: 28 (4.67%)
- Upper bounds: 99 (16.50%)
- Optimal solutions: 17 (2.83%)
Reference 5:
- Lower bounds: 30 (5.00%)
Reference 9:
- Upper bounds: 10 (1.67%)
Reference 8:
- Upper bounds: 2 (0.33%)
Reference 6:
- Lower bounds: 1 (0.17%)
RecordSets
Record sets:
ID;1
Author(s); Demeulemeester, E. and Herroelen, W.
Reference;Demeulemeester, E., & Herroelen, W. (1992). A Branch-and-Bound Procedure for the Multiple Resource-Constrained Project Scheduling Problem. Management Science, 38(12), 1803-1818. http://www.jstor.org/stable/2632711
Date; 1992/01/01
Hardware / software; Stevin Supercomputer Infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria(s);1h
Submission date; 2017/5/20
ID;2
Author(s); Klein, R. and Scholl, A.
Reference;Klein, R., & Scholl, A. (1999). Computing lower bounds by destructive improvement: An application to resource-constrained project scheduling. European Journal of Operational Research, 112(2), 322-346. https://doi.org/10.1016/S0377-2217(97)00442-6
Date; 1999/01/01
Hardware / software; i7, 16GB, 2GHz, C++, Visual Studio 2015 (x64), Windows 10
Stop criteria;none
Submission date; 2016/01/01
ID;3
Author(s); Debels, D. and Vanhoucke, M.
Reference;Debels, D., & Vanhoucke, M. (2007). A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem. Operations Research, 55(3), 457-469. https://doi.org/10.1287/opre.1060.0358
Date; 2007/01/01
Hardware / software; Stevin Supercomputer Infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria;cumulated results from several runs, on open instances only. Maximal run: 1000 runs x 500k schedules
Submission date; 2017/5/21
ID;4
Author(s);Schutt, A., Feydy, T., Stuckey, P.J., Wallace, M.G.
Reference;Schutt, A., Feydy, T., Stuckey, P. J., & Wallace, M. G. (2011). Explaining the cumulative propagator. Constraints, 16(3), 250-282. https://doi.org/10.1007/s10601-010-9103-2
Date; 2011/07/01
Hardware / software;X86-64 architecture running GNU/Linux and a Intel(R) Xeon(R) CPU E54052 processor with 2 GHz. Code in Mercury using the G12 Constraint Programming Platform
Stop criteria(s);10 min, results reported in the paper as new results at the time of publication
Submission date; 2019/10/28
ID;5
Author(s);Petr Vilim
Reference;Vilim, P. (2011). Timetable Edge Finding Filtering Algorithm for Discrete Cumulative Resources. International Conference on AI and OR Techniques in Constriant Programming for Combinatorial Optimization Problems, 16. https://doi.org/10.1007/978-3-642-21311-3_22
Date;2011
Hardware / software;Intel(R) Core(TM)2 Duo CPU T9400 on 2.53GHz
Stop criteria;time limit for each improvement step is 60 seconds
Submission date;2021
ID;6
Author(s);Haouari, M., Kooli, A., Nron, E., & Carlier, J.
Reference;Haouari, M., Kooli, A., Nron, E., & Carlier, J. (2014). A preemptive bound for the Resource Constrained Project Scheduling Problem. Journal of Scheduling, 17(3), 237248. https://doi.org/10.1007/s10951-013-0354-9
Date;2014
Hardware / software;Pentium IV 3.0 GHz Personal Computer with 1 GB RAM. We used CPLEX 11 for solving the LPs
Stop criteria
Submission date;2021
ID;7
Author(s); Coelho, J. and Vanhoucke, M.
Reference;Coelho, J., & Vanhoucke, M. (2018). An exact composite lower bound strategy for the resource-constrained project scheduling problem. Computers & Operations Research, 93, 135-150. https://doi.org/10.1016/j.cor.2018.01.017
Date; 2017/06/01
Hardware / software; STEVIN HPC-UGent infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria(s);Best results from all runs, with maximal run time of 1h
Submission date; 2017/12/31
ID;8
Author(s);Mario Vanhoucke and Jos Coelho
Reference;Vanhoucke, M., & Coelho, J. (2024). Reducing the feasible solution space of resource-constrained project instances. Computers & Operations Research, 165, 106567. https://doi.org/10.1016/j.cor.2024.106567
Date;to appear
"Hardware / software ";Stevin Supercomputer Infrastructure, C++, compiler intel / 2017.02, Linux
Stop criteria(s); 500000 schedules
Submission date; 2024/01/01
ID;9
Author(s);Vanhoucke, M. and Coelho, J.
Reference;Vanhoucke, M., & Coelho, J. (2024). A matheuristic for the resource-constrained project scheduling problem. European Journal of Operational Research, 319(3), 711725. https://doi.org/10.1016/j.ejor.2024.07.016
Date;01/12/2024
Hardware / software; STEVIN HPC-UGent infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria(s);Best results from all runs
Submission date; 2024/04/08
ID;10
Author(s);Vilm Heinz, Petr Vilm, Zdenk Hanzlek
Reference;Heinz, V., Hanzlek, Z., & Vilim, P. (2025). Reinforcement Learning for Search Tree Size Minimization in Constraint Programming: New Results on Scheduling Benchmarks, Computers & Industrial Engineering 209, 111413. https://doi.org/10.1016/j.cie.2025.111413
Date; 2024/10/10
Hardware / software;Unknown
Stop criteria(s);900 seconds
Submission date; 2024/10/10
ID;11
Author(s); Coelho, Jose and Vanhoucke, Mario
Reference; Coelho, Jose and Vanhoucke, Mario. 2024. Working paper 'Comparing and extending satisfiability solution methods for the resource-constrained project scheduling problem'
Date; 2024/10/20
Hardware / software; STEVIN HPC-UGent infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria(s);Several procedures and stop criteria, the last one with 20 hours.
Submission date; 2024/10/20
ID;12
Author(s);PSPLIB report
Reference;Check PSPLIB website: http://www.om-db.wi.tum.de/psplib
Date; 1999/01/01
Hardware / software;Not reported
Stop criteria;Results reported from several authors, UBs, LBs and optimal runs
Submission date;31/10/2024
ID;1
Author(s); Demeulemeester, E. and Herroelen, W.
Reference;Demeulemeester, E., & Herroelen, W. (1992). A Branch-and-Bound Procedure for the Multiple Resource-Constrained Project Scheduling Problem. Management Science, 38(12), 1803-1818. http://www.jstor.org/stable/2632711
Date; 1992/01/01
Hardware / software; Stevin Supercomputer Infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria(s);1h
Submission date; 2017/5/20
ID;2
Author(s); Klein, R. and Scholl, A.
Reference;Klein, R., & Scholl, A. (1999). Computing lower bounds by destructive improvement: An application to resource-constrained project scheduling. European Journal of Operational Research, 112(2), 322-346. https://doi.org/10.1016/S0377-2217(97)00442-6
Date; 1999/01/01
Hardware / software; i7, 16GB, 2GHz, C++, Visual Studio 2015 (x64), Windows 10
Stop criteria;none
Submission date; 2016/01/01
ID;3
Author(s); Debels, D. and Vanhoucke, M.
Reference;Debels, D., & Vanhoucke, M. (2007). A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem. Operations Research, 55(3), 457-469. https://doi.org/10.1287/opre.1060.0358
Date; 2007/01/01
Hardware / software; Stevin Supercomputer Infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria;cumulated results from several runs, on open instances only. Maximal run: 1000 runs x 500k schedules
Submission date; 2017/5/21
ID;4
Author(s);Schutt, A., Feydy, T., Stuckey, P.J., Wallace, M.G.
Reference;Schutt, A., Feydy, T., Stuckey, P. J., & Wallace, M. G. (2011). Explaining the cumulative propagator. Constraints, 16(3), 250-282. https://doi.org/10.1007/s10601-010-9103-2
Date; 2011/07/01
Hardware / software;X86-64 architecture running GNU/Linux and a Intel(R) Xeon(R) CPU E54052 processor with 2 GHz. Code in Mercury using the G12 Constraint Programming Platform
Stop criteria(s);10 min, results reported in the paper as new results at the time of publication
Submission date; 2019/10/28
ID;5
Author(s);Petr Vilim
Reference;Vilim, P. (2011). Timetable Edge Finding Filtering Algorithm for Discrete Cumulative Resources. International Conference on AI and OR Techniques in Constriant Programming for Combinatorial Optimization Problems, 16. https://doi.org/10.1007/978-3-642-21311-3_22
Date;2011
Hardware / software;Intel(R) Core(TM)2 Duo CPU T9400 on 2.53GHz
Stop criteria;time limit for each improvement step is 60 seconds
Submission date;2021
ID;6
Author(s);Haouari, M., Kooli, A., Nron, E., & Carlier, J.
Reference;Haouari, M., Kooli, A., Nron, E., & Carlier, J. (2014). A preemptive bound for the Resource Constrained Project Scheduling Problem. Journal of Scheduling, 17(3), 237248. https://doi.org/10.1007/s10951-013-0354-9
Date;2014
Hardware / software;Pentium IV 3.0 GHz Personal Computer with 1 GB RAM. We used CPLEX 11 for solving the LPs
Stop criteria
Submission date;2021
ID;7
Author(s); Coelho, J. and Vanhoucke, M.
Reference;Coelho, J., & Vanhoucke, M. (2018). An exact composite lower bound strategy for the resource-constrained project scheduling problem. Computers & Operations Research, 93, 135-150. https://doi.org/10.1016/j.cor.2018.01.017
Date; 2017/06/01
Hardware / software; STEVIN HPC-UGent infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria(s);Best results from all runs, with maximal run time of 1h
Submission date; 2017/12/31
ID;8
Author(s);Mario Vanhoucke and Jos Coelho
Reference;Vanhoucke, M., & Coelho, J. (2024). Reducing the feasible solution space of resource-constrained project instances. Computers & Operations Research, 165, 106567. https://doi.org/10.1016/j.cor.2024.106567
Date;to appear
"Hardware / software ";Stevin Supercomputer Infrastructure, C++, compiler intel / 2017.02, Linux
Stop criteria(s); 500000 schedules
Submission date; 2024/01/01
ID;9
Author(s);Vanhoucke, M. and Coelho, J.
Reference;Vanhoucke, M., & Coelho, J. (2024). A matheuristic for the resource-constrained project scheduling problem. European Journal of Operational Research, 319(3), 711725. https://doi.org/10.1016/j.ejor.2024.07.016
Date;01/12/2024
Hardware / software; STEVIN HPC-UGent infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria(s);Best results from all runs
Submission date; 2024/04/08
ID;10
Author(s);Vilm Heinz, Petr Vilm, Zdenk Hanzlek
Reference;Heinz, V., Hanzlek, Z., & Vilim, P. (2025). Reinforcement Learning for Search Tree Size Minimization in Constraint Programming: New Results on Scheduling Benchmarks, Computers & Industrial Engineering 209, 111413. https://doi.org/10.1016/j.cie.2025.111413
Date; 2024/10/10
Hardware / software;Unknown
Stop criteria(s);900 seconds
Submission date; 2024/10/10
ID;11
Author(s); Coelho, Jose and Vanhoucke, Mario
Reference; Coelho, Jose and Vanhoucke, Mario. 2024. Working paper 'Comparing and extending satisfiability solution methods for the resource-constrained project scheduling problem'
Date; 2024/10/20
Hardware / software; STEVIN HPC-UGent infrastructure, C++, compiler intel/2017.02, Linux
Stop criteria(s);Several procedures and stop criteria, the last one with 20 hours.
Submission date; 2024/10/20
ID;12
Author(s);PSPLIB report
Reference;Check PSPLIB website: http://www.om-db.wi.tum.de/psplib
Date; 1999/01/01
Hardware / software;Not reported
Stop criteria;Results reported from several authors, UBs, LBs and optimal runs
Submission date;31/10/2024
Relevant Results
Best Known Solutions
Original Instances
File Header
Title;J120
Description;Dataset proposed in the paper Kolisch, R., & Sprecher, A. (1997). PSPLIB - A project scheduling problem library. European Journal of Operational Research, 96(1), 205-216. https://doi.org/10.1016/S0377-2217(96)00170-1
Number;600
Format;sm
Description;Dataset proposed in the paper Kolisch, R., & Sprecher, A. (1997). PSPLIB - A project scheduling problem library. European Journal of Operational Research, 96(1), 205-216. https://doi.org/10.1016/S0377-2217(96)00170-1
Number;600
Format;sm
Date