Welcome to the OR&S project database
On this page, you will find different databases for project portfolio scheduling, risk analysis and project control.
- Artificial project data: 11 datasets containing +30,000 instances (and the option to create up to almost 4 million project instances)
- Empirical project data: 1 dataset with real projects with +150 project instances
- Project portfolio data: 2 datasets with almost 40,000 project portfolio instances
- Solutions Update: A new website where you can download and upload best known solutions
- Bookstore: Get to know how to use data for managing projects
- Quick links: If you don't want to read everything on this website, go to the bottom of this page and click on the quick links.
1. Artificial project data
OR&S has generated several datasets with several project instances for two well-known resource-constrained project scheduling problems.
The resource-constrained project scheduling problem (RCPSP)
- Download our 7 datasets (RCPLIB.zip):
- A summary of five datasets (RG30, RG300, DC1, DC2 and MT) is given in Vanhoucke et al. (2016).
- A new dataset CV with 623 hard instances is proposed in Coelho and Vanhoucke (2020).
- The 1kNetRes is a subset of NetRes and contains 3,810 instances. The full NetRes dataset can be generated using the MT set (only network data) and the NetRes data files (only resource data) containing almost 4 million projects as proposed in Vanhoucke and Coelho (2018).
- Visit RCPSP webpage for details.
The multi-mode resource-constrained project scheduling problem (MMRCPSP)
- Download the MMLIB50, MMLIB100 and MMLIB+ datasets (MMLIB.zip)
- Visit our MMRCPSP webpage for details
- Read Van Peteghem and Vanhoucke (2014).
The resource-constrained project scheduling problem with alternative subgraphs (RCPSP-AS)
- Download the ASLIB dataset (ASLIB.zip COMING SOON)
- Visit our RCPSP-AS webpage for details
- Read Servranckx and Vanhoucke (2019).
The multi-skilled resource-constrained project scheduling problem (MSRCPSP)
- Download the MSLIB dataset (MSLIB.zip)
- Visit our MSRCPSP webpage for details
- Read Snauwaert and Vanhoucke (2021) (under submission).
2. Empirical project data
OR&S has also created a project dataset with project instances from companies, initially consisting of 52 projects, but now grown to +200 real projects. This dataset contains project networks with resources for scheduling the project (RCPSP) but also risk data and project control data. The integration of project scheduling, schedule risk analysis and project control is called "Dynamic Scheduling" (DS, Vanhoucke (2013)).
- Download the DSLIB dataset (DSLIB.zip COMING SOON)
- Visit our empirical project data webpage for details.
- Read Batselier and Vanhoucke (2015) (this paper presents the first 52 projects)
3. Project portfolio data
The resource-constrained multi-project project scheduling problem (RCMPSP)
- Download the MPLIB1 and MPLIB2 datasets (MPLIB.zip).
- Visit our multi-project webpage for details.
- Read the papers by Van Eynde and Vanhoucke (2020) and Van Eynde and Vanhoucke (2021).
4. Solutions Update (download data and upload solutions)
We have developed a new tool "SolutionsUpdate" to download and upload solutions for the resource-constrained project scheduling problem using all datasets described above. The system is explained in Vanhoucke and Coelho (2018) and the new tool and website can be accessed on http://solutionsupdate.ugent.be.
Visit our bookstore and get to know how to manage projects with data. The bookstore contains 3 free books and 5 published books:
- The data-driven project manager: A Statistical Battle Against Project Obstacles (2018, Apress)
- Integrated project management sourcebook: A technical guide (2016, Springer)
- Integrated project management and control: First comes the theory, then the practice (2014, Springer)
- Project management with dynamic scheduling (2013, Springer)
- Measuring time: Improving project performance using earned value management (2010, Springer)
6. Quick links
Under construction (coming soon): Link to "download summary flyer" and "Download all data"