Datasets for the Project Portfolio Selection and Scheduling Problem

In addition, the Python program used to generate the problem instances is supplied, allowing researchers to generate new problem instances. The first model, extended from [1], is a project-oriented model such that projects are independently selected and scheduled, subject to various operational constraints such as prerequisites and mutual exclusion groups. In the second model, originally proposed in [2], the projects are grouped into predetermined sets, referred to as options.

Selection is made on the options, such that all projects within an option must be implemented if the option is selected. However, projects can be independently scheduled, within a fixed window. Furthermore, a project may be present in any number of options, but its cost is only counted once if it is selected as part of multiple options.

Instances in the project_instances folder are divided into three sub-folders (250, 500, 1000) based on the number of projects contained in the problem. Within each sub-folder, instances are named "PI_{index}_{number of projects}_{number of planning years}_{proportion of budget for initiating projects}_{proportion of budget for maintaining ongoing projects}_{time discount rate}.json". Instances in the option_instances folder are divided into four sub-folders (25, 50, 100, 250) based on the number of families contained in the problem.

Within each sub-folder, instances are named "OI_{number of families}_{number of projects}_{proportion of divestment projects}_{yearly budget deviation}_{number of planning years}.json". [1] K. R. Harrison, S. Elsayed, I. L. Garanovich, T. Weir, M. Galister, S. Boswell, R. Taylor, R. Sarker, A hybrid multi-population approach to the project portfolio selection and scheduling problem for future force design, IEEE Access 9 (2021) 83410–83430.

[2] K. R. Harrison, S. M. Elsayed, I. L. Garanovich, T. Weir, S. G. Boswell, R. A. Sarker, A new model for the project portfolio selection and scheduling problem with defence capability options, in: K. R. Harrison, S. M. Elsayed, I. L. Garanovich, T. Weir, S. G. Boswell, R. A. Sarker (Eds.), Evolutionary and Memetic Computing for

Project

Portfolio Selection and Scheduling, Springer International Publishing, 2022, pp. 89–123. doi:10.1007/978-3-030-88315-7_5.