Datasets for the Project Portfolio Selection and Scheduling Problem
Description
The primary objective of the Project Portfolio Selection and Scheduling Problem (PPSSP) is to maximise the total portfolio value through the selection and scheduling of a subset of projects subject to various operational constraints. This dataset includes a set of synthetically generated problem instances for two recently-proposed, generalised models of the PPSSP. These problem instances can be used by researchers to compare the performance of heuristic and meta-heuristic solution strategies. 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.
Files
Steps to reproduce
The two datasets (option_instances and project_instances) can be reproduced via the supplied Python program (PPSSP_datagen_master). Python 3.8 or later, along with the numpy and scipy Python packages, are required to execute the supplied source code.