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Generating datasets for the project portfolio selection and scheduling problem.

Kyle Robert Harrison1, Saber M Elsayed1, Ivan L Garanovich2

  • 1School of Engineering and Information Technology, University of New South Wales, Canberra ACT 2600, Australia.

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Summary
This summary is machine-generated.

This study introduces two project portfolio selection and scheduling problem (PPSSP) models and synthetic datasets. These resources aid researchers in comparing heuristic and meta-heuristic solution strategies for maximizing portfolio value under constraints.

Keywords:
Benchmark problemsPortfolio optimisationProject portfolio selectionProject scheduling

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Area of Science:

  • Operations Research
  • Management Science
  • Computer Science

Background:

  • The project portfolio selection and scheduling problem (PPSSP) is critical for maximizing organizational value.
  • Existing models may not fully capture the complexities of real-world project selection and scheduling.
  • There is a need for standardized datasets to evaluate solution methodologies.

Purpose of the Study:

  • To present two generalized models for the project portfolio selection and scheduling problem (PPSSP).
  • To introduce a suite of synthetically generated problem instances for these PPSSP models.
  • To facilitate the comparison of heuristic and meta-heuristic solution strategies.

Main Methods:

  • Description of two generalized mathematical models for PPSSP.
  • Generation of synthetic datasets using a provided Python program.
  • Focus on operational constraints and maximizing total portfolio value.

Main Results:

  • Two distinct variants of the PPSSP are detailed.
  • A comprehensive set of synthetic problem instances is proposed for each variant.
  • A Python program for instance generation is made available to the research community.

Conclusions:

  • The proposed models and datasets offer a valuable resource for PPSSP research.
  • Researchers can utilize these instances to benchmark and advance heuristic and meta-heuristic algorithms.
  • The open-source nature of the generation tool promotes reproducible research and further development.