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Matrix-based project dataset parsers.

Zsolt T Kosztyán1, Gergely L Novák2

  • 1Department of Quantitative Methods, University of Pannonia, Egyetem str. 10, Veszprém, H-8200, Hungary.

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|August 19, 2024
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Summary

This study introduces a novel parsing method to unify diverse project datasets. This enables comprehensive testing and comparison of scheduling and resource allocation algorithms across various project types.

Keywords:
Matrix-based projectsProject database parserProject parserStructural flexibility

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Area of Science:

  • Project Management
  • Data Science
  • Algorithm Analysis

Background:

  • Existing project datasets are fragmented, with heterogeneous file structures for simulated/real, individual/multiprojects, and single/multimodal attributes.
  • This fragmentation limits algorithm testing to single datasets, hindering robust evaluation of scheduling and resource allocation methods.
  • Diverse internal project structures make it challenging to ensure algorithm generalizability.

Purpose of the Study:

  • To develop a unified parsing method for diverse project datasets.
  • To enable comprehensive testing and comparison of scheduling and resource allocation algorithms.
  • To support the modeling of structural flexibility in agile, hybrid, and extreme project management.

Main Methods:

  • A novel parsing method designed to read multiple project types (simulated, real, individual, multiprojects, single/multimodal attributes).
  • Incorporation of activity priorities and flexible dependencies to model structural adaptability.
  • Development of a framework for building a large, integrated project database.

Main Results:

  • The parsing method successfully integrates heterogeneous project data sources.
  • Researchers can now utilize a unified database for testing algorithms across a wider range of project structures.
  • The method facilitates the analysis of algorithms considering activity priorities and dependency flexibility.

Conclusions:

  • The proposed parsing method enhances the ability to test and compare scheduling and resource allocation algorithms.
  • It provides a foundation for developing more robust and generalizable project management algorithms.
  • This approach supports advanced project management methodologies by accommodating structural flexibility.