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Fine-Tuning Large-Scale Project Scheduling

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Information Systems (EMCIS 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 501))

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Abstract

This paper explores the integration of artificial intelligence (AI) into project management, proposing a decision support system that optimizes project timelines and resources. The pilot study focuses on the Port of Agios Konstantinos in Greece. The methodology section introduces dual annealing as a stochastic optimization method and explains the use of a customizable cost function with overlap calculation to prioritize project aspects. An objective function is defined to maximize task alignment with optimal scheduling periods. The experimental results section presents three optimization cases, adjusting schedules for critical tasks in the pilot project based on different weightings of budget and weather considerations.

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Author information

Authors and Affiliations

  1. Department of Economics, Democritus University of Thrace, Komotini, Greece

    George Sklias & Dimitrios Dimitriou

  2. Department of Electrical and Computer Engineering, Democritus University of Thrace, Komotini, Greece

    Socratis Gkelios

  3. Intelligent Systems Laboratory, Neapolis University Pafos, Paphos, Cyprus

    George Sklias & Socratis Gkelios

Authors
  1. George Sklias
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  2. Socratis Gkelios
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  3. Dimitrios Dimitriou
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Corresponding author

Correspondence to George Sklias .

Editor information

Editors and Affiliations

  1. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

  2. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  3. British University in Dubai, Dubai, United Arab Emirates

    Khalid Al Marri

  4. Dubai Blockchain Center, Dubai, United Arab Emirates

    Marwan Al Zarouni

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Sklias, G., Gkelios, S., Dimitriou, D. (2024). Fine-Tuning Large-Scale Project Scheduling. In: Papadaki, M., Themistocleous, M., Al Marri, K., Al Zarouni, M. (eds) Information Systems. EMCIS 2023. Lecture Notes in Business Information Processing, vol 501. Springer, Cham. https://doi.org/10.1007/978-3-031-56478-9_20

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  • DOI: https://doi.org/10.1007/978-3-031-56478-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56477-2

  • Online ISBN: 978-3-031-56478-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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