IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v6y2025i3d10.1007_s43069-025-00488-z.html
   My bibliography  Save this article

Artificial Neural Network Approach with Multi-Back Propagation and Training–Testing to Solve the Resource Levelling Problem in PERT-CPM

Author

Listed:
  • Inma C Conde

    (Department of Economics, Universidad de Sevilla)

  • Luis Antonio Palma Martos

    (Department of Economics, Universidad de Sevilla)

  • María del Rocío Martínez Torres

    (Universidad de Sevilla)

  • Francisco Luis Cumbrera Hernández

    (Universidad de Sevilla)

Abstract

Nowadays, due to an improvement in technology and increased competition, project managers have the mandatory task of completing the projects in a foresighted time and with the specified resources. In order to finish the projects without delay, the classical PERT-CPM methodology is being extensively used. In this scenario, two critical problems emerge with no analytical solution: Resource Levelling and Resource Allocation. This paper focuses on the first problem from the novel perspective of Artificial Neural Networks within classic Training–Testing protocol. In order to obtain a reliable dataset for the training process two other targets have been the subject of our research: firstly, the goal was to calibrate quantitatively the efficiency of previous heuristic and metaheuristic approaches to, secondly, train our network with the best results. However, to perform that calibration, research was also conducted to find the optimal efficiency descriptor, beyond the classical Sum of Squares. Using the Principal Component Analysis, an optimal mixed descriptor was found, built from the RIC and Sum of Squares performance descriptors. The research obtained an accuracy of 96,2% regarding the predictions made with a trained neural network. Even if the preliminary task for the Training–Testing protocol is demanding, the reward obtained consists of a quick and comfortable resolution for all the subsequent projects.

Suggested Citation

  • Inma C Conde & Luis Antonio Palma Martos & María del Rocío Martínez Torres & Francisco Luis Cumbrera Hernández, 2025. "Artificial Neural Network Approach with Multi-Back Propagation and Training–Testing to Solve the Resource Levelling Problem in PERT-CPM," SN Operations Research Forum, Springer, vol. 6(3), pages 1-20, September.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:3:d:10.1007_s43069-025-00488-z
    DOI: 10.1007/s43069-025-00488-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-025-00488-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43069-025-00488-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Wilson, James M., 2003. "Gantt charts: A centenary appreciation," European Journal of Operational Research, Elsevier, vol. 149(2), pages 430-437, September.
    2. Bandelloni, M. & Tucci, M. & Rinaldi, R., 1994. "Optimal resource leveling using non-serial dyanamic programming," European Journal of Operational Research, Elsevier, vol. 78(2), pages 162-177, October.
    3. Rieck, Julia & Zimmermann, Jürgen & Gather, Thorsten, 2012. "Mixed-integer linear programming for resource leveling problems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 27-37.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hongbo Li & Linwen Zheng & Hanyu Zhu, 2023. "Resource leveling in projects with flexible structures," Annals of Operations Research, Springer, vol. 321(1), pages 311-342, February.
    2. Cédric Verbeeck & Vincent Peteghem & Mario Vanhoucke & Pieter Vansteenwegen & El-Houssaine Aghezzaf, 2017. "A metaheuristic solution approach for the time-constrained project scheduling problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 353-371, March.
    3. Qingyou Yan & Qian Zhang & Xin Zou, 2016. "A Cost Optimization Model for Multiresource Leveling Problem without Project Duration Constraint," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-8, July.
    4. Simon Emde & Hamid Abedinnia & Anne Lange & Christoph H. Glock, 2020. "Scheduling personnel for the build-up of unit load devices at an air cargo terminal with limited space," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(2), pages 397-426, June.
    5. Hongbo Li & Erik Demeulemeester, 2016. "A genetic algorithm for the robust resource leveling problem," Journal of Scheduling, Springer, vol. 19(1), pages 43-60, February.
    6. Gahm, Christian & Dünnwald, Bastian & Sahamie, Ramin, 2014. "A multi-criteria master production scheduling approach for special purpose machinery," International Journal of Production Economics, Elsevier, vol. 149(C), pages 89-101.
    7. Corrado Battisti, 2018. "Preparing students for the operational environmental career: an integrated project-based road map for academic programs," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 8(4), pages 573-583, December.
    8. Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo & Newman, Alexandra, 2024. "A target-time-windows technique for project scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 314(2), pages 792-806.
    9. Kreter, Stefan & Schutt, Andreas & Stuckey, Peter J. & Zimmermann, Jürgen, 2018. "Mixed-integer linear programming and constraint programming formulations for solving resource availability cost problems," European Journal of Operational Research, Elsevier, vol. 266(2), pages 472-486.
    10. Marta Gomes & Luís Cavique & Isabel Themido, 2006. "The crew timetabling problem: An extension of the crew scheduling problem," Annals of Operations Research, Springer, vol. 144(1), pages 111-132, April.
    11. Pascale Bendotti & Luca Brunod Indrigo & Philippe Chrétienne & Bruno Escoffier, 2024. "Resource leveling: complexity of a unit execution time two-processor scheduling variant and related problems," Journal of Scheduling, Springer, vol. 27(6), pages 587-606, December.
    12. Hübner, Felix & Schellenbaum, Uli & Stürck, Christian & Gerhards, Patrick & Schultmann, Frank, 2017. "Evaluation von Schedulingproblemen für die Projektplanung von Großprojekten am Beispiel des kerntechnischen Rückbaus," Working Paper Series in Production and Energy 22, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    13. Mingers, John & White, Leroy, 2010. "A review of the recent contribution of systems thinking to operational research and management science," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1147-1161, December.
    14. Ilia Tarasov & Alain Haït & Alexander Lazarev & Olga Battaïa, 2024. "Metric estimation approach for managing uncertainty in resource leveling problem," Annals of Operations Research, Springer, vol. 338(1), pages 645-673, July.
    15. Andrea Cardoni & Evgeniia Kiseleva & Francesco De Luca, 2020. "Continuous auditing and data mining for strategic risk control and anticorruption: Creating “fair” value in the digital age," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3072-3085, December.
    16. de Mesnard, Louis, 2017. "Attributing credit to coauthors in academic publishing: The 1/n rule, parallelization, and team bonuses," European Journal of Operational Research, Elsevier, vol. 260(2), pages 778-788.
    17. Lena Sophie Wohlert & Jürgen Zimmermann, 2024. "Resource overload problems with tardiness penalty: structural properties and solution approaches," Annals of Operations Research, Springer, vol. 338(1), pages 151-172, July.
    18. Neumann, K. & Zimmermann, J., 1999. "Resource levelling for projects with schedule-dependent time windows," European Journal of Operational Research, Elsevier, vol. 117(3), pages 591-605, September.
    19. Carvalho, Andréa Nunes & Oliveira, Fabricio & Scavarda, Luiz Felipe, 2015. "Tactical capacity planning in a real-world ETO industry case: An action research," International Journal of Production Economics, Elsevier, vol. 167(C), pages 187-203.
    20. Itai Lishner & Avraham Shtub, 2022. "Using an Artificial Neural Network for Improving the Prediction of Project Duration," Mathematics, MDPI, vol. 10(22), pages 1-16, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snopef:v:6:y:2025:i:3:d:10.1007_s43069-025-00488-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.