IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v17y2025i3p119-d1606910.html
   My bibliography  Save this article

Dynamic Workload Management System in the Public Sector: A Comparative Analysis

Author

Listed:
  • Konstantinos C. Giotopoulos

    (Department of Management Science and Technology, University of Patras, 26504 Patras, Greece)

  • Dimitrios Michalopoulos

    (Department of Management Science and Technology, University of Patras, 26504 Patras, Greece)

  • Gerasimos Vonitsanos

    (Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece)

  • Dimitris Papadopoulos

    (Department of Management Science and Technology, University of Patras, 26504 Patras, Greece)

  • Ioanna Giannoukou

    (Department of Management Science and Technology, University of Patras, 26504 Patras, Greece)

  • Spyros Sioutas

    (Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece)

Abstract

Efficient human resource management is critical to public sector performance, particularly in dynamic environments where traditional systems struggle to adapt to fluctuating workloads. The increasing complexity of public sector operations and the need for equitable task allocation highlight the limitations of conventional evaluation methods, which often fail to account for variations in employee performance and workload demands. This study addresses these challenges by optimizing load distribution through predicting employee capability using data-driven approaches, ensuring efficient resource utilization and enhanced productivity. Using a dataset encompassing public/private sector experience, educational history, and age, we evaluate the effectiveness of seven machine learning algorithms: Linear Regression, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Bagged Decision Trees, and XGBoost in predicting employee capability and optimizing task allocation. Performance is assessed through ten evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), ensuring a comprehensive assessment of accuracy, robustness, and bias. The results demonstrate ANFIS as the superior model, consistently outperforming other algorithms across all metrics. By synergizing fuzzy logic’s capacity to model uncertainty with neural networks’ adaptive learning, ANFIS effectively captures non-linear relationships and variations in employee performance, enabling precise capability predictions in dynamic environments. This research highlights the transformative potential of machine learning in public sector workforce management, underscoring the role of data-driven decision-making in improving task allocation, operational efficiency, and resource utilization.

Suggested Citation

  • Konstantinos C. Giotopoulos & Dimitrios Michalopoulos & Gerasimos Vonitsanos & Dimitris Papadopoulos & Ioanna Giannoukou & Spyros Sioutas, 2025. "Dynamic Workload Management System in the Public Sector: A Comparative Analysis," Future Internet, MDPI, vol. 17(3), pages 1-39, March.
  • Handle: RePEc:gam:jftint:v:17:y:2025:i:3:p:119-:d:1606910
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/17/3/119/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/17/3/119/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    2. Yu-Shan Chen & Ke-Chiun Chang, 2010. "Analyzing the nonlinear effects of firm size, profitability, and employee productivity on patent citations of the US pharmaceutical companies by using artificial neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 75-82, January.
    3. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    4. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    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. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    2. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    3. Doruk Cengiz & Arindrajit Dube & Attila S. Lindner & David Zentler-Munro, 2021. "Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," NBER Working Papers 28399, National Bureau of Economic Research, Inc.
    4. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    5. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    6. Thitiphat Klinsuwan & Wachiraphong Ratiphaphongthon & Rabian Wangkeeree & Rattanaporn Wangkeeree & Chatchai Sirisamphanwong, 2023. "Evaluation of Machine Learning Algorithms for Supervised Anomaly Detection and Comparison between Static and Dynamic Thresholds in Photovoltaic Systems," Energies, MDPI, vol. 16(4), pages 1-22, February.
    7. Daan Kolkman & Arjen van Witteloostuijn, 2019. "Data Science in Strategy: Machine learning and text analysis in the study of firm growth," Tinbergen Institute Discussion Papers 19-066/VI, Tinbergen Institute.
    8. Maria-Carmen García-Centeno & Román Mínguez-Salido & Raúl del Pozo-Rubio, 2021. "The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach," Mathematics, MDPI, vol. 9(11), pages 1-20, May.
    9. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    10. Garvit Arora & Shubhangi Tiwari & Ying Wu & Xuan Mei, 2024. "An Exploration to the Correlation Structure and Clustering of Macroeconomic Variables," Papers 2401.10162, arXiv.org, revised May 2024.
    11. Konrad Bogner & Florian Pappenberger & Massimiliano Zappa, 2019. "Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts," Sustainability, MDPI, vol. 11(12), pages 1-22, June.
    12. Huei-Wen Teng & Yu-Hsien Li, 2023. "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, vol. 5(1), pages 149-182, March.
    13. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    14. Keddouda, Abdelhak & Ihaddadene, Razika & Boukhari, Ali & Atia, Abdelmalek & Arıcı, Müslüm & Lebbihiat, Nacer & Ihaddadene, Nabila, 2024. "Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation," Applied Energy, Elsevier, vol. 363(C).
    15. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    16. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
    17. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    18. Hou-Tai Chang & Ping-Huai Wang & Wei-Fang Chen & Chen-Ju Lin, 2022. "Risk Assessment of Early Lung Cancer with LDCT and Health Examinations," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    19. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    20. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.

    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:gam:jftint:v:17:y:2025:i:3:p:119-:d:1606910. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.