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

Konstantinos C. Giotopoulos (), Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou and Spyros Sioutas
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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

Future Internet, 2025, vol. 17, issue 3, 1-39

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.

Keywords: workload management; public sector; load control; human resources; productivity; ranking; efficiency; task allocation; personnel selection (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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