# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 2 Related Work and Contributions
[PERSON] and [PERSON] (2019) introduced an ANN model that predicts fuzzy overall equipment effectiveness (OEE) and line performance. The integration of fuzzy logic and ANNs allowed for accurate performance measurement, particularly in the context of manufacturing operations [14]. The construction industry also benefits from ANN-based predictions. [PERSON] et al. (2023) proposed a hybrid model merging ANNs with the grasshopper optimization algorithm to forecast construction labor productivity, considering factors like worker skills, project complexity, and weather conditions. Their approach proved accurate and effective for prediction [15]. Similarly, [PERSON] and [PERSON] (2015) employed ANNs to measure and predict construction labor productivity, emphasizing the consideration of factors such as worker skills, project complexity, and weather conditions. Their study underscored the accuracy and efficacy of ANNs in predicting productivity in the construction domain [16]. In a different domain, [PERSON] et al. (2020) pioneered a three-step neural network artificial intelligence modeling approach for time, productivity, and cost prediction within the Italian forestry sector. Their methodology incorporated a diverse array of factors, including tree species, terrain characteristics, and weather conditions, to predict outcomes accurately [17]. Lastly, [PERSON], [PERSON], and [PERSON] (2014) presented an ANN model for predicting productivity and cycle time in manufacturing processes. Their model factors in various critical elements such as machine type, worker skills, and material characteristics, highlighting the accuracy and efficiency of ANN-based predictions in optimizing manufacturing processes [18]. These works collectively demonstrate the versatile applications of ANNs in forecasting and optimizing productivity across diverse domains. In the domain of performance estimation and analysis across diverse sectors, the integration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) proves to be a powerful tool. [PERSON], [PERSON], and [PERSON] (2021) introduced a hybrid approach utilizing technology readiness level (TRL), data envelopment analysis (DEA), and ANFIS to select and estimate the performance of Green Lean Six Sigma (GLSS) projects. Their hybrid methodology demonstrated effectiveness in accurately determining and evaluating project performance [19]. In the context of labor loss estimation, [PERSON] (2023) compared the performance of fuzzy logic with ANFIS. Their evaluation of labor loss data from a manufacturing company revealed that ANFIS is superior in accuracy and precision [20]. [PERSON] et al. (2023) focused on determining the leadership perceptions of construction employees, utilizing ANFIS. Their study emphasized ANFIS as an effective tool for accurately determining these perceptions, demonstrating its utility in the construction sector [21]. Education quality assessment is another domain in which ANFIS achieves excellence. [PERSON] et al. (2020) propose an ANFIS-inspired smart framework to assess education quality, showcasing its effectiveness in evaluating student performance, teacher quality, and school infrastructure [22], while [PERSON] and [PERSON] (2016) introduced an intelligent framework for productivity assessment and analysis of human resources, considering various factors such as resilience engineering, motivational aspects, health, safety, and ergonomics. Their approach proves to be effective in accurately assessing human resource productivity [23]. Considering organizational cohesion and its impact on employee productivity, [PERSON] et al. (2022) utilized ANFIS to model these relationships. Their study emphasized the significant positive impact of organizational cohesion on employee productivity and highlighted ANFIS as an effective analytical tool [24]. [PERSON], [PERSON], and [PERSON] (2013) proposed a system model for performance management in the public sector, employing a balanced scorecard approach.