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Decision Support Framework for Workforce Allocation in Manufacturing Using Machine Learning
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AI
DOI：10.1142/S021968672750048X.png)
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Abstract
En 中文
Efficient workforce allocation is essential for productivity and cost control, especially in small-batch, repetitive manufacturing environments. Traditional scheduling relies on static rules and managerial intuition, causing inefficiencies, downtime, and suboptimal task assignments. A data-driven approach is needed to adapt to real-time variations in operator skills, availability, and production demand. This research develops a machine learning-based framework that predicts operator-task performance, identifies key productivity factors, and generates dynamic, interpretable task assignment recommendations, improving efficiency, reducing labor waste, and supporting informed decision-making. The methodology collects operator IDs, skills, task types, shift schedules, performance metrics, and production constraints. Data cleaning, encoding, normalization, and feature engineering capture operator-task interactions and temporal effects. Predictive modelling uses Gradient Boosting Machine, regression, and time-series analysis to forecast performance and workforce demand. K-Nearest Neighbors and rule-based logic generate real-time task assignments, while Random Forest identifies critical factors and simulates allocation scenarios. Continuous learning updates models to adapt to evolving conditions, ensuring interpretable and efficient workforce management. Findings demonstrate substantial improvements: predicted operator productivity ranges from 0.70 to 0.80, task completion averages 23-32 min, and task allocation efficiency reaches 94-95%. GBM achieves 0.87 accuracy, RMSE 0.08, R2 0.81, and F1-score 0.84, while the hybrid DSS improves accuracy to 0.88, reduces downtime by 48%, and increases productivity by 12%. Future scope includes reinforcement learning, real-time IoT integration, and adaptive multi-objective optimization to further enhance scheduling, predictive accuracy, and operational efficiency in complex manufacturing environments.
Keywords:
Machine learning
workforce allocation
decision support system
predictive modeling
manufacturing optimization
productivity analysis
Journal
J
IF:
1.1
Papers:
59
Citations:
0