Classification of Worker Productivity and Resource Allocation Optimization with Machine Learning: Garment Industry
DOI:
https://doi.org/10.52436/1.jutif.2025.6.5.5263Keywords:
Bayesian Optimization, Garment Industry, Linear Programming, Machine Learning, Productivity Classification, Random ForestAbstract
This study presents an integrated predictive–prescriptive framework for improving workforce management in the garment industry by combining machine learning classification with linear programming optimization. Using a publicly available dataset of 1,197 production records, productivity levels were categorized into low, medium, and high classes. Data preprocessing included handling missing values, one-hot encoding of categorical variables, and class balancing using SMOTE. Eleven classification algorithms were evaluated, with LightGBM achieving the highest performance (accuracy 78.3%, weighted F1-score 78.3%, Cohen’s Kappa 63.4%) after hyperparameter tuning via Bayesian Optimization. The optimized model’s predictions were then incorporated into a linear programming model, implemented with PuLP, to maximize the allocation of high-productivity workers across production departments under capacity constraints. The results yielded an allocation plan assigning 117 high-productivity workers, significantly enhancing potential operational efficiency. The novelty of this work lies in integrating an optimized ensemble learning model with mathematical programming for end-to-end productivity classification and resource allocation, a combination rarely explored in labor-intensive manufacturing contexts. This framework offers a scalable decision-support tool for data-driven workforce planning and could be adapted to other manufacturing domains with similar operational structures.
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Copyright (c) 2025 A’isya Nur Aulia Yusuf, Zakiyyan Zain Alkaf, Elsa Sari Hayunah Nurdiniyah, Tri Wisudawati, Muhammad Ihsan Fawzi
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