Optimizing worker-task allocation in a fish processing factory using a predict-then-optimize approach
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Full Text |
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Author |
Abul Mukid Mohammad Mukaddes, Qawsar Ali, Md. Khairul Basar and Zobayer Hasan
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e-ISSN |
1819-6608 |
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On Pages
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60-69
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Volume No. |
21
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Issue No. |
2
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Issue Date |
March 20, 2026
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DOI |
https://doi.org/10.59018/012617
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Keywords |
predict-then-optimize, machine learning, task time prediction, workforce allocation, optimization.
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Abstract
Most industries in developing countries are highly dependent on tasks that involve human effort. Allocation of manual tasks to workers is thus a very important factor for reducing lead time in labor-dependent industries. The goal of this research lies in improving worker-task allocation in labor-dependent industries using a two-stage Predict-then-Optimize framework. In this study, machine learning was used to predict task completion time for each worker-task pair, and the results gained from the prediction model were then optimized through mixed-integer programming. Task-related data were collected multiple times at different hours of the day from 54 worker samples across 17 distinct tasks, making the dataset size 400. Random Forest, SVM, XGBoost, and ANN were used for prediction, and the optimization problem was formulated using Mixed Integer Programming and solved with the Gurobi optimizer. Random Forest Regressor (RFR) demonstrated the best performance with the lowest MAPE (6.63%) and the highest R2 score (0.98) for the prediction of task completion times based on demographic features and experience. Using these predictions, task allocation was optimized, cutting total process time by 67.5%-from over 50 minutes to just 16.3 minutes. So, this integrated Predict-then-Optimize framework approach can improve the industrial environment in labor-intensive industries by assisting top-level management in decision-making about reducing the total task completion time.
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