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A Regularized Mixed Integer Linear Programming framework with penalties for integrated workforce, subcontracting and production optimization
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DOI：10.3934/jimo.2026074.png)
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Abstract
En 中文
In this study, we introduce a Regularized Mixed-Integer Linear Programming (MILP) framework aimed at optimizing workforce allocation, production scheduling, subcontracting, and energy management within the footwear manufacturing domain. The proposed model incorporates two regularization-based penalty components: An L1 (lasso-type) penalty, which constrains abrupt fluctuations in workforce levels and job sequencing across planning horizons, and an L2 (ridge-type) penalty, which penalizes deviations from target capacity and energy utilization levels. This hybrid formulation improves model robustness, mitigates overfitting, and ensures a balanced trade-off between operational efficiency and cost performance. Empirical evaluation conducted over a six-period planning horizon using representative production data demonstrated the model's effectiveness. With regularization coefficients calibrated at lambda 1 = 0.1 and lambda 2 = 0.05, the MILP solver produced an optimal integer solution, yielding a total operational cost of Indian Rupee738,792.7 while sustaining workforce stability across all periods and completely avoiding additional hiring or layoffs.
Keywords:
flow shop scheduling
batch process
regularization Technique
mixed integer programming
subcontract
production optimization
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1.6
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145
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2.0K
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