A Regularized Mixed Integer Linear Programming framework with penalties for integrated workforce, subcontracting and production optimization
P. K. Sudhakar et al.
What the paper says
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 λ1 = 0.1 and λ2 = 0.05, the MILP solver produced an optimal integer solution, yielding a total operational cost of ₹738,792.7 while sustaining workforce stability across all periods and completely avoiding additional hiring or layoffs.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.