Robust Daily Operator Assignment in Garment Sewing Lines Using a Multi-Objective Optimization Framework with Skill-Based Feasibility and Stochastic Absenteeism Modeling

Garment sewing line operator assignment decisions are made daily based on supervisor intuition. Frequently, such decisions create skill operation mismatches that lead to longer bottleneck cycle times and increased stochastic absenteeism rates of 5–20%. This study presents a multi-objective mixed-integer linear programming (MOMILP) framework to simultaneously minimize bottleneck cycle time and reassignment intensity while considering the operator specific skills feasibility and actual probabilities of absenteeism. The proposed framework makes optimal allocation for the current operator within the skill limitations by applying a set of constraints to generate Pareto efficient plans that balance throughput and operational stability. Validation of 90 days period across three garment styles indicates that the optimal assignment has achieved a mean throughput of 55.84 pieces/hour, a 50.6% improvement over the 37.08 pieces/hour baseline (range: 28.5–92.4%) with bottleneck proxies accounting for 62% of the output variation. Robustness analysis reveals that the worst-case throughput is 96.4% while bottleneck and training simulations highlight cross-skilling priorities to enhance long-term resilience. This framework provides a deployable solution that combines skill-based allocation, multi-objective optimization, and uncertainty modeling. It allows labor intensive garment factories a practical, more data-driven, tool to continue to sustain productivity improvement without investing more capital.