Integrated Scheduling Model for Multi-Product Small-Batch Clothing Production Line

The clothing industry is rapidly transitioning from traditional mass production to multi-product, small-batch manufacturing systems, addressing increasingly diverse consumer needs. This study proposes an integrated scheduling optimization model that spans the entire apparel production process, from material inbound to packing, reflecting the complexity of modern ODM (Original Design Manufacturing) and OBM (Own Brand Manufacturing) environments. Leveraging real production data and FlexSim simulation, the model incorporates productivity dynamics through learning curves and applies Mixed-Integer Linear Programming (MILP) and Greedy algorithms for scheduling optimization. Simulation and real-case analyses demonstrate that dynamic allocation and process balancing enhance line utilization, decrease makespan, and increase production efficiency. These results suggest the proposed model offers practical and effective strategies for scheduling optimization and digital transformation in high-mix, low-volume apparel manufacturing settings.