Optimization of Clothing System Production Scheduling Based on Fusion Genetic Algorithm

In the context of the trend of personalized and small-batch production in the clothing manufacturing industry, the traditional production scheduling method, which relies on manual experience and simple optimization rules, is unable to cope with the large-scale, multi-style and rapidly changing market demand, resulting in problems such as unsmooth process coordination, unstable quality control and low production efficiency caused by bottleneck processes. This paper adopts the method of integrating genetic algorithm (GA) and ant colony optimization (ACO). By modeling the key production scheduling links and adopting the chromosome representation based on integer coding, GA is used to generate efficient scheduling plans, and combined with ACO to optimize local search, the screening of excellent scheduling plans is enhanced through the pheromone mechanism. The research results show that the method integrating GA and ACO minimizes the production cycle time, achieves an average machine utilization of 88.3% and a labor utilization of 86.5% in resource utilization, maintains a high product qualification rate of 95.2% to 98.9% in quality control, and shows excellent performance in process coordination fluency. Therefore, the hybrid optimization framework that integrates genetic algorithm and ant colony algorithm can effectively improve the efficiency and adaptability of clothing production scheduling.