<![CDATA[The garment industry faces complex production scheduling challenges due to high product variability and inaccurate process time estimation. Inefficient production scheduling often leads to production delays and reduced operational performance. Conventional scheduling methods such as First Come First Serve (FCFS) and Earliest Due Date (EDD) have been shown to be less effective in dynamic production environments. This study aims to optimize flow shop production scheduling in a children's garment manufacturing environment using a hybrid Random Forest–Genetic Algorithm approach. Random Forest is employed to predict the processing time of each job based on a simulated dataset regenerated from the company's historical production data collected in 2025 while preserving the statistical characteristics and relationships among variables. Subsequently, the Genetic Algorithm is used to optimize job sequencing by simultaneously minimizing makespan and weighted tardiness. The study follows the CRISP-DM methodology up to the model evaluation stage. The results show that the Random Forest model achieved satisfactory prediction performance for the cutting, sewing, and finishing stages, with R² values of 0.817, 0.981, and 0.867, respectively. Using a population size of 30, 100 generations, and 10 independent runs, the Genetic Algorithm achieved an improvement of 79.94% compared to FCFS and 39.71% compared to EDD. These findings demonstrate that the proposed hybrid Random Forest–Genetic Algorithm approach can generate a more adaptive, efficient, and data-driven production schedule than conventional scheduling methods.]]>
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