Optimasi Penjadwalan Produksi dengan Jaringan Syaraf Tiruan dan Algoritma Genetika - Siregar, Dzilhulaifa; Sofinah Harahap, Lailan; Fadlan Alamsyah, Muhammad (UNIVERSITAS SERAMBI MEKKAH, 2025)
Optimal production scheduling is essential to improve operational efficiency in the manufacturing industry. This study proposes a combination of Neural Networks (NN) and Genetic Algorithms (GA) to solve production scheduling problems. NN is used to predict processing time based on historical data, while GA optimizes the production sequence to minimize idle time and increase throughput. Simulation results show that this combined method provides a more efficient scheduling solution compared to conventional methods.