PENGEMBANGAN MODEL OPTIMASI ARTIFICIAL NEURAL NETWORK PADA PENJADWALAN PRODUKSI SNACK TORTILLA

Abstract

Production on May 2019, PT X produced 29,159 kg of Tortillas to be marketed domestically and abroad. The large amount of production shows the high consumer interest in Tortilla, which makes PT X produce large quantities in 1 month. Production of 29,159 kg was completed in 3 weeks with 3 shifts in 7 working days in the first and third week, and 6 working days in the second week. Inaccurate production planning makes Tortilla production exceed warehouse capacity, indicating that the production process is still running even though the number in June is as much as 17,346 kg and 26,835 kg in July resulting in overproduction of 6% in May 2019 and 15% in June 2019 so that there was an increase in July 2019 to 50%. The implementations of the Artificial Neural Network (ANN) method based on Particle Swarm Optimization (PSO) using the Steepest Ascent Hill Climbing Algorithm (SAHC) are optimize the final mean flow time by 51%, reduction in makespan by 0.5 in May-June and 0.1 in July, and a reduction in lateness by 13% after reprocessing results in an optimization that can overcome the problem of overproduction.