Using Genetic Algorithm and Simulation for Parallel Machine Scheduling in Plastic Packaging Manufacturing

This research focuses on optimizing production scheduling for parallel machines by employing a combination of simulation techniques and genetic algorithms. The primary goal is to efficiently allocate tasks to the machines, maximize raw material utilization, meet customer delivery deadlines, and minimize overall production time. Through a comprehensive study and data collection of planning and production sequencing in a plastic packaging factory, a significant issue arose in task allocation to machines. This is because there is no systematic study of production sequencing. Currently, production sequencing relies on the experience of planners in the production planning department, and existing production scheduling tools lack the ability to validate the optimal sequencing for achieving the best results. The researchers employed simulation techniques in conjunction with genetic algorithms to identify the optimal production sequence for the machines, thus maximizing the overall efficiency of the production system's operating time. The experiment's results show that employing simulation techniques and genetic methods for production sequencing significantly reduces the total running time of the production system. Specifically, it lowered the total running time from the current 251,190.90 seconds to 137,060.10 seconds, resulting in a reduction of 114,130.80 seconds, which accounts for 45.44 percent of the total working time.