A genetic algorithm-based approach for solving the production scheduling problem in unrelated parallel machines
TL;DR
This study aims to minimize the makespan while considering sequence-dependent setup times for both machines and work orders and proposes a genetic algorithm integrating reinforcement learning and variable neighborhood search.
Abstract
The scheduling of unrelated parallel machines (UPM) is a critical aspect of production planning and a major challenge in the manufacturing industry. Scheduling outcomes directly impact a business's profitability and sustainability. In the UPM problem, the processing times for each work order vary across machines and are not proportionally consistent. This becomes a common scheduling challenge for medium- to large-sized enterprises due to its complexity. To address the UPM scheduling problem, this study aims to minimize the makespan while considering sequence-dependent setup times for both machines and work orders. It then proposes a genetic algorithm integrating reinforcement learning and variable neighborhood search. To assess the feasibility and effectiveness of the proposed algorithm, real work orders from a renowned international semiconductor copper foil manufacturer (Company D) are used as test data to assess the proposed method. The results indicate that the proposed method can reduce the makespan by 22.45% compared to other existing methods used by Company D. Additionally, randomly generated test data were used to validate the effectiveness and feasibility of the method.