Fuzzy Logic Control of Automated Materials Handling Systems in Smart Factories
Keywords:
Industry, Materials Handling, Manufacturing, Fuzzy Logic, ControlAbstract
Modern manufacturing systems are becoming more complex and dynamic, so smarter control strategies are needed to improve efficiency. This study examines how fuzzy logic control (FLC) can optimise automated material handling systems (AMHS) in smart factories. Traditional control methods use fixed rules and exact mathematical models, but they often struggle with uncertainty, changing job arrival rates, and shifting system conditions. To address these challenges, a fuzzy logic-based control framework was created to support adaptive, real-time decision-making. The system uses key input variables such as queue length and waiting time to decide which jobs to handle first in the material handling process. A Mamdani-type fuzzy inference system was set up, using membership functions and a rule base to capture expert knowledge and system behaviour. The model was tested with a dataset of 10 jobs processed over 15 minutes, which reflects real manufacturing situations. We evaluated performance by comparing the FLC approach with a conventional control method across key metrics, including task completion time, average waiting time, throughput, and system delay. The simulation results demonstrated that the FLC significantly outperformed the traditional approach. Specifically, task completion time decreased by 15.4%, average waiting time decreased by 33.3%, and throughput increased by 22.7%, indicating improved system productivity. Additionally, system delay was minimized by 33.3%, highlighting the effectiveness of the FLC in managing congestion and enhancing flow efficiency. The results confirm that fuzzy logic control offers a robust and flexible solution for handling uncertainties and nonlinearities in automated material handling systems. By enabling intelligent prioritization and dynamic response to changing conditions, the proposed approach contributes to improved resource utilization and overall system performance. This study underscores the potential of FLC as a key enabler for smart manufacturing and Industry 4.0 applications. Integrating fuzzy logic control into AMHS provides a viable pathway to higher efficiency, adaptability, and reliability in smart factory operations. Future work may explore the integration of other artificial intelligence techniques, such as machine learning and optimization algorithms, to further enhance system performance and scalability.
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