Machine Learning Approaches to Optimising Factory Layouts for Enhanced Operational Efficiency

Factory layout planning (FLP) is one of the most significant decisions that have to be taken by an organization while establishing and designing a manufacturing unit. Traditional approaches to FLP, which rely on manual and computational methods, struggle to cope with the increasing complexity of modern manufacturing systems, characterized by diverse product types, shorter life cycles, and smaller batch sizes. Objective: To address these challenges, this paper seeks to utilized Reinforcement Learning (RL) to facilitate the automation of factory layout optimization by looking at material flow, transportation time, and productivity rates. Provide a case study based on RL for Factory layout. Methods: A novel RL-based framework is developed, where an RL agent dynamically allocates functional units within a grid-based factory layout. The framework is compared with both manual and conventional computer-aided approaches. In addition to RL, machine learning (ML) techniques such as supervised and unsupervised learning are also evaluated for layout optimization. Results: RL has a superior performance to the traditional model; the results depict a massive improvement in transport duration and enhanced workflow. This flexibility is inherent in RL to solve complex FLP without having to rely extensively on analytic models that may not be feasible when the problem space changes often. Conclusion: This research demonstrates that RL is a powerful tool for optimizing factory layouts, capable of dynamically responding to system changes and leading to continuous improvements in operational performance.