AI-Driven Metaheuristic Optimization for Sustainable and Adaptive Facility Layouts in Smart Manufacturing

This paper proposes an adaptive facility layout framework leveraging AI-driven metaheuristic algorithms to optimize energy-efficient and sustainable manufacturing layouts. The study integrates big data analytics, machine learning, and heuristic optimization techniques to minimize operational costs while ensuring environmental and social sustainability. A hybrid Firefly Algorithm and Chaotic Simulated Annealing approach is developed to generate optimized layout solutions under stochastic demand conditions. The proposed model is validated using real-world manufacturing scenarios, demonstrating its capability to enhance productivity, reduce carbon footprints, and maintain high safety standards. The results show significant improvements in layout efficiency, adaptability, and sustainability, making the framework suitable for modern smart manufacturing facilities.