A Smart Optimization and Decision Support System for Manufacturing Facility Layout Planning Using Machine Learning and AI-Based Modeling
Keywords:
Facility layout planning, Artificial intelligence, Machine learning, Optimization systemAbstract
Manufacturing facility layout planning is a critical determinant of operational efficiency, cost optimization, and production sustainability in modern industrial systems. Traditional layout planning approaches rely heavily on heuristic methods and static optimization models, which often fail to capture dynamic production constraints and multi-objective trade-offs in complex manufacturing environments. In response to these limitations, this study proposes a smart optimization and decision support system (SODSS) that integrates machine learning (ML) and artificial intelligence (AI)-based modeling techniques for enhanced facility layout design and decision-making.
The proposed framework leverages data-driven predictive modeling, intelligent optimization algorithms, and simulation-based evaluation to support decision-makers in generating optimal facility configurations under varying production scenarios. The system is grounded in recent advancements in AI applications in industrial and construction environments, where intelligent systems have demonstrated significant improvements in design accuracy, adaptability, and operational performance (Abioye et al., 2021). Furthermore, the integration of smart vision systems and deep learning techniques enhances spatial understanding and layout efficiency in manufacturing contexts (Baduge et al., 2022).
The study adopts an applied machine learning research methodology, combining optimization algorithms with predictive analytics to evaluate layout alternatives. The results indicate that AI-driven decision support systems significantly improve material flow efficiency, reduce operational bottlenecks, and enhance spatial utilization compared to conventional methods. The research contributes a structured AI-enabled decision framework that bridges the gap between theoretical facility layout optimization and practical industrial deployment.
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Copyright (c) 2026 Dr. Farhad Nikpour, Dr. Shirin Ghasemi (Author)
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