MOGO-AFNNet: A Deep Learning and Multi-Objective Genetic Algorithm Framework for Intelligent logistics Warehouse Layout and Inventory Control
Abstract
In modern logistics, warehouse layout and inventory control face challenges such as demand variability, inefficient space utilization, and frequent stockouts, where traditional analytical systems often fail to adapt in real time. To address these issues, this study proposes a Multi-Objective Genetic Algorithm-driven Adaptive Fuzzy Neuro Network (MOGO-AFNNet), which integrates an Adaptive Fuzzy Neuro Network (AFNNet) for demand forecasting with the NSGA-II genetic algorithm for multi-objective optimization. The Smart Logistics Supply Chain Dataset, comprising real-time IoT-based records of shipments, delays, stock levels, and operational costs, was employed. Data preprocessing was performed using Z-score normalization to standardize features, followed by Principal Component Analysis (PCA) to extract key variables such as reorder frequency, lead time variability, and item popularity. The AFNNet component enabled adaptive inventory regulation under uncertainty, while NSGA-II optimized warehouse layout and inventory strategies across conflicting KPIs. Experimental evaluation showed that the proposed framework achieved 97.5% accuracy, 98% precision, 96.8% recall, and a 97.9% F1-score, significantly outperforming baseline models. ANOVA confirmed significant performance differences among models (F = 21.47, p = 0.0004). These results demonstrate that MOGO-AFNNet offers a scalable and robust solution for intelligent logistics, reducing stockouts and enhancing warehouse efficiency in dynamic operational environments.References
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