AI-Optimized Warehouse Systems: Combining AGVs, RFID Technology, Zigbee Networks, and Agent-based Simulation Models

An AI-based system is proposed for automating warehousing processes regarding Automated Guided Vehicles (AGVs), Radio Frequency Identification (RFID), Zigbee networks, and Agent-Based Simulation Models. It tends to optimize efficiency, scalability, and flexibility for current supply chain scenarios. Self-operating material handling AGVs, RFID for on-time inventory update, Zigbee for optimal device-to-device communication, and agent-based models for process simulation and optimization are implemented. Key performance indicator-based performance evaluation detects improved performance 94% accuracy, 95% F1 score, and 92% scalability compared with traditional optimization techniques such as CSO, ACS, and WOA. Improvements guarantee better anomaly detection and improved operational effectiveness. The structure successfully transforms regular warehouse systems to intelligent, data-driven spaces to make real-time decisions and undergo adaptive logistics with improved sustainability and scalability in smart warehouse operations.