AI-Optimized Warehouse Systems: Combining AGVs, RFID Technology, Zigbee Networks, and Agent-based Simulation Models
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".