Automatic Guided Vehicles Introduction Impacts to Roll-On/Roll-Off Terminals: Simulation and Cost Model Analysis
Bibliographic record
Abstract
Automatic guided vehicles (AGVs) have been successfully applied to cargo terminals to reduce operating costs and improve productivity. However, the focus was on container terminal operations. Ports with roll-on/roll-off (RORO) terminals still heavily depend on human resources for the loading/unloading processes. Work operations are affected by human errors and safety issues. In particular, terminals where vehicles cannot be stacked pressure workers to handle cargo more rapidly, which induces more errors. In this study, we propose automating RORO terminal operations by using AGVs. We assessed the impact of AGVs on the productivity, cost efficiency, and environment. A series of simulation models was developed on the basis of the current loading system at an actual port to test the impact of AGVs. Then, we developed a cost model to analyze the economic benefit of AGVs compared with the current loading system. The environmental benefits were also analyzed. Results revealed that a system using 29 AGVs matched the productivity of the current loading system, and using more AGVs increased the productivity. For a given productivity level, the total operating cost of the AGV system was three times less than that of the current system over a 15-year period. The AGV system also showed great potential for improving the environmental friendliness of terminal operations. This is the first study to propose automating RORO terminal operations to improve productivity and sustainability through AGV technology rather than human factors. AGVs are expected to become a good option in the future to address labor shortages and the “untact” era.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".