Optimizing the number of deployed yard cranes in a container terminal
Bibliographic record
Abstract
Container terminals play an essential role in the current global supply chain, and their efficiency influences businesses throughout the supply chain. One of the most critical decisions in the operation of container terminals is the number of deployed rubber tyred gantry (RTG) cranes to handle the containers. The RTG crane is the main equipment that handles containers in the stacking area of many container terminals worldwide. Every shift, container terminal operators decide how many RTG cranes are needed to stack and retrieve containers. This decision, if made optimally, can significantly improve the daily throughput of terminals and dramatically reduce port congestion. This article aims to determine the optimal number of RTG cranes in a container terminal by formulating the problem as a mixed-integer linear program. Our proposed formulation can find optimal solutions efficiently, usually in less than a second. The results show that the workload, storage capacity, and shift duration significantly influence the optimal number of RTG cranes.
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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 | 0.001 | 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".