Positioning a Handshake Bay for Twin Stacking Cranes in an Automated Container Terminal Yard Block
Bibliographic record
Abstract
At automated container terminals (ACTs), twin automated stacking cranes (ASCs) can carry out the tasks—store and retrieve containers simultaneously in a yard block using a handshake bay, where a primary ASC stacks the container at the handshake bay and the other crane carries it to the destination bay. Although the handshake bay increases the degree of crane utilization, the ASCs will interfere with each other at the bay, decreasing the stacking efficiency. This study formulates a mixed-integer linear program (MILP) to position the handshake bay and simultaneously schedule the twin ASCs to minimize the tasks’ makespan. The proposed formulation considers the safe time interval to avoid crane collisions during adjacent crane movements. To solve the model, we developed a random-key genetic algorithm with a priority-based decoding scheme to optimize the task sequences and tasks assigned to the cranes. The priority-based GA can always generate feasible solutions by ranking the container-handling tasks. Numerical experiments prove that the safe temporal interval affects the makespan and the handshake bay’s position. An optimal handshake bay reduces 35% of the makespan compared with a nonoptimal bay, and the proposed algorithm is competitive compared with the on-the-shelf MILP solver and can solve medium- and large-scale instances in short computing time with gaps lower than 5% compared with ideal solutions.
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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".