Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem
Bibliographic record
Abstract
This work is devoted to the study of the Uncertain Quay Crane Scheduling Problem (QCSP), where the loading /unloading times of containers and travel time of quay cranes are considered uncertain.The problem is solved with a Simulation Optimization approach which takes advantage of the great possibilities offered by the simulation to model the real details of the problem and the capacity of the optimization to find solutions with good quality.An Ant Colony Optimization (ACO) meta-heuristic hybridized with a Variable Neighborhood Descent (VND) local search is proposed to determine the assignments of tasks to quay cranes and the sequences of executions of tasks on each crane.Simulation is used inside the optimization algorithm to generate scenarios in agreement with the probabilities of the distributions of the uncertain parameters, thus, we carry out stochastic evaluations of the solutions found by each ant.The proposed optimization algorithm is tested first for the deterministic case on several well-known benchmark instances.Then, in the stochastic case, since no other work studied exactly the same problem with the same assumptions, the Simulation Optimization approach is compared with the deterministic version.The experimental results show that the optimization algorithm is competitive as compared to the existing methods and that the solutions found by the Simulation Optimization approach are more robust than those found by the optimization algorithm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".