A unique hybrid particle swarm optimisation algorithm for simulation and improvement of crew scheduling problem
Why this work is in the frame
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Bibliographic record
Abstract
The crew scheduling problem is a set covering or set partitioning problem. It schedules the crew members so that all flights are covered, while the cost is minimised. The crew scheduling is an non-deterministic polynomialtime hard constrained combinatorial optimisation problem, so it cannot be exactly solved in a reasonable computation time. This paper presents a particle swarm optimisation (PSO) algorithm for simulating and solving the crew scheduling problem. The proposed algorithm is extended from the discrete version of PSO. By applying PSO to the crew scheduling problem, the cost is improved when compared with other well-known algorithms. This is the first study that introduces PSO for simulation and optimisation of the crew scheduling problem.
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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 | 0.013 | 0.006 |
| 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 | 0.001 |
| 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 it