An Optimization Model for the Simultaneous Operational Flight and Pilot Scheduling Problem
Bibliographic record
Abstract
This paper describes and solves the operational pilot scheduling problem for one day of operations. The problem consists in simultaneously modifying, as necessary, the existing flight departure schedules and planned individual work days (duties) while keeping planned aircraft itineraries unchanged. It requires the covering of all flights from one day of operations with available pilots while minimizing changes in both the flight schedule and the next day's planned duties. The newly constructed personalized duties must not exceed the maximum duty duration. Flight precedence constraints, coming from existing fixed aircraft itineraries, must be respected as well. The problem is mathematically formulated as an integer nonlinear multicommodity network flow model with time windows and additional constraints. To solve the problem, a Dantzig-Wolfe decomposition combined with a branch-and-bound method has been used. The master problem comprises the flight-covering constraints and a new set of flight precedence constraints. Subproblems consisting of time-constrained shortest-path problems with linear time costs are solved by a specialized dynamic-programming algorithm. The proposed optimization approach has been tested on several input data sets. All of them have been successfully solved in very short computational time.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".