Bibliographic record
Abstract
Flight dispatchers are responsible for flight planning prior to departure and flight monitoring while en-route. Their work involves multi-tasking and their workload is dynamic. We study such a problem under two nonlinear workload balancing measures: minimum peak workload and minimum absolute deviation. In order to solve practical instances efficiently, we use decomposition through Lagrangian relaxation to reduce the problem into easier-to-solve subproblems and prove that the Lagrangian lower bound has a closed-form expression for the peak workload objective. To find feasible solutions, we develop a Focus-Search-and-Improve heuristic with a genetic algorithm core where parts of the feasible solution set are explored and searched by a genetic algorithm, and solutions are further fine-tuned by an improvement heuristic. To test the efficiency of the proposed approach, we generated 231 instances based on 2019 U.S. Bureau of Transportation flight data that involve 17 different carriers and up to 3968 flights per instance. Numerical testing demonstrates the efficiency of the proposed approach in that the Lagrangian lower bound is very tight, and the heuristic finds optimal solutions in 33.4% of the instances and are on average 3.5% away from the Lagrangian lower bound. It also reveals that the difficulty of the problem increases for smaller workstation-to-flight ratios, and that the peak workload objective achieves the goal of balancing the workload at times where peaks occur but does not necessarily balance the workload throughout the workday. On the other hand, the absolute deviation objective achieves better balance between workstations at the expense of a slight increase in peak workload. • Goal: Assign flight dispatchers to workstations to balance workload during the shift. • Means: Minimize peak workload and absolute mean deviation at every time point. • Model: MIP solved through a Focus-Search-and-Improve heuristic. • Testing: 231 instances involving 17 different carriers and up to 3968 flights per instance.
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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 | 0.001 |
| 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".