Airline operational crew-aircraft planning considering revenue management: A robust optimization model under disruption
Why this work is in the frame
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Bibliographic record
Abstract
Airline planning involves various issues that, in a general, can be grouped as network planning, schedule design and fleet planning, aircraft planning, and crew scheduling decisions. This study mainly aims to optimize the Crew Scheduling (CS) decisions considering the operational constraints related to Aircraft Maintenance Routing (AMR) regulations. Since, after fuel, crew costs are vital for airlines, and aircraft maintenance constraints are important operationally, the integrated Crew Scheduling and Aircraft Maintenance Routing (CS-AMR) problem is an important issue for the airlines. The present research addresses this problem using the Revenue Management (RM) approach under some disruption scenarios in the initial schedule. The proposed approach enables airlines to make more efficient decisions during disruptions to prevent flight delay/cancellation costs and recaptures an acceptable part of the spilled demand caused by disruption through the fleet stand-by capacity. This approach considers a set of disruptions in the flight schedule under different probable scenarios and provides the optimal decisions. Accordingly, airlines have two decision-making stages: Here-and-Now (HN) decisions related to the initial schedule for crew, aircraft routing and stand-by capacity to face probable disruptions and Wait-and-See (WS) decisions that determine what the executive plan of each crew and aircraft should be under each scenario, and how to use different options for flight cancellation and substitution. To this end, a novel Two-Stage Robust Scenario-based Optimization (TSRSO) model is proposed that considers the HN and WS decisions simultaneously. A numerical example is solved, and its results verify the applicability and evaluate the performance of the proposed TSRSO model. Regarding the complexity of the proposed MILP model categorized as NP-hard problems, we develop a computationally efficient solution method to solve large-scale problem instances. A single-agent local search metaheuristic algorithm, Adaptive Large Neighborhood Search (ALNS), is applied to solve the CS-AMR problem efficiently. According to the result obtained by applying the proposed revenue management approach for the CS-AMR problem, airlines can drive a robust solution under disruption scenarios that not only minimizes the total delay/cancellation costs but also increases the profit by recapturing the spilled demand.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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