A Bilevel Modeling Approach to Pricing and Fare Optimization in the Airline Industry
Bibliographic record
Abstract
The airline revenue management problem can be decomposed into four distinct but related sub-problems that are usually treated separately: demand forecasting, overbooking, capacity allocation and pricing. Over the last decades, much interest has been devoted to the overbooking and capacity allocation issues and, today, most major airlines rely on computerized tools to deal with these two sub-problems. Pricing, however, has received less attention, which can be explained by the technical and theoretical di#culties inherent to the implementation of a practical Pricing Decision Support System. In this paper, we present a new modelling approach that allows for the joint solution of the capacity allocation and pricing sub-problems faced by a major North American airline. Using predefined booking limits, the resulting model can also applied be used in a "pure" pricing context. Our approach is based on the bilevel programming paradigm, a special case of hierarchical mathematical optimization. This modelling technique makes it possible to take into account matters such as customer segmentation, behavior with regard to fares and other product attributes, and the interactions induced by overlapping routes, which are typical of modern air transportation networks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".