OPTIMAL FENCING IN AIRLINE INDUSTRY WITH DEMAND LEAKAGE S Y ED A SI FRAZAANDMIHA EL ATURI AC
Bibliographic record
Abstract
Four decades from its beginning in the airline industry, revenue management (RM) practice evolved rapidly to complex systems with applicability in many industries and gained researchers’ attention. McGill and Ryzin (1999) and more recently Chiang et al. (2007) presented in detail an overview of RM research. After the 1983 US Airline Deregulation Act, considered as one of the most important applications of management science and operations research (Bell, 1998), two essential features remained in RM practice: demand segmentation (which for an airline means managing the set of fare classes) 3.1 Introduction 53 3.2 Model Development 56 3.2.1 No Fencing Investment 58 3.2.2 With Fencing Investment 59 3.3 Model Analysis 60 3.3.1 Hierarchical Optimization 60 3.3.2 Joint Optimization 62 3.3.2.1 Linear Fencing Cost 63 3.3.2.2 Nonlinear Fencing Cost 63 3.4 Numerical Experimentation 64 3.5 Conclusions 69 Appendix 3.A 69 Acknowledgment 80 References 80 and fare classes availability management. In particular, airline RM research has focused on four categories: forecasting, overbooking, quantity/inventory (booking) control and pricing; however, as noticed in Cote et al. (2003), integration of pricing and quantity controls is expected to improve the firm’s revenue. The tactics and strategies of RM are applied in general in business that has a fixed or perishable resource like the flight seats in airline business or the hotels’ rooms.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".