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Airline seat allocation competition

2008· article· en· W2072747920 on OpenAlexafffund
Michael Z.F. Li, Anming Zhang, Yimin Zhang

Bibliographic record

VenueInternational Transactions in Operational Research · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsNash equilibriumProfit (economics)RivalryUniquenessMicroeconomicsCompetition (biology)Computer scienceEconomicsDilemmaAir traffic managementOperations researchCost allocationMathematical economicsMathematical optimizationMathematicsAir traffic control

Abstract

fetched live from OpenAlex

Abstract This paper examines the classical seat allocation problem under competition between two airlines with different cost structure. The cost asymmetry that has been ignored in the yield management literature can be caused by either operations or distributions. We investigate the decision problem of two airlines offering two identical fare classes under both the simultaneous and sequential allocations. For both allocation cases, we show the existence, uniqueness and stability of pure‐strategy Nash equilibrium under a reasonable condition on the ratios of relative profit margins of the two fare classes. We find that there will be fewer seats protected for the full‐fare class if the discount seats can be booked first. We found that the asymmetry in costs has two effects on the equilibrium solutions: (a) an airline behaves aggressively for the fare class where it enjoys a cost advantage; (b) an airline tends to balance the trade‐offs internally when it has absolute cost advantage in both fare classes. In deriving the collusive solution for both cases for comparative purposes, we discover new insights by solving the two‐flight, two‐fare seat allocation problem with different cost structures on the two flights. In particular, we show that rivalry in full‐fare seat protection leads to a Prisoners' Dilemma for the carriers. Finally, a numerical example is used to illustrate various analytical results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.000

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.

Opus teacher head0.117
GPT teacher head0.351
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2008
Admission routes2
Has abstractyes

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