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Record W2130662252 · doi:10.1287/trsc.1090.0266

Optimal Baggage-Limit Policy: Airline Passenger and Cargo Allocation

2009· article· en· W2130662252 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTransportation Science · 2009
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNewsvendor modelRevenueAir cargoTransport engineeringAir travelLimit (mathematics)BusinessLow-cost carrierAviationOperations researchSupply chainEngineeringMarketingFinanceAerospace engineering

Abstract

fetched live from OpenAlex

Although air carriers derive revenue from both passengers and cargo, the majority of the literature on airline management has focused on passengers. With the rapid growth in air freight, more studies are needed to examine the growing impact of air freight on air transportation. This paper addresses the optimal baggage-limit policy for airlines. Because much of the cargo is currently transported in the residual aircraft belly space after all of the passenger baggage has been enplaned, it is important for carriers to plan passenger and cargo levels together when setting passenger baggage limits. We formulate this problem as a variant of the price-dependent multi-item newsvendor model with weight-volume capacity constraints. The effects of baggage weight, prices, and costs on the number of passengers and amount of cargo carried are studied. Based on the model and carriers' existing practice, we develop several illustrative cases. Our findings suggest that airlines may be able to increase profits with significant reductions in passenger baggage limits for large aircraft.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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