MétaCan
Menu
Back to cohort
Record W3001806490 · doi:10.1109/access.2020.2968410

Airplane Boarding Method for Passenger Groups When Using Apron Buses

2020· article· en· W3001806490 on OpenAlexaff
R. John Milne, Liviu‐Adrian Cotfas, Camelia Delcea, Mostafa Salari, Liliana Crăciun, Anca Gabriela Molănescu

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAirplaneDoorsComputer scienceBaseline (sea)Integer programmingEngineeringAerospace engineeringAlgorithmOperating system

Abstract

fetched live from OpenAlex

This paper proposes a method for reducing the time to complete the boarding of a two-door airplane when its passengers are transported from the airport terminal to the airplane using two apron buses. In contrast to other methods that assign passengers to apron buses, our method considers groups of passengers traveling together (e.g. families). In particular, we propose a mixed integer programming (MIP) model that assigns each group of passengers (including each single-passenger group) to one of the two apron buses based on their seating assignments. We assume that all seats on the apron buses and the two-door airplane are occupied. We conduct stochastic simulation experiments with the proposed MIP-based method and with a baseline method that assigns groups of passengers with seats furthest from one of the airplane doors to the first apron bus and assigns remaining groups to the second apron bus. Numerical results indicate that the proposed MIP-based method reduces the boarding time by up to 27.31% when compared with the baseline approach.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.235
GPT teacher head0.348
Teacher spread0.113 · 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 teacher head, 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

Citations18
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueIEEE AccessSame topicAviation Industry Analysis and TrendsFrench-language works237,207