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Record W2968190218 · doi:10.1109/uemcon.2018.8796573

Slot Reallocation for Ground Delay Programs

2018· article· en· W2968190218 on OpenAlexaff
Yin Sheng, Haibin Zhu, Guan Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsNipissing University
Fundersnot available
KeywordsProfit (economics)Computer scienceOperations researchAir traffic controlCommon groundTransport engineeringEngineeringEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Collaborative Decision Making (CDM) has been adopted by major airlines in most countries to improve Air Traffic Flow Management (ATFM). The CDM introduces significant benefits to Ground Delay Programs (GDP), and one is to reduce the overall delay costs by allocating and reallocating slots to flights. This paper focuses on the slot reallocation problem, and begins by modeling this problem with environments-classes, agents, roles, groups, and objects (E-CARGO) model. Then a qualification matrix expressing the profit of assigning each flight to each slot is obtained by integrating all the roles' requirements. An approach using group role assignment (GRA) is proposed to maximize the total profit according to the qualification matrix. Experiment results proves the effectiveness and efficiency of the proposed 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 score0.953
Threshold uncertainty score0.168

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 communication0.0000.000
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 head0.015
GPT teacher head0.218
Teacher spread0.203 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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