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Record W2004985180 · doi:10.1287/inte.30.2.41.11673

Air Transat Uses ALTITUDE to Manage Its Aircraft Routing, Crew Pairing, and Work Assignment

2000· article· en· W2004985180 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueINFORMS Journal on Applied Analytics · 2000
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsPolytechnique MontréalGroup for Research in Decision AnalysisHEC Montréal
Fundersnot available
KeywordsCharterScheduling (production processes)Operations researchCrew schedulingCrewFlexibility (engineering)NavyWork (physics)Computer scienceOperations managementEngineeringAeronautics

Abstract

fetched live from OpenAlex

Air Transat operates charter flights to vacation spots. In 1993, it had an opportunity to expand, but its manual planning and scheduling system could not support rapid growth. Air Transat acquired the airline operations management system ALTITUDE, a three-module optimization package for aircraft routing, crew pairing, and monthly work assignment. The system helped the airline to streamline planning and scheduling and to focus on its core business and expansion rather than internal operational problems. By reducing the planning cycle, increasing operational flexibility, and supporting marketing, the system helped the company to become the largest charter operator in Canada. ALTITUDE ensures cost-effective solutions by optimizing planning and scheduling problems and allowing easy interfacing among them. It saved the company an estimated eight to 12 percent of total costs during the first year and over a million dollars during the second year in operation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.247
Teacher spread0.231 · 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