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Optimal aircraft load balancing

2009· article· en· W2144900108 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

VenueInternational Transactions in Operational Research · 2009
Typearticle
Languageen
FieldEngineering
TopicOptimization and Packing Problems
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsComputer scienceMathematical optimizationPayload (computing)Set (abstract data type)Integer programmingFunction (biology)Linear programmingMacroPosition (finance)Cardinality (data modeling)Orientation (vector space)Operations researchMathematics

Abstract

fetched live from OpenAlex

Abstract The Canadian Forces employ cargo aircraft for countless missions to deliver equipment, supplies and passengers. Maximizing the payload while maintaining a safe load balance is of high importance. This paper details a mathematical formulation, a Mixed Integer Linear Program model, to solve the problem of determining the arrangement of a set of items in a cargo hold that optimizes the load balance. Items are modelled as rectangles with specified dimensions, mass and centre of gravity offsets. The main decision variables determine the orientation and placement of a given set of items. The objective function can be chosen to minimize deviation of the centre of gravity from the target position or to maximize a function of the items loaded (cardinality, priority, etc.). The formulation models item rotation, spacing requirements, load ordering, macro items, obstacles and constrained placement. Furthermore, specialized cut and transitivity constraints are developed that limit the solution search space.

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 categoriesInsufficient payload (model declined to judge)
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 score0.949
Threshold uncertainty score0.999

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.352
Teacher spread0.311 · 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