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On‐orbit servicing: a time‐dependent, moving‐target traveling salesman problem

2006· article· en· W2073089651 on OpenAlexaff
Jean-Marie Bourjolly, Özgür Gürtuna, A. Lyngvi

Bibliographic record

VenueInternational Transactions in Operational Research · 2006
Typearticle
Languageen
FieldComputer Science
TopicOptimization and Search Problems
Canadian institutionsConcordia UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsSpacecraftSpare partTravelling salesman problemConsumablesComputer scienceOrbit (dynamics)Real-time computingAerospace engineeringSimulationOperations managementAlgorithmEngineeringBusiness

Abstract

fetched live from OpenAlex

Abstract The robotic capability of maintaining and repairing space assets, on‐orbit servicing (OOS), has the potential to change the way spacecraft are designed, manufactured and operated. The most common OOS mission concept envisions an orbital “depot”, where consumables and spare parts for spacecraft will be stored. A “servicing platform”, based at this depot, will be used to service a number of client spacecraft and then return to the depot for resupply. We model OOS as a time‐dependent, moving‐target traveling salesman problem and present an algorithm for minimizing the total amount of energy or time required for OOS operations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.042
GPT teacher head0.348
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2006
Admission routes1
Has abstractyes

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