MétaCan
Menu
Back to cohort
Record W2105559690 · doi:10.1109/mis.2005.58

A Guided Cooperative Search for the Vehicle Routing Problem with Time Windows

2005· article· en· W2105559690 on OpenAlexaff
Alexandre Le Bouthillier, Teodor Gabriel Crainic, Peter Kropf

Bibliographic record

VenueIEEE Intelligent Systems · 2005
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsUniversité du QuébecUniversité du Québec à MontréalUniversité de Montréal
Fundersnot available
KeywordsComputer scienceMetaheuristicMechanism (biology)Diversification (marketing strategy)Identification (biology)Search algorithmFocus (optics)Guided Local SearchSearch engineData miningArtificial intelligenceInformation retrievalAlgorithm

Abstract

fetched live from OpenAlex

We have developed a pattern-identification mechanism that endows cooperative search with capabilities to create new information and guide the global search. The proposed mechanism sends information to independent metaheuristics about promising and unpromising patterns in the solution space. By fixing or prohibiting specific solution attribute values in certain search metaheuristics, we can focus the search on desired regions. The mechanism thus enforces better coordination between individual methods and controls the global search's diversification and intensification. An enhanced cooperative-search mechanism creates new information from exchanged solutions and guides the global search with a pattern-identification mechanism.

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)0.002
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.292
Teacher spread0.254 · 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

Citations73
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Intelligent SystemsSame topicVehicle Routing Optimization MethodsFrench-language works237,207