MétaCan
Menu
Back to cohort
Record W2099501018 · doi:10.1287/ijoc.1080.0312

<b>State-of-the Art Review</b>—Evolutionary Algorithms for Vehicle Routing

2009· article· en· W2099501018 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.

Bibliographic record

VenueINFORMS journal on computing · 2009
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsVehicle routing problemBenchmark (surveying)Evolutionary algorithmComputer scienceParticle swarm optimizationMathematical optimizationRouting (electronic design automation)MetaheuristicGenetic algorithmAlgorithmMemetic algorithmMathematics

Abstract

fetched live from OpenAlex

This paper is a survey of the literature on applications of evolutionary algorithms for vehicle routing problems. It reports on genetic algorithms, evolution strategies, and particle swarm optimization when applied to the classical capacitated vehicle routing problem and many of its variants. The performance of evolutionary algorithms is also compared with the best alternative problem-solving approaches on benchmark instances.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.627
Threshold uncertainty score0.540

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)0.0000.000

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 head0.020
GPT teacher head0.287
Teacher spread0.266 · 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