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Record W2787686159 · doi:10.1109/ssci.2017.8285385

An age layered population structure genetic algorithm for the multi-depot vehicle problem

2017· article· en· W2787686159 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsBrock University
Fundersnot available
KeywordsVehicle routing problemGenetic algorithmBenchmark (surveying)Ranking (information retrieval)Pareto principleMathematical optimizationComputer sciencePopulationRouting (electronic design automation)MathematicsArtificial intelligenceGeographyComputer network

Abstract

fetched live from OpenAlex

The vehicle routing problem is experienced in several industries such as transportation, distribution and logistics. We study the Multi-depot vehicle routing problem (MDVRP) a variant of the classical VRP with several depots and customers as a multi-objective problem where both the number of vehicles and total distance travelled are minimized simultaneously. A multipopulation structure, i.e., the age layered population structure genetic algorithms (ALPS-GA) with inter-layer transfer strategies is proposed. An empirical study is carried out using well-known MDVRP benchmark problems and using both the weighted sum and Pareto ranking fitness evaluation strategies. A comparative study with previously published genetic algorithms results shows the effectiveness of the proposed ALPS-GA approach.

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)0.000
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: Methods
Teacher disagreement score0.419
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.028
GPT teacher head0.305
Teacher spread0.277 · 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

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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