MétaCan
Menu
Back to cohort
Record W2523297565 · doi:10.1080/00207543.2016.1231940

A column generation based heuristic for the capacitated vehicle routing problem with three-dimensional loading constraints

2016· article· en· W2523297565 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

VenueInternational Journal of Production Research · 2016
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsConcordia University
Fundersnot available
KeywordsColumn generationMathematical optimizationHeuristicFIFO and LIFO accountingBenchmark (surveying)Tabu searchVehicle routing problemComputer scienceRouting (electronic design automation)ComputationAlgorithmMathematicsFIFO (computing and electronics)

Abstract

fetched live from OpenAlex

This paper addresses an integrated problem of vehicle routing and three-dimensional loading with additional practical constraints such as stability, fragility and LIFO. A column generation (CG) technique-based heuristic is proposed to handle this problem. To generate new columns in CG technique, first, an elementary shortest path problem is solved to find routes with negative reduced cost. Then an extreme point-based heuristic method is employed to verify feasibility of obtained routes in terms of loading and other constraints. To speed up the CG technique, fast column generation is also performed by applying an efficient heuristic pricing method. The CG technique, tested on the benchmark instances, outperforms the efficient tabu search method developed in the literature in terms of solution quality and computation time.

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)0.004
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 head0.093
GPT teacher head0.355
Teacher spread0.262 · 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