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Record W2035888716 · doi:10.1007/s13675-013-0012-1

Separating valid odd-cycle and odd-set inequalities for the multiple depot vehicle scheduling problem

2013· article· en· W2035888716 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEURO Journal on Computational Optimization · 2013
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsKronos (Canada)Group for Research in Decision AnalysisUniversité du Québec à MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDepotMathematical optimizationScheduling (production processes)Multi-commodity flow problemComputer scienceFlow networkColumn generationSet (abstract data type)MathematicsOperations research

Abstract

fetched live from OpenAlex

We consider the multicommodity network flow formulation of the multiple depot vehicle scheduling problem (MDVSP) and investigate several strategies within a branch-and-cut framework for solving the MDVSP. In particular, we compare subalgorithms for separating the cutting planes introduced by Hadjar et al. (Oper Res 54:130–149, 2006). We also evaluate the effectiveness of a new criterion for fixing some variables in the multicommodity flow formulation. Finally, we present computational experiments to illustrate the strengths and weaknesses of the strategies.

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: Methods
Teacher disagreement score0.390
Threshold uncertainty score0.713

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.289
Teacher spread0.253 · 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