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Record W2110620774 · doi:10.1287/opre.1120.1154

An Exact Algorithm for the Capacitated Arc Routing Problem with Deadheading Demand

2013· article· en· W2110620774 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

VenueOperations Research · 2013
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsArc routingBenchmark (surveying)Column generationMathematical optimizationRouting (electronic design automation)Computer scienceVehicle routing problemArc (geometry)Enhanced Data Rates for GSM EvolutionMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

We study an extension of the capacitated arc routing problem (CARP) called the capacitated arc routing problem with deadheading demand (CARPDD). This problem extends the classical capacitated arc routing problem by introducing an additional capacity consumption incurred by a vehicle deadheading an edge. It can be used, e.g., to model time or distance constrained arc routing problems. We show that the strongest CARP lower bounds can be weak when directly applied to the CARPDD, and we introduce a new family of valid inequalities shown to significantly strengthen these bounds. We develop an exact algorithm for the CARPDD based on cut-and-column generation and branch and price, and we report extensive computational results on a large set of benchmark instances. The same exact algorithm is also tested on classical CARP benchmark sets and is shown to improve upon the best known exact algorithms for the CARP.

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)0.002
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 score0.437
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.057
GPT teacher head0.359
Teacher spread0.301 · 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