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Record W2089725715 · doi:10.1057/jors.2009.83

Saving-based algorithms for vehicle routing problem with simultaneous pickup and delivery

2009· article· en· W2089725715 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

VenueJournal of the Operational Research Society · 2009
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHeuristicsVehicle routing problemPickupComputer scienceHeuristicRouting (electronic design automation)Extension (predicate logic)Mathematical optimizationAlgorithmMathematicsEmbedded systemArtificial intelligence

Abstract

fetched live from OpenAlex

The vehicle routing problem (VRP) with simultaneous pickup and delivery (VRPSPD) is an extension of the classical capacitated VRP (CVRP). In this paper, we present the saving heuristic and the parallel saving heuristic for VRPSPD. Checking the feasibility of a route in VRPSPD is difficult because of the fluctuating load on the route. In the saving heuristic, a new route is created by merging the two existing routes. We use a cumulative net-pickup approach for checking the feasibility when two existing routes are merged. The numerical results show that the performance of the proposed heuristics is qualitatively better than the existing insertion-based heuristics.

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)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.042
GPT teacher head0.336
Teacher spread0.294 · 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