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Record W2100711246 · doi:10.1287/ijoc.1060.0202

Variable Neighborhood Search for the Pickup and Delivery Traveling Salesman Problem with LIFO Loading

2007· article· en· W2100711246 on OpenAlexaff
Francesco Carrabs, Jean‐François Cordeau, Gilbert Laporte

Bibliographic record

VenueINFORMS journal on computing · 2007
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsTravelling salesman problemPickupFIFO and LIFO accountingVariable (mathematics)Mathematical optimizationVariable neighborhood searchHeuristic2-optTraveling purchaser problemHeuristicsComputer scienceMathematicsFIFO (computing and electronics)MetaheuristicArtificial intelligence

Abstract

fetched live from OpenAlex

This paper addresses a variation of the traveling salesman problem with pickup and delivery in which loading and unloading operations have to be executed in a last-in-first-out (LIFO) order. We introduce three new local search operators for this problem, which are then embedded within a variable neighborhood search heuristic. We evaluate the performance of the heuristic on data adapted from TSPLIB instances.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.258
Teacher spread0.239 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations139
Published2007
Admission routes1
Has abstractyes

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