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Record W2128811073 · doi:10.1287/ijoc.1110.0454

A Hybrid Tabu Search and Constraint Programming Algorithm for the Dynamic Dial-a-Ride Problem

2011· article· en· W2128811073 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

VenueINFORMS journal on computing · 2011
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsTabu searchComputer scienceHybrid algorithm (constraint satisfaction)AlgorithmMathematical optimizationConstraint programmingGuided Local SearchScheduling (production processes)Constraint (computer-aided design)HeuristicDynamic programmingConstraint logic programmingMathematicsStochastic programming

Abstract

fetched live from OpenAlex

This paper introduces a hybrid algorithm for the dynamic dial-a-ride problem in which service requests arrive in real time. The hybrid algorithm combines an exact constraint programming algorithm and a tabu search heuristic. An important component of the tabu search heuristic consists of three scheduling procedures that are executed sequentially. Experiments show that the constraint programming algorithm is sometimes able to accept or reject incoming requests, and that the hybrid method outperforms each of the two algorithms when they are executed alone.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.867
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.275
Teacher spread0.250 · 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