{"id":"W2037425183","doi":"10.1057/palgrave.jors.2602356","title":"VLSN search algorithms for partitioning problems using matching neighbourhoods","year":2007,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of New Brunswick","funders":"","keywords":"Neighbourhood (mathematics); Matching (statistics); Computer science; Exponential function; Algorithm; Mathematical optimization; Class (philosophy); Theoretical computer science; Mathematics; Artificial intelligence; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001370458,0.0004741259,0.0009139982,0.0008725346,0.0005950292,0.0008072954,0.001479055,0.0008318242,0.002743046],"category_scores_gemma":[0.005251653,0.0003483593,0.0006981668,0.001279335,0.0007123914,0.002470408,0.002240883,0.000816801,0.0005987153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000628763,"about_ca_system_score_gemma":0.0005693864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00100314,"about_ca_topic_score_gemma":0.00142392,"domain_scores_codex":[0.9993336,0.0002314304,0.0000464547,0.000142058,0.0001754062,0.00007109539],"domain_scores_gemma":[0.998719,0.0008056635,0.00009280008,0.0002036966,0.0001363321,0.00004264647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002323416,0.0001293529,0.001301886,0.0002992029,0.00006412226,0.0001093251,0.0002870413,0.6090791,0.00627359,0.11005,0.003553526,0.2686206],"study_design_scores_gemma":[0.00004574965,0.00008541423,0.000194704,0.00001883836,0.00001434478,0.00009557168,0.0000656239,0.9240645,0.001868074,0.06958553,0.003950774,0.00001079536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01564955,0.0001969067,0.9816915,0.00006780369,0.00001499298,0.00006833991,0.00002701356,0.0002391928,0.002044595],"genre_scores_gemma":[0.3350283,0.000354311,0.660538,0.0001074458,0.00003660263,0.0003362388,0.0003960548,0.0001767782,0.003026318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002743046,"threshold_uncertainty_score":0.009176373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1435839848271812,"score_gpt":0.4266171361414274,"score_spread":0.2830331513142462,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}