{"id":"W2603251541","doi":"10.1002/net.20332","title":"A branch‐and‐price‐based large neighborhood search algorithm for the vehicle routing problem with time windows","year":2009,"lang":"en","type":"article","venue":"Networks","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Mathematical optimization; Heuristic; Set (abstract data type); Computer science; Diversification (marketing strategy); Routing (electronic design automation); Algorithm; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008992765,0.0001967769,0.0002068181,0.00004332509,0.0002705573,0.0001143892,0.0001762007,0.0001256666,0.00002590879],"category_scores_gemma":[0.00001896417,0.0001476723,0.00005074737,0.0003939937,0.00002547975,0.0001065928,0.00002236218,0.0003422848,0.000004607556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004357969,"about_ca_system_score_gemma":0.00002654095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002313891,"about_ca_topic_score_gemma":0.000001354596,"domain_scores_codex":[0.9987096,0.00007505561,0.0002173784,0.0002370254,0.0001798919,0.0005810531],"domain_scores_gemma":[0.9990655,0.0004586159,0.00004174074,0.0002607633,0.00007929919,0.00009406143],"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":[0.00001300933,0.00001735338,0.0003728821,0.000009414593,0.00002773031,0.000001046,0.0001146653,0.7934887,0.0000573441,0.0001213713,0.0001503763,0.2056261],"study_design_scores_gemma":[0.001303945,0.0001045759,0.001739787,0.00005478935,0.00003237518,0.000005125955,0.00001520027,0.9958588,0.0001256664,0.00004884766,0.0005101016,0.0002007326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001132509,0.0004508696,0.9958138,0.0003339803,0.00005611898,0.0007058597,0.000006989146,0.0004144485,0.001085424],"genre_scores_gemma":[0.7735234,0.00002224665,0.2253575,0.0004375707,0.0003952721,0.0000546305,0.00001720531,0.00007265035,0.0001194976],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7723908,"threshold_uncertainty_score":0.60219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009967092434046134,"score_gpt":0.2421302769561041,"score_spread":0.2321631845220579,"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."}}