{"id":"W2135680984","doi":"10.1287/trsc.1070.0223","title":"Tabu Search, Partial Elementarity, and Generalized <i>k</i>-Path Inequalities for the Vehicle Routing Problem with Time Windows","year":2008,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":227,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Vehicle routing problem; Tabu search; Mathematical optimization; Benchmark (surveying); Generalization; Heuristic; Routing (electronic design automation); Path (computing); Set (abstract data type); Computer science; Relaxation (psychology); Shortest path problem; Mathematics; Graph; Theoretical computer science","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":[0.001077044,0.0001123808,0.0001224617,0.00005602002,0.0005357256,0.00006436481,0.0001731067,0.00002429538,0.00002127016],"category_scores_gemma":[0.00001815835,0.00008422788,0.00002162185,0.0004464596,0.0003276221,0.0003935876,0.000005348725,0.00008619832,0.000001892493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002962169,"about_ca_system_score_gemma":0.00009005233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009405001,"about_ca_topic_score_gemma":0.00003777423,"domain_scores_codex":[0.9988167,0.00003115987,0.0002537287,0.0002162041,0.0003778682,0.0003043029],"domain_scores_gemma":[0.9994735,0.0001446698,0.00004422565,0.0001389256,0.000125317,0.0000734382],"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":[0.00004895938,0.00001802092,0.03505726,0.00005923133,0.0000224835,0.000002808441,0.01314132,0.9169374,0.02996005,0.00263386,0.00005571728,0.00206296],"study_design_scores_gemma":[0.001122268,0.00006766857,0.03717532,0.0000237147,0.00002734129,0.00000613979,0.0003774705,0.9379273,0.02267597,0.00006040123,0.0003166034,0.0002198054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7052914,0.00003385648,0.2938739,0.0001273376,0.00003135904,0.0003977469,0.00002274722,0.0001381275,0.00008350238],"genre_scores_gemma":[0.8933262,0.00002734797,0.1063803,0.0000748908,0.00003300517,0.0000551323,0.00001515961,0.00001798587,0.00006997936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1880349,"threshold_uncertainty_score":0.4120423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03526509438761437,"score_gpt":0.2763590414941016,"score_spread":0.2410939471064872,"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."}}