{"id":"W2119601963","doi":"10.1287/opre.1120.1048","title":"A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems","year":2012,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":685,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Vehicle routing problem; Benchmark (surveying); Metaheuristic; Mathematical optimization; Computer science; Population; Genetic algorithm; Routing (electronic design automation); Algorithm; Mathematics; Medicine","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":[0.001445568,0.0001025818,0.0001145636,0.0001537299,0.0005590562,0.000213889,0.0001064852,0.00005070542,0.00004130988],"category_scores_gemma":[0.000286841,0.0001062317,0.00002522896,0.0002401513,0.0000681301,0.0002522123,0.00006170103,0.000232042,0.00002827939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007823614,"about_ca_system_score_gemma":0.00003756439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000484222,"about_ca_topic_score_gemma":0.000008423943,"domain_scores_codex":[0.9986751,0.0001625999,0.0002173605,0.0001702949,0.0002271585,0.0005474593],"domain_scores_gemma":[0.9992352,0.0001925059,0.000006368768,0.000198518,0.0002080002,0.0001594322],"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":[0.000002239299,0.00006905315,0.003985792,0.0001050225,0.00003898013,0.000001311914,0.002484212,0.703074,0.02358387,0.000342437,0.00034305,0.2659701],"study_design_scores_gemma":[0.000300904,0.00002761648,0.003623697,0.00001777149,0.000004901511,0.000014461,0.0001279318,0.9907183,0.003872706,0.00001214586,0.001156287,0.0001232455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3355439,0.0007417302,0.6623821,0.00008227894,0.0000975229,0.0006761429,0.0000194949,0.0001385734,0.0003182714],"genre_scores_gemma":[0.5581167,0.0000606455,0.4410846,0.000008284718,0.0001579684,0.0002542058,0.00001210783,0.00003729462,0.0002682672],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2876444,"threshold_uncertainty_score":0.4332005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06420246300526486,"score_gpt":0.3580434042946693,"score_spread":0.2938409412894044,"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."}}