{"id":"W4308195387","doi":"10.1016/j.ejor.2022.10.045","title":"Recent advances in vehicle routing with stochastic demands: Bayesian learning for correlated demands and elementary branch-price-and-cut","year":2022,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Vehicle routing problem; Bayesian probability; Benchmark (surveying); Iterated function; Mathematical optimization; Operations research; Feature (linguistics); Constraint (computer-aided design); Cover (algebra); Key (lock); Routing (electronic design automation); Artificial intelligence; Mathematics; Engineering","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":[0.005983418,0.001670216,0.003600136,0.00129501,0.0005423733,0.002846697,0.004426087,0.0023998,0.003193239],"category_scores_gemma":[0.01614662,0.00205673,0.001847818,0.003368279,0.002068362,0.006113691,0.002314729,0.005013875,0.000620474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002196779,"about_ca_system_score_gemma":0.002098961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005871002,"about_ca_topic_score_gemma":0.005151438,"domain_scores_codex":[0.997852,0.0008821044,0.0001046668,0.0004865333,0.0005434607,0.0001311949],"domain_scores_gemma":[0.9872162,0.009860986,0.000684587,0.0006696766,0.001245853,0.0003227379],"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":[0.00009993734,0.0001173451,0.001024959,0.000322972,0.0001209895,0.00003868311,0.00006731048,0.7559469,0.0002628251,0.1209055,0.002527255,0.1185654],"study_design_scores_gemma":[0.000007748607,0.00001300462,0.0001716704,0.00002053851,0.00001469568,0.00001138537,0.00000574578,0.9308911,0.0000816557,0.06772292,0.001050096,0.000009469666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006294218,0.006191006,0.9841314,0.0008562336,0.00009953729,0.00001991887,0.00007716435,0.00008450183,0.002245975],"genre_scores_gemma":[0.368223,0.03691739,0.5805629,0.0007830673,0.002097718,0.0001905369,0.0009961894,0.0004065511,0.009822624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005983418,"threshold_uncertainty_score":0.03164369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02970163875155932,"score_gpt":0.3193930716785738,"score_spread":0.2896914329270145,"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."}}