{"id":"W3104464641","doi":"10.1016/j.cor.2023.106526","title":"Unified Branch-and-Benders-Cut for two-stage stochastic mixed-integer programs","year":2024,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Foundation for Innovation; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Mathematical optimization; Benders' decomposition; Integer (computer science); Decomposition; Generality; Heuristic; Branch and bound; Heuristics; Discretization; Stochastic programming; Mathematics; Branch and cut; Computer science; Integer programming","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00118507,0.0001584352,0.0001650707,0.0003906624,0.0003276143,0.0008475084,0.0002279534,0.00007959051,0.00002611022],"category_scores_gemma":[0.0001260059,0.0001583307,0.0000590152,0.0007149648,0.0001185487,0.0002414752,0.0001008561,0.0004314058,0.00004306847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001284504,"about_ca_system_score_gemma":0.0001026252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002859876,"about_ca_topic_score_gemma":0.00006750174,"domain_scores_codex":[0.9984689,0.000173215,0.0002516654,0.000356027,0.0002888682,0.0004613268],"domain_scores_gemma":[0.9987758,0.0005432433,0.000004414164,0.0002851296,0.0002530534,0.000138405],"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":[0.000004754918,0.00001757237,0.000003390347,0.0001640833,0.00005566902,0.000003603189,0.0007447816,0.936435,0.0009687456,0.01712526,0.001356668,0.04312045],"study_design_scores_gemma":[0.0003055076,0.0000793842,0.000009787719,0.0001071436,0.000008749135,0.0000082801,0.0001571199,0.9960316,0.0005644241,0.00016384,0.002399005,0.0001651935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01241316,0.0003952371,0.9839687,0.0005352479,0.0006565716,0.0009425004,0.00001663014,0.0006563462,0.0004155992],"genre_scores_gemma":[0.8848013,0.00001751088,0.1132509,0.00002526433,0.0002393804,0.0002796624,0.0000772679,0.00007839677,0.001230259],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8723882,"threshold_uncertainty_score":0.8172543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09352603497581218,"score_gpt":0.3962700240992904,"score_spread":0.3027439891234782,"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."}}