{"id":"W2758864124","doi":"","title":"An Extended Branch-and-Bound Method for Locomotive Assignment","year":2003,"lang":"fr","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Backtracking; Branch and bound; Mathematical optimization; Computer science; Heuristic; Node (physics); Set (abstract data type); Branch and cut; Branch and price; Integer programming; Operations research; 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":[0.001945926,0.001272788,0.001406343,0.001536235,0.000653724,0.001146331,0.002145504,0.001462405,0.006396064],"category_scores_gemma":[0.004336755,0.0007171404,0.0007969357,0.002306793,0.0007298235,0.001530911,0.001153741,0.001609841,0.001518102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009395405,"about_ca_system_score_gemma":0.001475874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005927132,"about_ca_topic_score_gemma":0.005040663,"domain_scores_codex":[0.9988977,0.0003965301,0.00004124887,0.0001351696,0.0004267842,0.0001026547],"domain_scores_gemma":[0.9983211,0.001092573,0.0001056904,0.0001231244,0.0003027397,0.00005471412],"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":[0.0002286697,0.0001229674,0.000439019,0.0002089619,0.00007984508,0.00009575472,0.0001070013,0.6711485,0.003044964,0.02745385,0.003508788,0.2935618],"study_design_scores_gemma":[0.00004234773,0.00004097959,0.00007326675,0.00002008947,0.00001203699,0.00002634021,0.000006231921,0.9878236,0.0005227016,0.008110339,0.003311918,0.00001000734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002093123,0.000224079,0.9953734,0.00004484924,0.00002340957,0.00004886111,0.00003024833,0.0002841328,0.001877796],"genre_scores_gemma":[0.06597375,0.0003060363,0.9296662,0.00008287141,0.00005072466,0.0003004395,0.0001892064,0.0002268645,0.003203938],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006396064,"threshold_uncertainty_score":0.02139693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01475733785249362,"score_gpt":0.2841046295085984,"score_spread":0.2693472916561048,"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."}}