{"id":"W2652595374","doi":"","title":"An Enhanced Optimization Model for Scheduling Freight Trains","year":2013,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Pacific Railway (Canada); Concordia University","funders":"","keywords":"Train; Schedule; Scalability; Scheduling (production processes); Computer science; Column generation; Freight trains; Track (disk drive); Mathematical optimization; Convergence (economics); Operations research; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006898351,0.001072591,0.0008700896,0.0003941403,0.0002911451,0.001216642,0.001478292,0.001067288,0.005462538],"category_scores_gemma":[0.001200911,0.0005148879,0.0007948281,0.0008225791,0.0004673726,0.001214421,0.0008558547,0.001550033,0.0007636448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001212089,"about_ca_system_score_gemma":0.001627378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01032781,"about_ca_topic_score_gemma":0.008737406,"domain_scores_codex":[0.9994973,0.0001411524,0.00002257938,0.000118301,0.0001439766,0.00007664703],"domain_scores_gemma":[0.9997011,0.0001424848,0.00004660667,0.00002485061,0.00005859947,0.00002632875],"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":[0.00001419415,0.0000180424,0.00005320478,0.00002015787,0.000005931725,0.00001994966,0.000009655771,0.9893925,0.0005659368,0.005898536,0.0004753458,0.003526581],"study_design_scores_gemma":[0.000005172614,0.000007107848,0.00001842319,0.000001275644,0.000001860289,0.000003371506,0.000001842569,0.998323,0.00008259371,0.00104652,0.0005073872,0.000001396733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01008357,0.0001832936,0.9836088,0.0001767098,0.00004702334,0.00007095905,0.000225091,0.0002242547,0.005380348],"genre_scores_gemma":[0.5349836,0.0007637403,0.4411236,0.0002433228,0.000136353,0.0005904689,0.0008123409,0.0002270383,0.02111949],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01032781,"threshold_uncertainty_score":0.02053541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007685638485981742,"score_gpt":0.1907168208890957,"score_spread":0.1830311824031139,"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."}}