{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007026747,0.0001376094,0.0001397656,0.00006883157,0.0001459826,0.00006896563,0.0001260256,0.0001424737,0.00003435812],"category_scores_gemma":[0.00001325843,0.0001331058,0.00005660864,0.00009356097,0.00002955272,0.0002821629,0.000003071099,0.00009025485,0.000009042252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006443715,"about_ca_system_score_gemma":0.00001083366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003087158,"about_ca_topic_score_gemma":0.000009284599,"domain_scores_codex":[0.9992964,0.000009352043,0.000188498,0.0001685156,0.0000844953,0.0002527244],"domain_scores_gemma":[0.9996335,0.00001883063,0.00002493882,0.0001787551,0.00005218645,0.00009180979],"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":[0.000001318985,0.00001176432,0.000003453544,0.00002757168,0.00001092171,1.913142e-7,0.001604272,0.9762803,0.01360857,0.0057184,0.0001075028,0.002625753],"study_design_scores_gemma":[0.0002288137,0.00002117182,0.0000143972,0.00000974956,0.000005836621,0.000001100213,0.0001882911,0.9967178,0.001993098,0.0005244552,0.0001144988,0.000180841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2993645,0.00007332011,0.698575,0.00002746471,0.0001801907,0.0001593719,0.000004119911,0.0002271498,0.001388875],"genre_scores_gemma":[0.8324691,0.00002351332,0.1668099,0.00004060959,0.0001622783,0.0001223336,0.00002502282,0.00004068043,0.0003065243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5331045,"threshold_uncertainty_score":0.5427898,"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."}}