{"id":"W2150889816","doi":"10.1287/trsc.34.2.133.12308","title":"A Benders Decomposition Approach for the Locomotive and Car Assignment Problem","year":2000,"lang":"en","type":"article","venue":"Transportation Science","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":173,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lagrangian relaxation; Decomposition; Column generation; Integer programming; Benders' decomposition; Mathematical optimization; Decomposition method (queueing theory); Simplex algorithm; Context (archaeology); Linear programming relaxation; Computer science; Dynamic programming; Linear programming; Set (abstract data type); Engineering; Mathematics","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":[0.0001766555,0.00005226535,0.00004517508,0.00002125847,0.0001468723,0.00003604461,0.00006943565,0.00001457469,0.00005146542],"category_scores_gemma":[0.00000114056,0.00003849234,0.00001323186,0.0001140115,0.0001415972,0.0001095183,4.994172e-7,0.00003367827,7.121093e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001656543,"about_ca_system_score_gemma":0.00001295646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001977853,"about_ca_topic_score_gemma":0.00001336445,"domain_scores_codex":[0.9995436,0.000002258969,0.00009664612,0.0001130998,0.0001290909,0.0001152902],"domain_scores_gemma":[0.9998427,0.00002558401,0.00001049824,0.00006037463,0.00002455523,0.00003628387],"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":[0.00001332499,0.00003374598,0.0007816778,0.00008896599,0.0000150038,0.000001078409,0.003898812,0.8999333,0.001337432,0.01134859,0.0001860474,0.08236202],"study_design_scores_gemma":[0.000573401,0.00008029753,0.1062386,0.00001684894,0.0000711165,0.000004318789,0.000766823,0.8824071,0.002547268,0.001502893,0.005503691,0.0002876792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05473192,0.00009498162,0.9348927,0.00009898211,0.00004494201,0.0006292579,0.0000363676,0.00008403837,0.009386802],"genre_scores_gemma":[0.988292,0.00003748454,0.01149711,0.00002951468,0.00001047438,0.00005376846,0.00001885691,0.000004539932,0.00005620497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9335601,"threshold_uncertainty_score":0.1569672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01422500301572305,"score_gpt":0.2400552185029919,"score_spread":0.2258302154872689,"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."}}