{"id":"W2159784590","doi":"10.1287/opre.1050.0222","title":"Dynamic Aggregation of Set-Partitioning Constraints in Column Generation","year":2005,"lang":"en","type":"article","venue":"Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kronos (Canada); Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Crew scheduling; Mathematical optimization; Scheduling (production processes); Computer science; Degeneracy (biology); Equivalence relation; Relaxation (psychology); Set (abstract data type); Equivalence (formal languages); Job shop scheduling; Relation (database); Routing (electronic design automation); Mathematics; Discrete mathematics","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":[0.001127034,0.0008904656,0.001178174,0.001102623,0.0007911824,0.0009419431,0.0009579198,0.0005442273,0.00408293],"category_scores_gemma":[0.004224605,0.0005332883,0.0008168226,0.002218859,0.0006049878,0.001256856,0.00124717,0.001170054,0.0005580947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006667795,"about_ca_system_score_gemma":0.001291765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004587472,"about_ca_topic_score_gemma":0.006331141,"domain_scores_codex":[0.9990694,0.0003309222,0.00005113736,0.0001270065,0.000292402,0.0001291822],"domain_scores_gemma":[0.9974579,0.001448044,0.0002032306,0.0003825001,0.0004262418,0.0000821194],"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":[0.0001563494,0.0002345328,0.001271432,0.0002119109,0.00006025371,0.0001702465,0.0002098578,0.6988422,0.01184431,0.02110877,0.006829986,0.2590601],"study_design_scores_gemma":[0.00004295155,0.00009032102,0.0002663911,0.00001711485,0.00002160213,0.00005915956,0.00004709995,0.9765772,0.005722497,0.01258913,0.004546012,0.00002050408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03740878,0.0002582082,0.9570859,0.0001612713,0.0000748904,0.0002418541,0.0002019291,0.0006824157,0.003884686],"genre_scores_gemma":[0.2808411,0.0002294978,0.7154226,0.000135847,0.00006703469,0.0003482376,0.0006677578,0.0001769219,0.002110861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004587472,"threshold_uncertainty_score":0.0136587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07615527304630118,"score_gpt":0.3934507778213092,"score_spread":0.317295504775008,"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."}}