{"id":"W2916653839","doi":"10.1002/net.21925","title":"Primal column generation framework for vehicle and crew scheduling problems","year":2020,"lang":"en","type":"article","venue":"Networks","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crew scheduling; Column generation; Scheduling (production processes); Crew; Column (typography); Computer science; Operations research; Aeronautics; Engineering; Operations management; Mathematics; Mathematical optimization","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":[0.0001916042,0.0001034961,0.0001332767,0.0000148135,0.0000953153,0.00008741445,0.00006313319,0.0001568337,0.00001784198],"category_scores_gemma":[0.000121697,0.0001213792,0.00002723771,0.0001643709,0.0000153692,0.00008985693,0.00002273086,0.0002009945,0.000002214409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002013941,"about_ca_system_score_gemma":0.000008349349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.108184e-7,"about_ca_topic_score_gemma":0.000001068269,"domain_scores_codex":[0.9993419,0.0000287594,0.0001789719,0.000175981,0.00006951084,0.0002048807],"domain_scores_gemma":[0.9996278,0.0001147893,0.00003055827,0.00008845434,0.00003888092,0.00009955576],"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":[0.00000446749,0.000002846141,0.0005021633,0.00004044931,0.00001356474,2.463019e-7,0.0002020358,0.9804189,0.0008213466,0.0009769812,0.0003430246,0.01667402],"study_design_scores_gemma":[0.0002309326,0.00003585981,0.0001533588,0.00002764447,0.0000138385,7.509974e-7,0.00001143036,0.9977864,0.0004950934,0.0001905585,0.0009207319,0.0001333877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02517123,0.0008280157,0.9726388,0.0003458981,0.0002136384,0.0003182247,0.000002015395,0.0003489892,0.0001331411],"genre_scores_gemma":[0.5116333,0.0000578846,0.4871435,0.0002969924,0.0007842401,0.00003195985,0.00001133975,0.00003545551,0.000005330783],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4864621,"threshold_uncertainty_score":0.4949701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03948768764023189,"score_gpt":0.2636379322735107,"score_spread":0.2241502446332788,"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."}}