{"id":"W2566755312","doi":"","title":"Airline crew scheduling: Models, algorithms, and data sets","year":2014,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew scheduling; Crew; Cockpit; Computer science; Scheduling (production processes); Column generation; Operations research; Distributed computing; Engineering; Mathematical optimization; Aeronautics; Operations management; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001572447,0.0003676559,0.000397792,0.0002870374,0.0001753388,0.0001926808,0.0007631957,0.0003044581,0.00001727267],"category_scores_gemma":[0.0003694066,0.0004049531,0.00005225985,0.0004168102,0.0000693244,0.0006422682,0.000463069,0.0004875811,0.00000969351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001389253,"about_ca_system_score_gemma":0.0000565433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006827353,"about_ca_topic_score_gemma":0.0002149949,"domain_scores_codex":[0.99786,0.0001654713,0.000485594,0.0005653866,0.0002905631,0.0006330039],"domain_scores_gemma":[0.9976923,0.0001828024,0.00009367119,0.001595901,0.00008543554,0.0003498897],"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":[0.00001010632,0.000040917,0.001436828,0.00007256803,0.0000465249,0.000007277115,0.00009992306,0.8853449,0.0009842782,0.005346937,0.001282769,0.105327],"study_design_scores_gemma":[0.0004124714,0.00003278161,0.001321443,0.00004636749,0.00003608712,0.00006450866,0.00002400875,0.991683,0.001156243,0.002242687,0.002553834,0.000426551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01262077,0.001916822,0.9807962,0.0009794584,0.0001455669,0.0003405662,0.00007986768,0.002248008,0.0008728111],"genre_scores_gemma":[0.2964075,0.0004627608,0.7020836,0.0005124069,0.0001463036,0.00005346781,0.0001204934,0.0001189818,0.00009448302],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2837868,"threshold_uncertainty_score":0.9998403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02730670661075655,"score_gpt":0.2704184124073049,"score_spread":0.2431117057965483,"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."}}