{"id":"W3021274929","doi":"10.2298/yjor0501025s","title":"The operational flight and multi-crew scheduling problem","year":2005,"lang":"en","type":"article","venue":"Yugoslav journal of operations research","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Crew; Crew scheduling; Operations research; Computer science; Column generation; Scheduling (production processes); Schedule; Set cover problem; Set (abstract data type); Mathematical optimization; Aeronautics; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.001351974,0.00007592553,0.00009309444,0.0002142678,0.0007115233,0.000293512,0.0001720574,0.00004596268,0.00007776164],"category_scores_gemma":[0.0001238648,0.00005381136,0.00003449801,0.00038237,0.0001225612,0.0004730126,0.00001207353,0.0005060077,0.0000246103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007060362,"about_ca_system_score_gemma":0.0001861203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007359296,"about_ca_topic_score_gemma":0.0003758098,"domain_scores_codex":[0.9987825,0.00006603885,0.0004761842,0.00007773162,0.0003961984,0.000201368],"domain_scores_gemma":[0.9986309,0.0001320092,0.00001588499,0.0001228837,0.001007529,0.00009075476],"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":[0.00001820759,0.000144459,0.0007711151,0.00002581842,0.0001339596,0.000005702872,0.002243801,0.8989887,0.02772904,0.03907221,0.003888597,0.02697834],"study_design_scores_gemma":[0.002219735,0.0001812898,0.01509904,0.0001135521,0.00002938423,0.0001910068,0.002032172,0.7768849,0.01081904,0.0002359441,0.1918699,0.0003240941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8934464,0.00447473,0.07743707,0.02071184,0.0003012164,0.0006817253,0.00002458295,0.00007398421,0.00284843],"genre_scores_gemma":[0.940083,0.0007486285,0.05825339,0.00004560867,0.0002161529,0.0000251077,0.000006852897,0.00001421029,0.0006070803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1879813,"threshold_uncertainty_score":0.5472534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06053945776518042,"score_gpt":0.3664722131157127,"score_spread":0.3059327553505322,"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."}}