{"id":"W2997174909","doi":"10.1016/j.ifacol.2019.11.632","title":"On the impact of the number of operators in a flow shop environment","year":2019,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université du Québec à Chicoutimi","funders":"","keywords":"Operator (biology); Task (project management); Scheduling (production processes); Computer science; Quality (philosophy); Sequence (biology); Flow (mathematics); Flow shop scheduling; Mathematical optimization; Job shop scheduling; Mathematics; Schedule; Engineering","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":[0.005021533,0.001215598,0.0009792495,0.0008126562,0.0008951378,0.001738673,0.001017928,0.001229653,0.003091934],"category_scores_gemma":[0.04018618,0.0004802819,0.0005176343,0.0008334991,0.001813883,0.00425451,0.001339244,0.001551085,0.0001612482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009371422,"about_ca_system_score_gemma":0.0009849187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002479292,"about_ca_topic_score_gemma":0.002622571,"domain_scores_codex":[0.9951652,0.002978717,0.000138501,0.0003975481,0.0006788963,0.0006410851],"domain_scores_gemma":[0.8552509,0.1363538,0.003093998,0.001470175,0.001995793,0.00183541],"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":[0.001329216,0.0005007542,0.006541227,0.000229536,0.0001233492,0.0004633148,0.00008921378,0.9530073,0.005369497,0.006374632,0.0006549445,0.02531702],"study_design_scores_gemma":[0.00008552123,0.001171465,0.004717825,0.00005185641,0.0001672924,0.0002623339,0.0002388787,0.9790428,0.003839205,0.009528073,0.0008543776,0.00004040377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9003978,0.002596624,0.07689475,0.001572257,0.000208761,0.000108978,0.0001845112,0.0001689518,0.01786742],"genre_scores_gemma":[0.9849065,0.0004597782,0.01366117,0.00009272435,0.00008244377,0.00001779526,0.00005370082,0.00005270939,0.0006732157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005021533,"threshold_uncertainty_score":0.02655667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006042849277933837,"score_gpt":0.2220956951390751,"score_spread":0.2160528458611412,"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."}}