{"id":"W2593859287","doi":"","title":"Empirical improvements of a dynamic scheduling engine in an industrial environment","year":2016,"lang":"en","type":"other","venue":"Espace ÉTS (ETS)","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut de Technologie Agroalimentaire","funders":"Mitacs","keywords":"Computer science; Flow shop scheduling; Dynamic priority scheduling; Metaheuristic; Scheduling (production processes); Scalability; Job shop scheduling; Distributed computing; Mathematical optimization; Schedule; Industrial engineering; Engineering; Algorithm; Database","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001523301,0.0003172329,0.0003949241,0.0004212322,0.00001264627,0.00001793106,0.0002125896,0.0005075878,0.001753692],"category_scores_gemma":[0.0000283009,0.0002959152,0.00006390848,0.0001355484,0.00003741129,0.00005562315,0.00004635736,0.0003434066,0.0001618797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001765657,"about_ca_system_score_gemma":0.00003925591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001716753,"about_ca_topic_score_gemma":0.00004291375,"domain_scores_codex":[0.9987521,0.00003962192,0.0003150668,0.0003197601,0.0002618816,0.0003115912],"domain_scores_gemma":[0.9993472,0.00002935158,0.0001093848,0.0003843369,0.000008762512,0.0001209633],"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":[0.0002338952,0.001186571,0.007995442,0.0007630525,0.001079009,0.0001174934,0.002374523,0.7056915,0.006613092,0.00009146446,0.03317279,0.2406812],"study_design_scores_gemma":[0.009093076,0.0003256871,0.0006468836,0.001767136,0.0001385491,0.000005373262,0.0003323354,0.8781851,0.001661948,0.00003924625,0.1055232,0.002281416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1890011,0.01088479,0.5639113,0.00189924,0.01832324,0.008191678,0.001579263,0.006731466,0.199478],"genre_scores_gemma":[0.1375599,0.002813092,0.3528631,0.0001955839,0.003591866,0.0003505103,0.0006824444,0.004685846,0.4972577],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2977797,"threshold_uncertainty_score":0.9999493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01502749906781158,"score_gpt":0.2568156779947208,"score_spread":0.2417881789269092,"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."}}