{"id":"W1986243952","doi":"10.1007/s00170-011-3798-0","title":"Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem","year":2011,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; University of Guelph","funders":"","keywords":"Job shop scheduling; Computer science; Scheduling (production processes); Flow shop scheduling; Mathematical optimization; Genetic algorithm; Heuristic; Distributed computing; Algorithm; Artificial intelligence; Schedule; Mathematics; Machine learning","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":[0.0001342232,0.0001362761,0.0001778125,0.0002374776,0.00004956583,0.00002541265,0.0004858332,0.00006303284,0.00001518208],"category_scores_gemma":[0.00003345102,0.000104252,0.0000562958,0.00003728247,0.00007051599,0.0001267044,0.00007129425,0.0002250451,0.000002460818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005261214,"about_ca_system_score_gemma":0.00001742173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.115025e-7,"about_ca_topic_score_gemma":4.550585e-7,"domain_scores_codex":[0.9991756,0.000004827007,0.0003625577,0.0001112671,0.0001683536,0.0001773888],"domain_scores_gemma":[0.9995033,0.00005697444,0.0001289999,0.0001369549,0.0001307569,0.00004302362],"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":[0.00003012111,0.00003109177,0.000004221352,0.00001582578,0.0001418393,0.00001572908,0.0001740702,0.738097,0.0006167014,0.00110121,0.00003279698,0.2597394],"study_design_scores_gemma":[0.0006714092,0.00006760743,0.00002537795,0.00006016511,0.00002722392,0.0005864174,0.0001600124,0.7831067,0.0961924,0.1188906,0.00008711062,0.0001248703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.162189,0.0002464344,0.8366801,0.0002410987,0.0002079126,0.0001206805,0.000006588147,0.0001438683,0.00016429],"genre_scores_gemma":[0.1773343,0.0002073546,0.822254,0.00003126214,0.0000677328,0.00001694846,0.000001252866,0.00002667771,0.00006048338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2596145,"threshold_uncertainty_score":0.4251271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01496866569046227,"score_gpt":0.2352950972755173,"score_spread":0.220326431585055,"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."}}