{"id":"W2320343174","doi":"10.1142/9789812702289_0039","title":"DESIGNING CELLULAR MANUFACTURING SYSTEMS: A GENETIC ALGORITHM APPROACH","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cellular manufacturing; Genetic algorithm; Algorithm; Engineering; Manufacturing engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000592691,0.0008759417,0.0007239046,0.0007923261,0.0005910709,0.0008760318,0.0009424026,0.001649309,0.002158232],"category_scores_gemma":[0.00146054,0.0006019544,0.0007316417,0.0008012853,0.0008296418,0.0006344347,0.0006390294,0.0006315946,0.0002804892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008063118,"about_ca_system_score_gemma":0.001129999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004752454,"about_ca_topic_score_gemma":0.004405585,"domain_scores_codex":[0.9997634,0.00008687229,0.00001006897,0.00003652797,0.00006708458,0.00003605448],"domain_scores_gemma":[0.9996457,0.0002350284,0.00003089271,0.00001711851,0.00005785074,0.00001349294],"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":[0.00001071228,0.00001438406,0.0001300135,0.00002677325,0.00001304249,0.00001979352,0.00002390721,0.9820794,0.0008192826,0.004467744,0.0001305095,0.01226446],"study_design_scores_gemma":[0.00001226575,0.00003114179,0.00003944917,0.000008000817,0.00001264986,0.00001091262,0.00001361535,0.9958836,0.0004406043,0.002985863,0.0005579609,0.000003916213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02236851,0.0002357514,0.9716656,0.0001365887,0.00003430865,0.00008929949,0.00001815887,0.0001510498,0.005300734],"genre_scores_gemma":[0.4646587,0.0006169362,0.5305563,0.0001325746,0.00003235079,0.0004154619,0.00006603854,0.00006700958,0.003454679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004752454,"threshold_uncertainty_score":0.009449601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009317230882727599,"score_gpt":0.1818570106555321,"score_spread":0.1725397797728045,"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."}}