{"id":"W2398526858","doi":"10.1016/j.eswa.2016.05.027","title":"A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":116,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Adaptive neuro fuzzy inference system; Artificial neural network; Data mining; Machine learning; Artificial intelligence; Neuro-fuzzy; Fuzzy logic; Selection (genetic algorithm); Parametric statistics; Supplier evaluation; Perceptron; Process (computing); Sensitivity (control systems); Supply chain management; Supply chain; Fuzzy control system; 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":[0.0009178608,0.0006480088,0.001361127,0.00103314,0.0007263139,0.001389234,0.001753607,0.001929917,0.003119182],"category_scores_gemma":[0.002134126,0.0006431065,0.0009723345,0.001356247,0.0004758824,0.001434337,0.0007747761,0.0007967557,0.0004849119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096546,"about_ca_system_score_gemma":0.001266645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01708251,"about_ca_topic_score_gemma":0.009072948,"domain_scores_codex":[0.9995962,0.0001547211,0.00002279837,0.00006800354,0.0001185104,0.00003979559],"domain_scores_gemma":[0.9992687,0.0004701684,0.00005915514,0.00004167319,0.0001311486,0.00002903409],"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":[0.00002957979,0.00002428767,0.0001668986,0.00001477398,0.00001607552,0.00002921006,0.00001432564,0.9913071,0.0002143799,0.00202764,0.0001479259,0.00600784],"study_design_scores_gemma":[0.000002577986,0.000004557905,0.00001910834,0.000001169692,0.000002880812,0.000002593815,0.000001125695,0.9995075,0.00004947179,0.000359284,0.00004808901,0.000001747696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04501506,0.0002846638,0.945351,0.0002053538,0.00006794116,0.00007358244,0.0001478234,0.0006230254,0.008231509],"genre_scores_gemma":[0.9137836,0.0002545751,0.0819227,0.00008104115,0.00003333941,0.0002278687,0.0001662267,0.00005407743,0.003476681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01708251,"threshold_uncertainty_score":0.03396618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1372853844337297,"score_gpt":0.4290438737337837,"score_spread":0.291758489300054,"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."}}