{"id":"W2006725660","doi":"10.1016/j.eswa.2008.12.039","title":"Supplier selection: A hybrid model using DEA, decision tree and neural network","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":282,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Data envelopment analysis; Computer science; Purchasing; Artificial neural network; Selection (genetic algorithm); Decision tree; Supplier evaluation; Vendor; Operations research; Machine learning; Artificial intelligence; Decision tree model; Data mining; Supply chain management; Supply chain; Mathematical optimization; Business; Mathematics; Marketing","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":[0.002470421,0.000966699,0.002191865,0.001602475,0.0006764634,0.001962205,0.002243173,0.001766797,0.004026067],"category_scores_gemma":[0.003269206,0.0007586501,0.001029318,0.003349601,0.0004390744,0.002217651,0.0007380532,0.0009500531,0.0005292895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001613993,"about_ca_system_score_gemma":0.001656831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01288796,"about_ca_topic_score_gemma":0.01180036,"domain_scores_codex":[0.9987425,0.0006504288,0.00006302816,0.0001861694,0.0002615212,0.00009637787],"domain_scores_gemma":[0.9981336,0.001348363,0.000140996,0.00005909821,0.000261131,0.00005680758],"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":[0.00006759253,0.00007230097,0.0005299198,0.00004291943,0.00008299116,0.00004246157,0.00001774086,0.9800541,0.0001684792,0.004199262,0.0003705433,0.01435175],"study_design_scores_gemma":[0.000005951242,0.000009474137,0.00008490217,0.000002519691,0.00001120778,0.000007087009,0.000003020879,0.998546,0.00004834684,0.001195884,0.00008199576,0.00000355726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06710571,0.000478587,0.9242399,0.0004133,0.00006195585,0.0001472956,0.0002743647,0.0002346826,0.007044306],"genre_scores_gemma":[0.8252373,0.0005405396,0.1649849,0.0001535555,0.00008371286,0.0003544763,0.0003425501,0.00006392971,0.008238953],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01288796,"threshold_uncertainty_score":0.02562588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07092476119191075,"score_gpt":0.343568813380332,"score_spread":0.2726440521884212,"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."}}