{"id":"W4385621628","doi":"10.1016/j.eswa.2023.121121","title":"A supplier selection & order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Quality and Supply Management","field":"Business, Management and Accounting","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cape Breton University; Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Principal component analysis; Component (thermodynamics); Supply chain; Process (computing); Selection (genetic algorithm); Artificial neural network; Mathematical optimization; Supply chain management; Artificial intelligence; Operations research; Data mining; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004725046,0.0001964425,0.0002343851,0.0005220094,0.0006058881,0.0004609404,0.0001398249,0.00009359014,0.00007240283],"category_scores_gemma":[0.00003768063,0.000176429,0.00003515409,0.002608229,0.00004480248,0.0004175915,0.0000847648,0.0001926991,0.00004125356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004978898,"about_ca_system_score_gemma":0.000009059675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001449217,"about_ca_topic_score_gemma":0.0001057693,"domain_scores_codex":[0.9986468,0.00003885289,0.0003575713,0.0004221949,0.0002910346,0.0002435745],"domain_scores_gemma":[0.9992021,0.00007333708,0.000268755,0.000226616,0.0002057645,0.0000233708],"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":[0.00007010622,0.0003117144,0.1108494,0.0005648698,0.001038021,0.000001551801,0.003212371,0.7557824,0.001339155,0.1064005,0.01085468,0.00957527],"study_design_scores_gemma":[0.0001784668,0.00001334838,0.001156098,0.00009524593,0.0001838036,0.000001552159,0.002994503,0.6847332,0.00004619782,0.000190039,0.310017,0.0003904832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003698936,0.0001396529,0.9923073,0.0005259726,0.00003523921,0.001024277,0.000002101567,0.0006915742,0.001574926],"genre_scores_gemma":[0.9524634,0.0000492539,0.03918547,0.0003408209,0.0006412462,0.004301491,0.001977813,0.00005634236,0.0009841722],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9531218,"threshold_uncertainty_score":0.7194565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01482272298969247,"score_gpt":0.274710680714615,"score_spread":0.2598879577249226,"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."}}