{"id":"W4388662555","doi":"10.1016/j.cie.2023.109757","title":"A robust-fuzzy multi-objective optimization approach for a supplier selection and order allocation problem: Improving sustainability under uncertainty","year":2023,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematical optimization; Fuzzy logic; Credibility; Operations research; Computer science; Robust optimization; Sustainability; Goal programming; Selection (genetic algorithm); Sensitivity (control systems); Credibility theory; Total cost; Engineering; Mathematics; Economics; Microeconomics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003415431,0.0003379469,0.0004588767,0.0008777143,0.0003197744,0.0006814541,0.0004319678,0.0003440591,0.000008046692],"category_scores_gemma":[0.00469088,0.000314356,0.0001126703,0.00271784,0.00005030892,0.0006301788,0.0003157357,0.0003714065,0.000002106714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005362398,"about_ca_system_score_gemma":0.0002640111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001047419,"about_ca_topic_score_gemma":0.000006737042,"domain_scores_codex":[0.9966048,0.0001704707,0.0008508809,0.001096108,0.000708103,0.0005696326],"domain_scores_gemma":[0.9965702,0.001428569,0.0002720846,0.0004000248,0.001160761,0.0001683575],"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":[0.0000823036,0.00004815616,0.0001388926,0.00003373859,0.00002925201,6.1208e-7,0.0005021738,0.9691797,0.0003913525,0.0002961752,0.0002880359,0.02900958],"study_design_scores_gemma":[0.00210663,0.00007299817,0.0003728022,0.00002379725,0.0000239416,0.000008302978,0.0009304907,0.9955723,0.00005391805,0.0003159989,0.0001875297,0.0003312613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02671964,0.00001358843,0.9700581,0.0001819523,0.0006745888,0.001967755,0.00001317434,0.0003563211,0.00001483411],"genre_scores_gemma":[0.4452319,0.000001885122,0.5537959,0.00003140668,0.000412622,0.0002789521,0.00008015147,0.0000554736,0.0001117907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4185122,"threshold_uncertainty_score":0.9999309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1269772613815963,"score_gpt":0.3329300269842453,"score_spread":0.205952765602649,"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."}}