{"id":"W2011446372","doi":"10.1016/j.apm.2014.12.022","title":"An improved multi-choice goal programming approach for supplier selection problems","year":2014,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Goal programming; Purchasing; Mathematical optimization; Selection (genetic algorithm); Computer science; Operations research; Interval (graph theory); Minification; Linear programming; Control (management); Mathematics; Operations management; Artificial intelligence; Economics","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":[0.0005507009,0.0003551947,0.0004433919,0.00009564203,0.000189341,0.0002036425,0.0002326739,0.0002321213,0.0000166831],"category_scores_gemma":[0.00004575716,0.0003289132,0.0001103092,0.0002160596,0.00005494143,0.0001955991,0.00002483783,0.0002698303,0.00001810711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005553278,"about_ca_system_score_gemma":0.000009368564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001830864,"about_ca_topic_score_gemma":7.224625e-7,"domain_scores_codex":[0.9981012,0.00001777982,0.000572021,0.00045504,0.0002087289,0.000645207],"domain_scores_gemma":[0.9990773,0.0002064682,0.00007199786,0.000313163,0.00007839998,0.0002527045],"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":[0.00000813857,0.0004197005,9.872211e-7,0.00145986,0.00003585189,2.208964e-8,0.000410716,0.9110433,0.002722657,0.0746286,0.00001135695,0.009258853],"study_design_scores_gemma":[0.0008182149,0.00005600851,1.515212e-7,0.00002417488,0.00006299471,0.000003056574,0.00008489952,0.98022,0.0008767326,0.0168569,0.0005864004,0.0004104631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000659286,0.00001270434,0.991511,0.000006269509,0.00003700076,0.002085214,0.00000205296,0.001237799,0.004448682],"genre_scores_gemma":[0.3467824,0.000001783363,0.6519138,0.00002121105,0.0001068112,0.0009879451,0.00004328649,0.0001054345,0.00003729669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3461232,"threshold_uncertainty_score":0.9999163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02383307217140284,"score_gpt":0.242820653460959,"score_spread":0.2189875812895561,"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."}}