{"id":"W2099507590","doi":"10.5267/j.ijiec.2014.11.002","title":"A De Novo programming approach for a robust closed-loop supply chain network design under uncertainty: An M/M/1 queueing model","year":2015,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Queueing theory; Mathematical optimization; Computer science; Programming paradigm; Nonlinear programming; Linear programming; Supply chain network; Robust optimization; Fuzzy logic; Layered queueing network; Integer programming; Supply chain; Operations research; Nonlinear system; Supply chain management; Mathematics; Computer network; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002085449,0.001604423,0.001528469,0.0008110401,0.0005453879,0.001941566,0.001633724,0.001963589,0.002484791],"category_scores_gemma":[0.003193662,0.000931514,0.001342217,0.0007730828,0.001183874,0.001371671,0.001415933,0.002072967,0.0002207749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001801608,"about_ca_system_score_gemma":0.001952117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00850777,"about_ca_topic_score_gemma":0.004212204,"domain_scores_codex":[0.999233,0.0002971426,0.00002832015,0.0001945151,0.0001577292,0.00008930181],"domain_scores_gemma":[0.9985546,0.0009563573,0.0001895263,0.00004076286,0.0001965814,0.0000621752],"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":[0.0000112926,0.00000925856,0.00005244896,0.00002013555,0.000009650285,0.00003147815,0.00001427504,0.9905549,0.0003399172,0.007042688,0.00007090553,0.001843041],"study_design_scores_gemma":[0.000002253531,0.000009747383,0.00001226306,0.00000244417,0.000003106639,0.000002857683,0.000002605326,0.9980313,0.00009960229,0.001717423,0.0001137392,0.000002650403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005399006,0.0001413186,0.9922971,0.0001124619,0.00001744423,0.00003319084,0.00003601633,0.00004869905,0.001914766],"genre_scores_gemma":[0.7422501,0.0006432629,0.2491287,0.0001496613,0.00007926085,0.0004043264,0.0001874589,0.0000840935,0.007073176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00850777,"threshold_uncertainty_score":0.01691645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.081387449982893,"score_gpt":0.2687711344862456,"score_spread":0.1873836845033526,"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."}}