{"id":"W2056050109","doi":"10.1016/j.compchemeng.2009.06.029","title":"Robust supply chain performance via Model Predictive Control","year":2009,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"McMaster University","keywords":"Model predictive control; Supply chain; Supply chain optimization; Optimization problem; Control theory (sociology); Mathematical optimization; Robust optimization; Robustness (evolution); Computer science; Closed loop; Engineering; Control engineering; Control (management); Supply chain management; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006082964,0.0002840274,0.0003151886,0.00009189748,0.00002850458,0.00002717546,0.0002232708,0.0001258813,0.000001849252],"category_scores_gemma":[0.00001506626,0.0003226189,0.00007269563,0.0001725317,0.00001294142,0.0002826296,0.00001843062,0.000267812,0.000007476438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000206811,"about_ca_system_score_gemma":0.00000775976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.542621e-7,"about_ca_topic_score_gemma":2.007548e-8,"domain_scores_codex":[0.9988495,0.00000459028,0.0003022988,0.0002497741,0.0001689463,0.0004248655],"domain_scores_gemma":[0.9994957,0.00004507235,0.00003079982,0.0002353941,0.000043737,0.0001492804],"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":[0.00001198318,0.000009360676,0.000008548818,0.00003631277,0.00002746383,0.000002332549,0.0000570679,0.9301888,0.06532329,0.00005791346,0.00008825668,0.004188695],"study_design_scores_gemma":[0.0008606538,0.00003096661,0.00007297685,0.00008791385,0.00001496585,0.00001343276,0.000001156292,0.9915588,0.006983429,0.00002481053,0.00003937812,0.0003114848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01922911,0.0001820807,0.9785803,0.0000435282,0.0002795348,0.0002571533,0.000006392113,0.001181494,0.0002403828],"genre_scores_gemma":[0.9519423,0.0000163032,0.04762352,0.00007002628,0.0002437019,0.00002875646,0.00001714264,0.00005026264,0.000007985509],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9327132,"threshold_uncertainty_score":0.9999226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003979777568447033,"score_gpt":0.1542873220899977,"score_spread":0.1503075445215507,"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."}}