{"id":"W1963807494","doi":"10.1016/j.compchemeng.2013.10.019","title":"Robust decision making for hybrid process supply chain systems via model predictive control","year":2013,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model predictive control; Supply chain; Process (computing); Computer science; Supply chain management; Mathematical optimization; Reduction (mathematics); Process control; Control (management); Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003251791,0.001369213,0.001956394,0.0008146426,0.0008134406,0.002624128,0.001273561,0.001570841,0.002355396],"category_scores_gemma":[0.006912088,0.001107216,0.001004227,0.0006660901,0.001759688,0.001755468,0.001814119,0.001800477,0.0003039397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259473,"about_ca_system_score_gemma":0.001677889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009147964,"about_ca_topic_score_gemma":0.005449619,"domain_scores_codex":[0.9983335,0.0005986739,0.00008534508,0.0003403726,0.000415726,0.0002264096],"domain_scores_gemma":[0.996005,0.002803582,0.0004999696,0.0001835179,0.0004227927,0.00008514521],"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":[0.00007451731,0.00002116668,0.00007403202,0.00002908649,0.00002974795,0.00002753793,0.00002530912,0.9889798,0.0004841552,0.00350098,0.0001482605,0.006605322],"study_design_scores_gemma":[0.000007980659,0.00001503337,0.00002881874,0.000001978751,0.000004139205,0.000001664001,0.000002218211,0.9976953,0.0001348874,0.002073408,0.00003122245,0.000003297618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03975741,0.0003403594,0.9558145,0.0004100739,0.000064516,0.00006424989,0.00007636713,0.0003419728,0.003130555],"genre_scores_gemma":[0.9746795,0.00009672031,0.02364422,0.0000537146,0.00003881705,0.00008719422,0.00005652546,0.0000319913,0.001311404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009147964,"threshold_uncertainty_score":0.01818943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005202702110665376,"score_gpt":0.185495578137203,"score_spread":0.1802928760265377,"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."}}