{"id":"W2396268331","doi":"10.1007/s10898-016-0438-0","title":"Robust optimization approximation for joint chance constrained optimization problem","year":2016,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Bounding overwatch; Robust optimization; Optimization problem; Mathematics; Convexity; Constrained optimization; Probabilistic logic; Constraint (computer-aided design); Continuous optimization; Computer science; Multi-swarm optimization; 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":[0.003125215,0.001375091,0.002315302,0.001058167,0.0003442618,0.001808033,0.001551254,0.001906078,0.003306006],"category_scores_gemma":[0.009608589,0.0007753408,0.001183684,0.001011945,0.001034535,0.001536761,0.001770547,0.002157204,0.0005402577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085229,"about_ca_system_score_gemma":0.001519002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005654855,"about_ca_topic_score_gemma":0.002642111,"domain_scores_codex":[0.9988121,0.0005683468,0.00004575642,0.000154265,0.0003072953,0.0001123251],"domain_scores_gemma":[0.9967598,0.00232843,0.0002684182,0.0001629082,0.0003723349,0.000108002],"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":[0.00005479317,0.00002738396,0.0001986682,0.00009022759,0.00005579602,0.00004441121,0.00002264322,0.9588389,0.0004855927,0.02804686,0.001151706,0.01098306],"study_design_scores_gemma":[0.000003217775,0.000006358543,0.00002090433,0.000004906529,0.000004310065,0.000005330449,0.000001533508,0.9960952,0.00004868191,0.003660671,0.0001467352,0.000002114821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005076363,0.0004443896,0.9918191,0.0002087358,0.00004215186,0.00001744016,0.00003924477,0.00008703661,0.00226543],"genre_scores_gemma":[0.5425694,0.001471047,0.4443279,0.0002997362,0.000291815,0.0003528171,0.0006331471,0.0003440159,0.009710141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005654855,"threshold_uncertainty_score":0.01652795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07599725735112603,"score_gpt":0.3229622976148248,"score_spread":0.2469650402636987,"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."}}