{"id":"W2081075072","doi":"10.1177/1748006x15573166","title":"A multi-constrained maintenance scheduling optimization model for a hydrocarbon processing facility","year":2015,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Scheduling (production processes); Reliability engineering; Preventive maintenance; Computer science; Reliability (semiconductor); Flexibility (engineering); Predictive maintenance; Optimal maintenance; Mathematical optimization; Engineering; Operations research; Operations management","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":[],"consensus_categories":[],"category_scores_codex":[0.001901875,0.0001649651,0.0003954666,0.00007545116,0.00006755332,0.00001756754,0.0001978794,0.0001631734,4.590524e-7],"category_scores_gemma":[0.003412207,0.00011615,0.0001718741,0.0002264704,0.0002177858,0.0004092268,0.00003595047,0.0002968032,4.21844e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000119904,"about_ca_system_score_gemma":0.0001287951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000333477,"about_ca_topic_score_gemma":6.076044e-7,"domain_scores_codex":[0.9985386,0.00001204333,0.000831216,0.0001615803,0.0002750706,0.0001815163],"domain_scores_gemma":[0.9980537,0.00005529349,0.0004637733,0.00009416694,0.001202559,0.0001304857],"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":[0.0001625308,0.00007663284,0.0001273012,0.0004161007,0.00001753168,4.647906e-8,0.0004165656,0.996081,0.001267136,0.0006536948,0.00001813521,0.0007633237],"study_design_scores_gemma":[0.001281209,0.00009257402,0.00002073672,0.0003145989,0.00008752626,0.00001051371,0.0002980228,0.9911724,0.004478171,0.00208577,0.00004325289,0.0001151694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2089958,0.0001904832,0.7900963,0.00008876532,0.0002500535,0.000300022,0.00002359801,0.00002932545,0.00002562519],"genre_scores_gemma":[0.801948,0.000293062,0.1977071,0.000004290043,0.00002692483,0.000007502793,0.000001094532,0.00000807882,0.000003990715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5929521,"threshold_uncertainty_score":0.4736459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671111014212885,"score_gpt":0.2223405440632989,"score_spread":0.2056294339211701,"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."}}