{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006957133,0.001060426,0.00117624,0.0006614642,0.0005055156,0.001328774,0.001736858,0.001948029,0.003660286],"category_scores_gemma":[0.0009683846,0.0006995228,0.0008169323,0.001047774,0.000561759,0.0008373827,0.000744738,0.001229974,0.0004092503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001495234,"about_ca_system_score_gemma":0.00197442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01755082,"about_ca_topic_score_gemma":0.009686925,"domain_scores_codex":[0.9995129,0.0001423234,0.00002160357,0.0001145861,0.0001131356,0.00009546927],"domain_scores_gemma":[0.999594,0.0001848032,0.00009314814,0.00001520182,0.00007851741,0.00003429432],"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":[0.00001784939,0.00001573005,0.00009624392,0.0000293571,0.000008566713,0.00004914983,0.00001343165,0.9952649,0.0004379618,0.002282778,0.0002008039,0.00158331],"study_design_scores_gemma":[0.000007076029,0.00001695231,0.00008323502,0.000002595981,0.000006432324,0.000008401927,0.000005468797,0.9990882,0.0000757253,0.0004826128,0.000220112,0.000003191202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06403396,0.0008433196,0.9175506,0.0006479943,0.00008965663,0.0001563192,0.0007714787,0.000346608,0.0155601],"genre_scores_gemma":[0.9145668,0.0008861177,0.06570835,0.0001068736,0.00005838536,0.000524891,0.0005575336,0.00008872632,0.0175023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01755082,"threshold_uncertainty_score":0.03489733,"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."}}