{"id":"W2790716556","doi":"10.1080/00207543.2018.1436789","title":"Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions","year":2018,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":268,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundo para o Desenvolvimento das Ciências e da Tecnologia; China Scholarship Council; National Natural Science Foundation of China","keywords":"Maintenance actions; Scheduling (production processes); Optimal maintenance; Reliability engineering; Computer science; Predictive maintenance; Preventive maintenance; Simulated annealing; Condition-based maintenance; Mathematical optimization; Component (thermodynamics); Corrective maintenance; Operations research; Engineering; Mathematics; Algorithm","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":[0.001382843,0.0008098937,0.001091817,0.0004933341,0.0003584888,0.0007699626,0.001624336,0.0007926694,0.001147696],"category_scores_gemma":[0.00276808,0.0005732892,0.0008649086,0.0005642145,0.0005873076,0.0007684559,0.0006239084,0.0006722808,0.0001279251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136925,"about_ca_system_score_gemma":0.001026165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007447129,"about_ca_topic_score_gemma":0.004459029,"domain_scores_codex":[0.9991973,0.0002030669,0.00004089683,0.0001499993,0.000213659,0.0001950902],"domain_scores_gemma":[0.9982326,0.0008410253,0.0004398355,0.0001311799,0.0002184986,0.0001368305],"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":[0.00004354444,0.00001708202,0.0003161402,0.00002473832,0.00001625206,0.00005371126,0.000017637,0.9933252,0.0009855917,0.001639052,0.00006845454,0.003492537],"study_design_scores_gemma":[0.00001188122,0.00004647288,0.0002797423,0.000001719055,0.00001033747,0.0000171559,0.000005375887,0.9983468,0.0002146087,0.0009897705,0.00007352794,0.00000273877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2228209,0.0004299874,0.7738059,0.000175581,0.00003723206,0.0001215324,0.0001470505,0.0001930724,0.002268794],"genre_scores_gemma":[0.9824378,0.0001028233,0.01659597,0.00001716641,0.00001203056,0.00005162158,0.00006499352,0.00001552411,0.0007019421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007447129,"threshold_uncertainty_score":0.01480758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07131587596696437,"score_gpt":0.3766434791121299,"score_spread":0.3053276031451655,"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."}}