{"id":"W1875558896","doi":"10.1016/j.apm.2015.08.013","title":"Considering human error in optimizing production and corrective and preventive maintenance policies for manufacturing systems","year":2015,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; École de Technologie Supérieure; Toronto Metropolitan University","funders":"","keywords":"Preventive maintenance; Corrective maintenance; Production (economics); Time horizon; Reliability engineering; Operations research; Computer science; Sensitivity (control systems); Work (physics); Control (management); Production manager; Production planning; Risk analysis (engineering); Mathematical optimization; Engineering; Economics; Business; Mathematics; Microeconomics","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":[0.003200412,0.001345801,0.001573221,0.00120972,0.0006657264,0.002318464,0.001508788,0.002442286,0.001761476],"category_scores_gemma":[0.01635499,0.0009442447,0.0008915548,0.001125107,0.001448448,0.002124561,0.001476953,0.001188767,0.0001302589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002180225,"about_ca_system_score_gemma":0.003137471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03778761,"about_ca_topic_score_gemma":0.0143903,"domain_scores_codex":[0.998121,0.0009326277,0.00007467271,0.0002347438,0.0003225017,0.0003144792],"domain_scores_gemma":[0.9878744,0.009711125,0.001070373,0.0002330879,0.0008159887,0.0002949529],"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":[0.00002197848,0.00001179101,0.0002729618,0.00001633791,0.00001372998,0.00003607126,0.00002394047,0.9965137,0.00006893103,0.001731849,0.00006329587,0.001225401],"study_design_scores_gemma":[0.000003638147,0.00001425338,0.0001611729,0.000004587506,0.00001007757,0.000007388324,0.00001832117,0.9976819,0.00007260808,0.001971908,0.00004933097,0.000004866262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2823041,0.001107242,0.7080507,0.00183775,0.0002525079,0.0001048531,0.0001448653,0.0001370715,0.006060935],"genre_scores_gemma":[0.9853696,0.0002608601,0.01165523,0.0000534289,0.00006615785,0.00003473924,0.00002879987,0.00003090264,0.00250035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03778761,"threshold_uncertainty_score":0.07513535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0760200487860924,"score_gpt":0.2601110716541991,"score_spread":0.1840910228681066,"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."}}