{"id":"W1968975376","doi":"10.1007/s10845-013-0766-6","title":"Multi-objective modeling for preventive maintenance scheduling in a multiple production line","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"University of Tehran","keywords":"Downtime; Preventive maintenance; Reliability engineering; Maintainability; Production line; Scheduling (production processes); Spare part; Production (economics); Reliability (semiconductor); Engineering; Predictive maintenance; Computer science; 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":[0.001689444,0.001366622,0.001900141,0.001078721,0.0007672441,0.001929411,0.00226357,0.002002422,0.003355424],"category_scores_gemma":[0.002453732,0.001325917,0.001493862,0.001146901,0.0006885582,0.001177892,0.0008747906,0.001421536,0.0003294431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001684418,"about_ca_system_score_gemma":0.001435557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0289573,"about_ca_topic_score_gemma":0.01630782,"domain_scores_codex":[0.9993652,0.0002490394,0.00002948457,0.0001016808,0.0001332988,0.0001213782],"domain_scores_gemma":[0.998656,0.0008164028,0.0002213197,0.00004130746,0.0001693698,0.00009551823],"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":[0.000009342981,0.000008581347,0.00005208751,0.000007423159,0.000007808445,0.00001411779,0.000004653702,0.9990141,0.00007963725,0.0003332801,0.00002596684,0.0004429275],"study_design_scores_gemma":[0.000001907789,0.00000556606,0.00002473383,8.055614e-7,0.000002677842,0.000001163052,0.000001508435,0.9998232,0.0000180136,0.0001026568,0.00001675931,0.000001065045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1747935,0.0008946857,0.8129006,0.0005569061,0.0001334196,0.000158119,0.0004656945,0.0003173702,0.00977976],"genre_scores_gemma":[0.9554491,0.0003938065,0.03658545,0.00005880784,0.00004520113,0.0001755189,0.0001936601,0.00007611853,0.0070223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0289573,"threshold_uncertainty_score":0.05757749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02240486260363487,"score_gpt":0.2377450395923964,"score_spread":0.2153401769887616,"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."}}