{"id":"W1143538317","doi":"10.1016/j.ijpe.2015.07.034","title":"Joint optimal lot sizing and preventive maintenance policy for a production facility subject to condition monitoring","year":2015,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Ontario Centres of Excellence","keywords":"Computer science; Production (economics); Preventive maintenance; Statistic; Covariate; Operations research; Sizing; Process (computing); Markov chain; Reliability engineering; Operations management; Statistics; Mathematics; Engineering; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002029187,0.001481215,0.00273596,0.001254425,0.0006298792,0.001780456,0.002059989,0.002371272,0.003154191],"category_scores_gemma":[0.003894908,0.001260192,0.0008944852,0.001129924,0.001036556,0.001407019,0.0008191207,0.00110846,0.0003964357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001662853,"about_ca_system_score_gemma":0.002699507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007501594,"about_ca_topic_score_gemma":0.00530281,"domain_scores_codex":[0.9990914,0.0002602189,0.00004346048,0.0002183615,0.0001552356,0.0002313913],"domain_scores_gemma":[0.9966416,0.001885316,0.0005959377,0.0001693618,0.000434148,0.0002736062],"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":[0.0004816252,0.0001166592,0.0005982444,0.0001199731,0.00004622918,0.00009345626,0.00002523823,0.984966,0.003128384,0.001877486,0.0006766647,0.007869971],"study_design_scores_gemma":[0.00003477221,0.0001011257,0.0008393416,0.000005808415,0.00002924843,0.00002159138,0.0000125009,0.9970891,0.0005626878,0.001221735,0.00007144116,0.0000106951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3851039,0.001107602,0.6025243,0.001428647,0.0001639248,0.0004200145,0.0009073448,0.001316538,0.007027751],"genre_scores_gemma":[0.9807351,0.000132827,0.01687095,0.00003817396,0.00004457603,0.0000834399,0.0001295,0.00003024562,0.001935252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007501594,"threshold_uncertainty_score":0.01491588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02576251183624556,"score_gpt":0.2663750514590995,"score_spread":0.2406125396228539,"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."}}