{"id":"W3090050045","doi":"10.1109/aparm49247.2020.9209399","title":"Joint optimization of maintenance and production scheduling for unrelated parallel-machine system","year":2020,"lang":"en","type":"article","venue":"2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM)","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Preventive maintenance; Scheduling (production processes); Computer science; Simulated annealing; Mathematical optimization; Job shop scheduling; Reliability engineering; Optimal maintenance; Production (economics); Engineering; Mathematics; Algorithm; Schedule","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004537642,0.0003470478,0.0004630653,0.00008391535,0.0001553625,0.00005908906,0.0001746656,0.0001671581,0.000006552078],"category_scores_gemma":[0.0005863738,0.0003308538,0.0001143215,0.0002452027,0.0001040143,0.0003074428,0.00005199046,0.0003483857,0.000002753425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001816536,"about_ca_system_score_gemma":0.00003020653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000705914,"about_ca_topic_score_gemma":8.548789e-7,"domain_scores_codex":[0.9977718,0.00004644678,0.000815702,0.0007331062,0.0003041493,0.0003287719],"domain_scores_gemma":[0.9986295,0.00007521267,0.0002046432,0.0002509032,0.000657954,0.0001818092],"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":[0.0004436577,0.00004249704,0.00002300404,0.0003994734,0.00006543966,0.000001752195,0.0004386843,0.993343,0.00238763,0.001852254,0.00005645884,0.0009461556],"study_design_scores_gemma":[0.00137417,0.0001469009,0.000004641908,0.0004405219,0.00003783803,0.00001882988,0.0009720364,0.9945995,0.001518132,0.0002863243,0.0002738494,0.000327225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0185965,0.0003525226,0.9701176,0.007349088,0.001274823,0.0008612932,0.00009383886,0.0004327061,0.0009215906],"genre_scores_gemma":[0.7959534,0.001441921,0.2020071,0.00007333062,0.0001616347,0.00009975202,0.0001035228,0.00005785242,0.0001015205],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7773569,"threshold_uncertainty_score":0.9999143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01085399439751257,"score_gpt":0.2133707667311316,"score_spread":0.202516772333619,"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."}}