{"id":"W2793964015","doi":"10.1109/tr.2017.2775958","title":"Joint Optimization of Jobs Sequence and Inspection Policy for a Single System With Two-Stage Failure Process","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Job shop scheduling; Sequence (biology); Process (computing); Preventive maintenance; Computer science; Reliability engineering; Optimization problem; Scheduling (production processes); Monte Carlo method; Schedule; Engineering; Mathematics; Statistics","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":[0.002514783,0.001455658,0.002149853,0.0008845844,0.0006299356,0.001286687,0.001418399,0.001561259,0.002488732],"category_scores_gemma":[0.004440135,0.001142996,0.001272073,0.0008730146,0.001105557,0.001300004,0.0009594644,0.001205537,0.0003298598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002217433,"about_ca_system_score_gemma":0.002833711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01044321,"about_ca_topic_score_gemma":0.005412442,"domain_scores_codex":[0.9988398,0.0003027488,0.00004948459,0.0002197578,0.000187273,0.0004009181],"domain_scores_gemma":[0.9969021,0.001693333,0.0005753172,0.0001550178,0.0003544383,0.0003199427],"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":[0.00006257111,0.00002612866,0.0001928172,0.00002468417,0.000009550863,0.00003247181,0.00001175672,0.9967875,0.0006737587,0.0006782631,0.00006445932,0.001436023],"study_design_scores_gemma":[0.00001143096,0.00004501726,0.000200155,0.000001998119,0.000008782567,0.00000869074,0.00000685942,0.9987632,0.0002815697,0.0006322058,0.0000345343,0.000005623437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3119743,0.0005659909,0.6814415,0.000622644,0.00005131183,0.0002568123,0.0002261168,0.0004991242,0.004362196],"genre_scores_gemma":[0.9575235,0.0002155106,0.03877084,0.00004208611,0.00001931705,0.0001789722,0.0001071523,0.00006772208,0.00307481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01044321,"threshold_uncertainty_score":0.02076483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01509929830814213,"score_gpt":0.2374629000868932,"score_spread":0.2223636017787511,"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."}}