{"id":"W4412749571","doi":"10.1016/j.cie.2025.111438","title":"Optimization of a bi-objective reliability redundancy allocation problem with heterogeneous components and strategy selection","year":2025,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Selection (genetic algorithm); Redundancy (engineering); Reliability engineering; Computer science; Reliability (semiconductor); Mathematical optimization; Engineering; Mathematics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0001467339,0.0001791194,0.0002342361,0.0001796418,0.00004679673,0.00004105117,0.00007842194,0.0001568032,0.00000222868],"category_scores_gemma":[0.00003564614,0.000183367,0.00003038473,0.0005048082,0.00003791603,0.0002071164,0.00002541016,0.0002027741,2.032257e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001751981,"about_ca_system_score_gemma":0.00004430718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004147866,"about_ca_topic_score_gemma":0.000002765574,"domain_scores_codex":[0.9991286,0.00002669416,0.0003221337,0.0002368704,0.0001047921,0.0001809063],"domain_scores_gemma":[0.9995691,0.00005832182,0.00005707674,0.0001398931,0.0001298805,0.00004572267],"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":[0.00004969088,0.0000254271,0.0002539633,0.0001389608,0.00005937921,4.17395e-7,0.00007678008,0.9935119,0.001668136,0.0001871759,0.00002525996,0.004002973],"study_design_scores_gemma":[0.0007698613,0.0001202129,0.0004956058,0.0002964213,0.00003055895,0.000005618532,0.0000125693,0.9932365,0.004765218,0.00003639523,0.00007634839,0.0001547127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2206932,0.00006368152,0.7780279,0.00003058921,0.0003060856,0.0005109329,0.000003240313,0.00024175,0.0001226071],"genre_scores_gemma":[0.974635,0.00004690421,0.02517665,0.000004299091,0.00004804442,0.0000317311,0.00002599283,0.00002173398,0.000009627446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7539418,"threshold_uncertainty_score":0.7477489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0093349960262908,"score_gpt":0.1908862625573089,"score_spread":0.1815512665310182,"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."}}