{"id":"W173371885","doi":"","title":"Integrating product modularization and assembly line reconfiguration decisions: a genetic algorithm approach","year":2006,"lang":"en","type":"article","venue":"international conference on Modelling and simulation","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Control reconfiguration; Modular programming; Modular design; Computer science; Genetic algorithm; Product (mathematics); Mathematical optimization; Product line; Integer (computer science); Algorithm; Engineering; Mathematics; Manufacturing engineering; Embedded system; Programming language; Machine learning","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":[0.0001612398,0.0001836547,0.0001467801,0.0002020852,0.0001083845,0.0002040298,0.00007118536,0.00009305712,0.00000556255],"category_scores_gemma":[0.0000594053,0.0001866888,0.00002148412,0.0001147262,0.00001954185,0.0003168019,0.0000137982,0.0001477792,0.000002913883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005744549,"about_ca_system_score_gemma":0.00001703534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004744411,"about_ca_topic_score_gemma":0.000006149898,"domain_scores_codex":[0.9988936,0.00003259272,0.0003718438,0.0003221479,0.0002497172,0.0001300676],"domain_scores_gemma":[0.9993786,0.00008920734,0.00008733423,0.0001190512,0.0002873302,0.000038486],"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":[0.000009128077,0.0000219692,0.0001141608,0.000008550778,0.00001066138,4.842664e-7,0.00009459189,0.9524852,0.000774512,0.002932277,0.000005181286,0.04354328],"study_design_scores_gemma":[0.0002709772,0.00002928618,0.0005296668,0.00007903975,0.00001184868,0.000004922877,0.00003519951,0.9940622,0.0003867415,0.004365114,0.00003789526,0.0001870598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1138267,0.0000950487,0.8829467,0.00006102909,0.0001676408,0.0002113901,0.000009167722,0.0001638828,0.002518416],"genre_scores_gemma":[0.8420236,0.0001583415,0.157164,0.0000128142,0.0002192466,0.00001737294,0.00029424,0.00002488783,0.00008546082],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7281969,"threshold_uncertainty_score":0.7612947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03453306002744725,"score_gpt":0.2589592298276795,"score_spread":0.2244261698002323,"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."}}