{"id":"W2606713714","doi":"10.1016/j.cie.2017.04.019","title":"Improving configuration of complex production lines via simulation-based optimization","year":2017,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Simulated annealing; Production line; Mathematical optimization; Ant colony optimization algorithms; Computer science; Automotive industry; Production (economics); Computation; Simulation-based optimization; Industrial engineering; Operations research; Engineering; Algorithm; Mathematics","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":[0.000173958,0.0002144715,0.0002670518,0.0001884548,0.0001624612,0.0001151081,0.0002315494,0.0001735264,0.00001000537],"category_scores_gemma":[0.0005122252,0.0002573885,0.00006024816,0.0001401184,0.00003537969,0.0004557132,0.00003139786,0.0001783672,0.000002131846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001048764,"about_ca_system_score_gemma":0.00003593217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002256533,"about_ca_topic_score_gemma":0.000001365431,"domain_scores_codex":[0.9989349,0.00001478316,0.0004446429,0.0002150342,0.0001878813,0.0002027434],"domain_scores_gemma":[0.9989812,0.0001107054,0.0002396217,0.0004339696,0.0001743719,0.00006008554],"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":[0.00001065913,0.00001130653,0.0001607417,0.00006521369,0.00002216152,6.140284e-7,0.00003113566,0.9725199,0.01579767,0.00001625233,0.00004418347,0.01132019],"study_design_scores_gemma":[0.0006453295,0.00002801485,0.0004336424,0.0001067921,0.00002473462,7.433275e-7,0.000003204366,0.978702,0.0197112,0.000001238375,0.0001167675,0.0002263228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01835164,0.00001457297,0.9783417,0.00006015924,0.00237303,0.0003622338,0.000004421137,0.0004512117,0.00004101651],"genre_scores_gemma":[0.9574156,0.000001913254,0.04146339,0.00000496411,0.0009433944,0.00001093032,0.0001001425,0.00005354859,0.0000061226],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.939064,"threshold_uncertainty_score":0.9999878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0311103759646734,"score_gpt":0.2369655150424987,"score_spread":0.2058551390778253,"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."}}