{"id":"W4285166142","doi":"10.5267/j.ijiec.2022.2.001","title":"Assembly line rebalancing and worker assignment considering ergonomic risks in an automotive parts manufacturing plant","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automotive industry; Constructive; Assembly line; Heuristic; Quality (philosophy); Benchmark (surveying); Manufacturing engineering; Engineering; Risk analysis (engineering); Work (physics); Computer science; Operations research; Industrial engineering; Process (computing); Business; Mechanical engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004509633,0.0001738854,0.0002531475,0.0005912926,0.00007186806,0.0001037342,0.000224353,0.00006508382,0.00001828545],"category_scores_gemma":[0.0001205527,0.0002062259,0.00005012558,0.0001345477,0.00001081403,0.0003692422,0.00009369487,0.000669986,7.386661e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006464595,"about_ca_system_score_gemma":0.00008389162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002465152,"about_ca_topic_score_gemma":0.00001061311,"domain_scores_codex":[0.9985762,0.0000562256,0.0006664649,0.0001444207,0.0003580665,0.0001986065],"domain_scores_gemma":[0.9993199,0.0001985667,0.0001996032,0.00007941091,0.00010016,0.000102383],"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":[0.00002538345,0.00003675398,0.001917669,0.000003299118,0.00009759251,0.00009253538,0.0004462596,0.9941362,0.0008596819,0.00004185422,0.00005439839,0.00228836],"study_design_scores_gemma":[0.001420965,0.000102287,0.008908982,0.0001509163,0.00002124187,0.0002599868,0.0003039399,0.9866314,0.001595483,0.00003972464,0.0003500232,0.0002150715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9052191,0.00008560642,0.09188359,0.0001627639,0.002358425,0.0001321658,0.00003335804,0.00009690382,0.00002811662],"genre_scores_gemma":[0.993924,0.00001774884,0.005432006,0.00002460009,0.000496153,0.00001222625,0.00005270637,0.00003832004,0.000002285454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08870488,"threshold_uncertainty_score":0.840965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03440726592990508,"score_gpt":0.2632480629523334,"score_spread":0.2288407970224283,"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."}}