{"id":"W2395424964","doi":"10.1080/03155986.2006.11732738","title":"OPtimally Balancing Large Assembly Lines: Updating Johnson S 1988 Fable Algorithm<sup>*</sup>","year":2006,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Fable; Computer science; Algorithm; Heuristic; Task (project management); Process (computing); Range (aeronautics); Selection (genetic algorithm); Verifiable secret sharing; Mathematical optimization; Mathematics; Artificial intelligence; Programming language; Engineering; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001624034,0.001190569,0.001210548,0.001588608,0.001031495,0.001772644,0.002667981,0.001709309,0.006653474],"category_scores_gemma":[0.005709536,0.0008016885,0.0006819372,0.002273713,0.0009870332,0.003072893,0.001426043,0.001805808,0.002144182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002288253,"about_ca_system_score_gemma":0.002791974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01909436,"about_ca_topic_score_gemma":0.02218528,"domain_scores_codex":[0.9988476,0.0002812982,0.00005847634,0.0002146496,0.0003774273,0.0002205016],"domain_scores_gemma":[0.9985628,0.0006904743,0.0001284018,0.0001863843,0.0003485917,0.00008346525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003694572,0.0002687396,0.001419575,0.0001372318,0.00003789958,0.0001299617,0.0001773164,0.3171024,0.003583678,0.0241695,0.02650964,0.6260945],"study_design_scores_gemma":[0.0001812356,0.0001717517,0.0005581966,0.00006568879,0.00005007081,0.000146187,0.0001338248,0.9035701,0.004163445,0.03256874,0.05834059,0.00005026419],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05416865,0.003735117,0.9022244,0.001881372,0.0005582809,0.0002247111,0.0002571038,0.004634788,0.03231556],"genre_scores_gemma":[0.150088,0.00136911,0.8360164,0.0008237866,0.0002311573,0.0001706781,0.0004873323,0.000731483,0.01008193],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01909436,"threshold_uncertainty_score":0.03796643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01484369503608432,"score_gpt":0.2775481892404374,"score_spread":0.262704494204353,"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."}}