{"id":"W2559026348","doi":"10.1186/s40064-016-3756-2","title":"Multilevel hybrid method for optimal buffer sizing and inspection stations positioning","year":2016,"lang":"en","type":"article","venue":"SpringerPlus","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Sizing; Computer science; Tabu search; Heuristic; Genetic algorithm; Production line; Mathematical optimization; Fraction (chemistry); Quality (philosophy); Algorithm; Machine learning; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0006960807,0.0005689022,0.000870793,0.001066955,0.0004347938,0.0007134281,0.001221019,0.0009075078,0.004749352],"category_scores_gemma":[0.001544903,0.0005154477,0.0008320893,0.0007797616,0.0004465528,0.0006137783,0.0009059531,0.0007551364,0.0006540334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019121,"about_ca_system_score_gemma":0.001299203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00580949,"about_ca_topic_score_gemma":0.006719943,"domain_scores_codex":[0.999598,0.0001283956,0.00001357601,0.00004975553,0.0001639661,0.0000463186],"domain_scores_gemma":[0.9994546,0.0003542696,0.00005427382,0.00003089552,0.00008303875,0.00002290287],"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":[0.00009580282,0.00004230989,0.0003697163,0.00009889861,0.00005026671,0.00003688987,0.00005625633,0.8920767,0.005246966,0.01078242,0.001228815,0.08991496],"study_design_scores_gemma":[0.0000110837,0.00002010904,0.00004406294,0.000003844978,0.000003840477,0.000005404466,0.000003763747,0.9980522,0.0002933055,0.001167865,0.0003909345,0.000003706463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005281918,0.0001075274,0.9923603,0.00006304985,0.00001816175,0.00003040118,0.00003634403,0.0002737466,0.001828565],"genre_scores_gemma":[0.2381748,0.0001391282,0.7581053,0.00009941657,0.00002860716,0.0003742035,0.0001158954,0.0001319429,0.002830701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00580949,"threshold_uncertainty_score":0.01588821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007181734968708085,"score_gpt":0.2459005175216817,"score_spread":0.2387187825529736,"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."}}