{"id":"W2044612972","doi":"10.1016/j.procir.2014.01.072","title":"Robust Metaheuristics for Scheduling Cellular Flowshop with Family Sequence-Dependent Setup Times","year":2014,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Metaheuristic; Particle swarm optimization; Mathematical optimization; Flow shop scheduling; Job shop scheduling; Computer science; Minification; Scheduling (production processes); Robustness (evolution); Profitability index; Algorithm; Mathematics; Schedule","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008411342,0.0008462955,0.0006993659,0.0007793668,0.0003040296,0.0006226104,0.0006286816,0.0008264087,0.0005917964],"category_scores_gemma":[0.002063013,0.0003786789,0.000774265,0.0007361177,0.0004269398,0.0004487639,0.0003809778,0.0006490716,0.00008901749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007721816,"about_ca_system_score_gemma":0.0009272228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003063895,"about_ca_topic_score_gemma":0.002341426,"domain_scores_codex":[0.9996762,0.0001357946,0.00001902838,0.00003838483,0.00008778201,0.00004272873],"domain_scores_gemma":[0.9993216,0.0003948396,0.0001609291,0.00003584923,0.00006084484,0.00002590158],"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":[0.00003037273,0.00002395156,0.0001481546,0.00002461848,0.00003315876,0.00001661644,0.000009843002,0.9890358,0.001389064,0.001656062,0.0001089617,0.007523311],"study_design_scores_gemma":[0.000008745027,0.00004367667,0.00008300557,0.000003471234,0.000007927621,0.000007137088,0.000005071598,0.9983456,0.0004750823,0.0008425256,0.0001749277,0.000002785841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1144916,0.001079532,0.8802252,0.0001831959,0.00007325897,0.0001077933,0.0000897437,0.0002844118,0.003465218],"genre_scores_gemma":[0.8427336,0.0004026438,0.1555572,0.00007621743,0.00002777074,0.0001774735,0.00009678715,0.00005056041,0.0008778418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003063895,"threshold_uncertainty_score":0.006092131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555913918458076,"score_gpt":0.214669913453017,"score_spread":0.1891107742684363,"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."}}