{"id":"W2757816263","doi":"","title":"Three meta-heuristics to solve the no-wait two-stage assembly flow shop scheduling problem","year":2013,"lang":"en","type":"article","venue":"Scientia Iranica","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Job shop scheduling; Flow shop scheduling; Mathematical optimization; Variable neighborhood search; Computer science; Heuristics; Scheduling (production processes); Differential evolution; Metaheuristic; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006509938,0.0002657311,0.0002870912,0.0001177407,0.0003198001,0.000526039,0.0006567172,0.00008141866,0.001182334],"category_scores_gemma":[0.0001755025,0.0001897929,0.0001860953,0.0007188901,0.0000679241,0.0002365263,0.0001347641,0.0003005079,0.002681701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004143289,"about_ca_system_score_gemma":0.00004330096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004297231,"about_ca_topic_score_gemma":0.0000731576,"domain_scores_codex":[0.9981719,0.00002158451,0.000383999,0.0003956904,0.0004793115,0.0005475392],"domain_scores_gemma":[0.9986485,0.000112158,0.00005197486,0.0006585092,0.0002920731,0.0002368102],"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":[0.000002254985,0.000024163,0.00001962513,0.00002647921,0.0002434471,0.000002273203,0.0002435982,0.9902472,0.001977415,0.001006522,0.004784196,0.001422805],"study_design_scores_gemma":[0.0002614289,0.00002193894,0.00005068278,0.00001621839,0.0001902496,0.000003084102,0.00006708165,0.9927746,0.0009150984,0.0003086567,0.005109793,0.0002811355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009987041,0.0004401206,0.9766311,0.001034795,0.001864801,0.0008282006,0.00002981192,0.0006826526,0.008501483],"genre_scores_gemma":[0.07491925,0.00001371244,0.9232695,0.0003966479,0.0002917566,0.0001435082,0.00001641981,0.00007775163,0.000871514],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0649322,"threshold_uncertainty_score":0.9997307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02666929109530004,"score_gpt":0.2426763820359148,"score_spread":0.2160070909406148,"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."}}