{"id":"W24732036","doi":"","title":"A hybrid algorithm with diversification and intensification for permutation flow shop scheduling","year":2008,"lang":"en","type":"article","venue":"international conference on Modelling and simulation","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Ottawa","funders":"","keywords":"Computer science; Tabu search; Mathematical optimization; Flow shop scheduling; Population; Adaptive memory; Algorithm; Simulated annealing; Evolutionary algorithm; Parallel computing; Metaheuristic; Benchmark (surveying); Scheduling (production processes); Job shop scheduling; Mathematics; Artificial intelligence","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":[0.001536576,0.0008410958,0.0009496621,0.0007594302,0.0004288375,0.0005726211,0.001437739,0.0008513108,0.003040741],"category_scores_gemma":[0.00151194,0.0004972756,0.0005982658,0.0007086776,0.0005148869,0.0007712962,0.0010137,0.0007548357,0.0005010663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006374636,"about_ca_system_score_gemma":0.001083625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002340747,"about_ca_topic_score_gemma":0.002453084,"domain_scores_codex":[0.9996333,0.0001570939,0.00001923526,0.00005675101,0.0000723838,0.00006128055],"domain_scores_gemma":[0.9994504,0.0003297212,0.00004542312,0.00005218346,0.00007496316,0.000047344],"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":[0.0004373608,0.0001481201,0.0009909935,0.00009695648,0.0000755664,0.00005555882,0.00007037822,0.8118002,0.003307178,0.007328319,0.001559328,0.1741299],"study_design_scores_gemma":[0.00005011441,0.00006688052,0.00005676963,0.000003344354,0.000007263152,0.00001031164,0.000004451111,0.9973717,0.0002900653,0.001667224,0.0004681445,0.000003644613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0452086,0.0002697457,0.9506389,0.0001423413,0.0000502913,0.0001501027,0.00005960746,0.001003467,0.00247692],"genre_scores_gemma":[0.378099,0.0001263487,0.618173,0.0001809879,0.00005435861,0.0005098983,0.0002257234,0.0001806114,0.002450095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003040741,"threshold_uncertainty_score":0.01017225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06145035858737097,"score_gpt":0.2644279912094806,"score_spread":0.2029776326221096,"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."}}