{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009989896,0.0001360058,0.0001079994,0.0001512051,0.0001759298,0.00007551732,0.00005516901,0.00005483747,0.000008095626],"category_scores_gemma":[0.00002569635,0.0001343452,0.00002030433,0.00005341204,0.00004534209,0.000252418,0.000006765265,0.00009616596,0.000003130159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004169812,"about_ca_system_score_gemma":0.00001709857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004968083,"about_ca_topic_score_gemma":5.321121e-7,"domain_scores_codex":[0.9992663,0.00001187167,0.0001870397,0.000243269,0.0001849688,0.0001065398],"domain_scores_gemma":[0.9992887,0.00009249912,0.00005414664,0.00008224177,0.0004272045,0.00005518563],"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":[0.00004608696,0.00001197196,0.00007838443,0.00000936032,0.00002351815,7.946915e-7,0.0005168593,0.978038,0.00005659812,0.0009271079,0.000002311923,0.02028905],"study_design_scores_gemma":[0.0005745037,0.0000542105,0.0001018456,0.00004770892,0.00001493903,0.00001167597,0.0001867786,0.9977508,0.0002474031,0.0008129513,0.00003268859,0.0001645177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1966657,0.00003570038,0.8025546,0.0001356317,0.0001281584,0.0001626802,0.00002166879,0.0001235333,0.0001722911],"genre_scores_gemma":[0.7769533,0.0002086046,0.2224448,0.00002572019,0.00007254228,0.00002173183,0.0002136501,0.00001666884,0.00004298051],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5802875,"threshold_uncertainty_score":0.5478439,"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."}}