{"id":"W2091706292","doi":"10.1080/00207540903055685","title":"Hybrid simulated annealing with memory: an evolution-based diversification approach","year":2009,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Ottawa","funders":"","keywords":"Simulated annealing; Metaheuristic; Computer science; Heuristics; Mathematical optimization; Job shop scheduling; Scheduling (production processes); Economic shortage; Job shop; Artificial intelligence; Flow shop scheduling; Algorithm; Mathematics","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":[0.001129052,0.00008814151,0.0001024223,0.0006680061,0.0001088686,0.0001114206,0.0003344563,0.00003923654,0.0000318319],"category_scores_gemma":[0.0002148679,0.00007767742,0.00003814745,0.0003494285,0.00005440997,0.0004773241,0.000007113855,0.000393865,0.000007815322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002983892,"about_ca_system_score_gemma":0.0001130123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008382414,"about_ca_topic_score_gemma":4.536506e-7,"domain_scores_codex":[0.9982367,0.00009982534,0.0002782614,0.0001602131,0.00105905,0.0001659665],"domain_scores_gemma":[0.9970645,0.00003650473,0.00009086413,0.0001549952,0.00255075,0.0001024104],"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":[0.0001362547,0.0001129715,0.000186379,0.000004586083,0.00004763839,0.000010937,0.0001586076,0.9916221,0.001573052,0.00008854299,0.0003200151,0.005738905],"study_design_scores_gemma":[0.0007436079,0.0002352517,0.001867911,0.00005072767,0.0000110644,0.0001128721,0.0004130593,0.984029,0.01194079,0.0002763059,0.0001973517,0.0001220432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6219645,0.0002168847,0.3730925,0.002374556,0.001198291,0.0002172801,0.000005836135,0.0001550812,0.0007751476],"genre_scores_gemma":[0.9656082,0.00001913652,0.03343612,0.00002061533,0.0007956618,0.00000137398,0.00003065546,0.00001390438,0.00007427351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3436438,"threshold_uncertainty_score":0.3167593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04930509127594975,"score_gpt":0.3286149233449412,"score_spread":0.2793098320689915,"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."}}