{"id":"W4323342275","doi":"10.5267/j.ijiec.2023.2.004","title":"Heuristics and metaheuristics to minimize makespan for flowshop with peak power consumption constraints","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province; Fundo para o Desenvolvimento das Ciências e da Tecnologia; National Natural Science Foundation of China","keywords":"Metaheuristic; Job shop scheduling; Mathematical optimization; Heuristics; Computer science; Simulated annealing; Iterated local search; Benchmark (surveying); Local search (optimization); Algorithm; Mathematics; Schedule","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008685101,0.001167571,0.0009030633,0.001412979,0.0005350523,0.0007779996,0.0009445493,0.0008592379,0.001079524],"category_scores_gemma":[0.001520748,0.0005590162,0.0009345097,0.001281876,0.0004286898,0.0007662979,0.0004465922,0.0007295604,0.0001268772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009956217,"about_ca_system_score_gemma":0.001817807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00462074,"about_ca_topic_score_gemma":0.004833769,"domain_scores_codex":[0.9996089,0.0001511895,0.00002147065,0.00005369372,0.00008954651,0.00007529736],"domain_scores_gemma":[0.999545,0.0002689911,0.00008006262,0.00002554666,0.00005250821,0.00002775562],"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":[0.00003742725,0.00005052223,0.0002530565,0.00007275027,0.00005439232,0.0000484055,0.00002696169,0.9698545,0.0009062674,0.005336013,0.0007443553,0.02261531],"study_design_scores_gemma":[0.00001737414,0.00004846847,0.0001247819,0.0000126446,0.00001890698,0.00002374223,0.00002366262,0.9950144,0.0005077559,0.003483265,0.000718777,0.000006236835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07285424,0.00197036,0.9165587,0.0002907638,0.0001498563,0.0002695177,0.0001475685,0.0004555919,0.00730322],"genre_scores_gemma":[0.6080621,0.001202991,0.3873398,0.0001680504,0.00008097977,0.0003560335,0.0002177658,0.0001106029,0.002461629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00462074,"threshold_uncertainty_score":0.009187698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03397247829670139,"score_gpt":0.2703464638553159,"score_spread":0.2363739855586145,"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."}}