{"id":"W4393121446","doi":"10.5267/j.ijiec.2024.2.002","title":"A case study of whale optimization algorithm for scheduling in C2M model","year":2024,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Whale; Scheduling (production processes); Optimization algorithm; Computer science; Mathematical optimization; Job shop scheduling; Algorithm; Business; Mathematics; Fishery; Computer network; Biology","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":[0.0009196478,0.000847586,0.0008610668,0.0006361376,0.001508627,0.001334158,0.001267115,0.002426914,0.008206658],"category_scores_gemma":[0.001641806,0.0003596842,0.0009895293,0.00108085,0.0006564726,0.001015996,0.001048078,0.001061023,0.0004399891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001220576,"about_ca_system_score_gemma":0.001442438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03134029,"about_ca_topic_score_gemma":0.02176129,"domain_scores_codex":[0.9995311,0.0001625299,0.00002337969,0.00008853293,0.00007899373,0.0001155066],"domain_scores_gemma":[0.9992203,0.000472523,0.00005717052,0.00005011922,0.0001032003,0.0000967364],"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":[0.00008630954,0.00006231054,0.0008195897,0.00007757151,0.00001779845,0.0005819126,0.00007046483,0.9811301,0.0004398664,0.009222231,0.001256766,0.006235058],"study_design_scores_gemma":[0.00001517064,0.00002488251,0.0001519388,0.000003864768,0.000004770936,0.00003752342,0.00004494427,0.9971629,0.0001480743,0.001499731,0.0009015726,0.000004752676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4021974,0.001562044,0.5329995,0.002423026,0.0002808263,0.0004712096,0.000825552,0.0007250212,0.05851548],"genre_scores_gemma":[0.9173449,0.0003181771,0.07087396,0.00009224874,0.0000333407,0.00019444,0.0002822951,0.00007126418,0.01078935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03134029,"threshold_uncertainty_score":0.06231576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04080842903519794,"score_gpt":0.2904687834622092,"score_spread":0.2496603544270112,"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."}}