{"id":"W2099583394","doi":"10.5267/j.msl.2013.06.022","title":"ATM cash management using genetic algorithm","year":2013,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Cash; Genetic algorithm; Algorithm; Cash management; Business; Finance; Machine learning","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":[0.0005559847,0.0005838614,0.0008190345,0.0008987354,0.0005061376,0.0009918379,0.001006123,0.001000219,0.002511936],"category_scores_gemma":[0.001199084,0.0003095212,0.0005092616,0.0008695659,0.0003712292,0.0005272879,0.0003999752,0.0005281135,0.0002329545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077568,"about_ca_system_score_gemma":0.001411479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01380692,"about_ca_topic_score_gemma":0.007096815,"domain_scores_codex":[0.999761,0.00007754518,0.00001098852,0.00005525144,0.00004366757,0.00005160295],"domain_scores_gemma":[0.99963,0.0002059842,0.00005276457,0.00001689922,0.00007279567,0.0000215681],"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":[0.00002253651,0.00003470177,0.0004293719,0.00001643024,0.00001691897,0.00001898266,0.00001595465,0.9725638,0.0003660714,0.001710082,0.0003819266,0.02442325],"study_design_scores_gemma":[0.000007404267,0.00001557642,0.00006773075,0.000003010449,0.000004756026,0.000004129507,0.000005853103,0.9987531,0.00009730791,0.00082755,0.000210993,0.000002532066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1366384,0.0009246095,0.8464342,0.00053948,0.0001083228,0.0002112217,0.0001722907,0.0009429345,0.0140286],"genre_scores_gemma":[0.8476229,0.0003777408,0.1477248,0.0001357619,0.00003774858,0.0001693188,0.0001495043,0.000040217,0.0037419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01380692,"threshold_uncertainty_score":0.02745306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008819749481747557,"score_gpt":0.2070507609996484,"score_spread":0.1982310115179009,"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."}}