{"id":"W2121605956","doi":"10.1613/jair.4278","title":"Integrating Queueing Theory and Scheduling for Dynamic Scheduling Problems","year":2014,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Queueing theory; Computer science; Scheduling (production processes); Mathematical optimization; Layered queueing network; Dynamic priority scheduling; Schedule; Distributed computing; Mathematics; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.009382523,0.00014779,0.0002689498,0.0005151964,0.0002862669,0.000276302,0.0002794655,0.0001207091,0.00002033645],"category_scores_gemma":[0.003616858,0.0001300018,0.00009346713,0.0004527305,0.0001452804,0.0002832862,0.00004318422,0.0008395757,0.000009567536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009743746,"about_ca_system_score_gemma":0.00007400612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004884969,"about_ca_topic_score_gemma":0.00001359662,"domain_scores_codex":[0.9979596,0.0002453067,0.0007386039,0.0001732765,0.0004315979,0.0004516161],"domain_scores_gemma":[0.9969602,0.001779626,0.000121088,0.0001418948,0.0008206675,0.0001765877],"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":[0.00005757514,0.00002537184,0.00002781463,0.000128789,0.00004119991,0.00000200063,0.001249121,0.6619287,0.01289427,0.02697668,0.000003160494,0.2966653],"study_design_scores_gemma":[0.00005169657,0.0001755124,0.0000041107,0.0003355478,0.00001068111,0.00002307685,0.003908258,0.9286614,0.01571718,0.05090778,0.00007390278,0.0001308469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.142786,0.000928501,0.85537,0.0001675677,0.0003742326,0.0001675577,9.815507e-7,0.00004726044,0.0001579281],"genre_scores_gemma":[0.7214159,0.000227036,0.2779495,0.00001056336,0.0003331892,0.000008700476,9.251071e-7,0.00003583153,0.00001823868],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.57863,"threshold_uncertainty_score":0.5301322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06363703394117042,"score_gpt":0.3678860068633077,"score_spread":0.3042489729221373,"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."}}