{"id":"W4415994659","doi":"10.2139/ssrn.5712098","title":"Adaptive job scheduling and resource allocation using digital twin and deep reinforcement learning method","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Resource-Constrained Project Scheduling","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Scheduling (production processes); Markov decision process; Resource allocation; Job scheduler; Process (computing); Markov process; Dynamic priority scheduling","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001283517,0.0005527431,0.0009893993,0.0004734353,0.0003228114,0.0008990728,0.001284465,0.001084799,0.002435111],"category_scores_gemma":[0.00444759,0.0004953431,0.0004059947,0.0005513965,0.000828711,0.001288973,0.001104373,0.001411933,0.0002424551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105833,"about_ca_system_score_gemma":0.001568246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006156956,"about_ca_topic_score_gemma":0.005379713,"domain_scores_codex":[0.9996442,0.0000975656,0.00001776468,0.0000892946,0.00007712474,0.00007410355],"domain_scores_gemma":[0.9984388,0.000908706,0.0001340402,0.0001092111,0.0002695466,0.0001396912],"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":[0.0001163479,0.00006213778,0.0005339499,0.00003944945,0.00002826057,0.00002661716,0.00002018015,0.9569297,0.001140847,0.006409941,0.0006136395,0.03407889],"study_design_scores_gemma":[0.000002885389,0.000005056583,0.00002383992,9.042062e-7,0.000001484752,0.000001383146,8.622443e-7,0.9988562,0.00007751061,0.0009983993,0.00003063604,9.356022e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.050411,0.0003051847,0.9457411,0.0003483428,0.0001161596,0.00003251633,0.00005651393,0.0003347208,0.002654548],"genre_scores_gemma":[0.9004412,0.0001304461,0.09602623,0.0001550107,0.0000506584,0.00008022119,0.0000800113,0.00004562673,0.002990665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006156956,"threshold_uncertainty_score":0.0122422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04495937040205105,"score_gpt":0.3504378119208982,"score_spread":0.3054784415188472,"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."}}