{"id":"W4386773691","doi":"10.5267/j.ijiec.2023.6.003","title":"A dynamic scheduling method with Conv-Dueling and generalized representation based on reinforcement learning","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Computer science; Dynamic priority scheduling; Scheduling (production processes); Rate-monotonic scheduling; Reinforcement learning; Fair-share scheduling; Mathematical optimization; Artificial intelligence; Schedule; Mathematics","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":[0.0007407686,0.0008172831,0.0008807397,0.0004382881,0.0004572255,0.0005577424,0.001525189,0.0008175735,0.002488412],"category_scores_gemma":[0.001376036,0.000498921,0.0007776935,0.0004871013,0.0004922802,0.001044399,0.0009159492,0.001238092,0.0003095324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009987026,"about_ca_system_score_gemma":0.00140586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01528233,"about_ca_topic_score_gemma":0.01028675,"domain_scores_codex":[0.9997196,0.00005860329,0.00001690431,0.00008404892,0.00005912903,0.00006172829],"domain_scores_gemma":[0.9996659,0.0001171243,0.00004380347,0.0000309385,0.0001038217,0.00003845053],"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":[0.00005011867,0.00004356268,0.0004064011,0.00004721983,0.00002852255,0.00005444192,0.00003986408,0.9284841,0.001630376,0.004412623,0.001400954,0.0634018],"study_design_scores_gemma":[0.000003641888,0.00001119611,0.00002150372,0.000001289367,0.000001954452,0.000004055098,0.000001527413,0.9989747,0.000151044,0.0006824298,0.0001448997,0.000001803712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01783257,0.0002804665,0.978559,0.0001998715,0.00008837567,0.00004465835,0.00005161853,0.0006310599,0.002312463],"genre_scores_gemma":[0.8148842,0.0002299294,0.1787806,0.0003019472,0.00006407234,0.0001754552,0.0003232132,0.0001500856,0.005090394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01528233,"threshold_uncertainty_score":0.03038675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02677162049098065,"score_gpt":0.295867984628311,"score_spread":0.2690963641373303,"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."}}