{"id":"W2074379456","doi":"10.1115/msec2006-21111","title":"Agent-Based Dynamic Manufacturing Scheduling","year":2006,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Profitability index; Scheduling (production processes); Computer science; Software; Manufacturing engineering; Dynamic priority scheduling; Advanced manufacturing; Productivity; Industrial engineering; Job shop scheduling; Engineering; Operations management; Business; Embedded system; Telecommunications; Operating system","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":[0.0007990329,0.0006306727,0.0008104055,0.0004580356,0.0007784093,0.001316249,0.001352777,0.0008296161,0.002927413],"category_scores_gemma":[0.00167599,0.0003831727,0.0003912926,0.0005205068,0.0005596272,0.0008402154,0.0008954318,0.000761733,0.0005179647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000962163,"about_ca_system_score_gemma":0.001424695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006503546,"about_ca_topic_score_gemma":0.006518561,"domain_scores_codex":[0.9994961,0.0001823571,0.0000275127,0.00009550535,0.0001305524,0.00006788665],"domain_scores_gemma":[0.9992022,0.0003878756,0.00008796802,0.00008907995,0.0001423842,0.00009051779],"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":[0.0001009973,0.00005104476,0.0002611097,0.00004470371,0.00002775603,0.00005273587,0.00003199852,0.9542379,0.001436397,0.01400337,0.00169681,0.02805514],"study_design_scores_gemma":[0.00001681676,0.00001319117,0.00004077742,0.000002443474,0.000004761496,0.00000876317,0.000006051124,0.9932982,0.0002708797,0.004325049,0.00200999,0.000003114867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0276548,0.0005707092,0.9569607,0.0004940452,0.0001748794,0.0001222075,0.0001394907,0.001102267,0.01278077],"genre_scores_gemma":[0.7865103,0.0005909234,0.2039189,0.000133975,0.00008511748,0.000253502,0.0003189096,0.0001224637,0.008065959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006503546,"threshold_uncertainty_score":0.01293141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005886035071542907,"score_gpt":0.2021473810552327,"score_spread":0.1962613459836899,"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."}}