{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004691716,0.0001053502,0.00007840821,0.00008269085,0.0000475739,0.00004560292,0.00007275988,0.00005144232,0.0002331628],"category_scores_gemma":[0.000003315133,0.0001054464,0.00004117712,0.0000764687,0.000009580324,0.00005569213,0.000006228158,0.00008431686,0.000148074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004632798,"about_ca_system_score_gemma":0.000006489258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001692501,"about_ca_topic_score_gemma":0.00001298965,"domain_scores_codex":[0.9994686,0.000005028789,0.000139313,0.0001080872,0.00009609142,0.0001829111],"domain_scores_gemma":[0.999793,0.00001853568,0.00001090368,0.0001275112,0.00001225565,0.00003781692],"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":[6.401322e-7,0.000006680637,0.00004913216,0.00001199034,0.00000437605,0.000004262046,0.00000364789,0.9971876,0.0003387446,0.00006769312,0.00005394741,0.002271282],"study_design_scores_gemma":[0.0002242546,0.000002929128,0.000560383,0.000009022528,0.000004815344,0.000001365464,0.00001631719,0.9716614,0.0269799,0.00006187965,0.0003359701,0.0001417929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1939267,0.000131254,0.7934834,0.00005587729,0.0003336078,0.00005282879,0.000001676064,0.001186263,0.01082846],"genre_scores_gemma":[0.7282926,0.000003000576,0.2710933,0.00004557178,0.00004968287,0.0000045092,0.00002059175,0.00002519515,0.0004655173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.534366,"threshold_uncertainty_score":0.4299979,"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."}}