{"id":"W4386304012","doi":"10.32920/24058728","title":"Real-Time Optimization of Production Scheduling: Strategies, Models, and Algorithms","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Scheduling (production processes); Computer science; Production (economics); Mathematical optimization; Job shop scheduling; Job shop; Optimization algorithm; Operations research; Algorithm; Industrial engineering; Flow shop scheduling; Engineering; Mathematics; Schedule; Economics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003089181,0.0003108465,0.0004088729,0.000286455,0.00004713835,0.0001143637,0.0001580353,0.0003900301,0.00004584314],"category_scores_gemma":[0.00004536128,0.0003322058,0.00006584387,0.000277142,0.00005353653,0.0002395496,0.0001516349,0.0003410687,0.00001200403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005686264,"about_ca_system_score_gemma":0.0001058494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001088706,"about_ca_topic_score_gemma":0.000003610573,"domain_scores_codex":[0.9985322,0.00003254135,0.0005003818,0.0004638105,0.0002559682,0.0002150882],"domain_scores_gemma":[0.9991072,0.00003462897,0.0001206414,0.0004294612,0.0002217365,0.00008630291],"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":[0.000003828148,0.00001478339,0.000003499597,0.000322858,0.000077406,9.867974e-7,0.0002129459,0.9980249,0.00014124,0.0003041954,0.0001453353,0.0007480502],"study_design_scores_gemma":[0.0001398132,0.00001366532,0.00001189931,0.0001549691,0.00004798477,0.000003142464,0.0002669872,0.9964151,0.0006006015,0.002027801,0.000003143969,0.0003149258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005851435,0.0003011934,0.9863725,0.000120508,0.001326651,0.0004344847,0.00003302238,0.002156639,0.003403566],"genre_scores_gemma":[0.0145905,0.005861884,0.9774004,0.000003068675,0.0002977178,0.00005361254,0.0004780431,0.0001495855,0.001165238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008972151,"threshold_uncertainty_score":0.999913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03457887576853821,"score_gpt":0.2548764552386413,"score_spread":0.2202975794701031,"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."}}