{"id":"W2149179348","doi":"10.1109/cimat.1994.389041","title":"The use of batch sizing to improve flow and waiting times in FMS","year":2002,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Sizing; Scheduling (production processes); Computer science; Flexible manufacturing system; Product (mathematics); Genetic algorithm; Job shop scheduling; Process (computing); Industrial engineering; Manufacturing engineering; Mathematical optimization; Engineering; Mathematics; Machine learning; Operating system; Schedule","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":[0.0008049986,0.0006054268,0.0004976587,0.0004275868,0.0003872197,0.0004570523,0.0006025981,0.0003286137,0.0009929835],"category_scores_gemma":[0.001597327,0.0002677926,0.0003306343,0.000419305,0.0004838585,0.0009584174,0.0002421414,0.0003523373,0.0001282556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006513731,"about_ca_system_score_gemma":0.0007510058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004620338,"about_ca_topic_score_gemma":0.00430791,"domain_scores_codex":[0.9997391,0.0000750603,0.0000182324,0.00004667837,0.00009010018,0.00003080509],"domain_scores_gemma":[0.9995093,0.0003062196,0.00006301265,0.00004459301,0.00005819808,0.00001865583],"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":[0.0005749062,0.0001166691,0.001234886,0.0001117155,0.00004580968,0.00004115206,0.00006985923,0.806075,0.03784898,0.006615211,0.0007281001,0.1465378],"study_design_scores_gemma":[0.00004105846,0.0002360859,0.0006740388,0.000005491586,0.00002685919,0.00002029115,0.00001049873,0.9789407,0.0159792,0.003127222,0.0009243278,0.0000143211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2158432,0.001014854,0.7777424,0.0002227637,0.0001584964,0.00007946968,0.00008130173,0.001236387,0.003621147],"genre_scores_gemma":[0.8517604,0.0003202371,0.1462423,0.00005213856,0.00004247936,0.00003298394,0.00007000858,0.00009447279,0.001384926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004620338,"threshold_uncertainty_score":0.009186924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134366161926976,"score_gpt":0.1991838292832329,"score_spread":0.1778401676639632,"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."}}