{"id":"W2145245983","doi":"10.5555/1162708.1163005","title":"Optimal lot-sizing in a two-stage system with auto-correlated arrivals","year":2005,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Sizing; Independence (probability theory); Computer science; Mathematical optimization; Constant (computer programming); Product (mathematics); Production (economics); Stage (stratigraphy); Industrial engineering; Mathematics; Statistics; Engineering","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":[0.0001237382,0.0001719709,0.000188816,0.0001577156,0.00003684953,0.00009720652,0.0001225322,0.00006225417,0.0001712151],"category_scores_gemma":[0.00001840482,0.0001645502,0.00002806005,0.0002340634,0.00002003019,0.000276543,0.00001677198,0.0001914916,0.0001064897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001291394,"about_ca_system_score_gemma":0.0000274429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001465027,"about_ca_topic_score_gemma":0.00003760971,"domain_scores_codex":[0.9990653,0.00003089951,0.0003181547,0.0002097507,0.0001555948,0.000220317],"domain_scores_gemma":[0.9995224,0.00006639161,0.00005083184,0.0001908139,0.00009986678,0.00006968219],"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":[0.00001798797,0.00001013154,0.001366662,0.00002375115,0.00001491337,0.000006802574,0.0009255469,0.9954148,0.00008143268,0.0002993381,0.000002473879,0.001836139],"study_design_scores_gemma":[0.0009379675,0.00001717891,0.0007675099,0.0002293958,0.000007364335,0.000004713997,0.0003464831,0.9968915,0.0003907924,9.435733e-7,0.0001981122,0.0002080243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2437642,0.00003749034,0.752274,0.0000396632,0.0001582717,0.0001486133,0.000004287384,0.000564125,0.00300933],"genre_scores_gemma":[0.9620373,0.000001680931,0.03749648,0.0000297625,0.00007810338,0.00001219314,0.00001504019,0.00003197272,0.0002975109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.718273,"threshold_uncertainty_score":0.6710162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01802930488890663,"score_gpt":0.2581707402629884,"score_spread":0.2401414353740817,"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."}}