{"id":"W1985556047","doi":"10.1016/j.ijpe.2013.01.031","title":"Joint optimal lot sizing and production control policy in an unreliable and imperfect manufacturing system","year":2013,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Polytechnique Montréal","funders":"","keywords":"Sizing; Computer science; Holding cost; Mathematical optimization; Inventory control; Operations research; Safety stock; Total cost; Imperfect; Production (economics); Minification; Robustness (evolution); Control (management); Reliability engineering; Supply chain; Economics; Engineering; Mathematics; Microeconomics; Business","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":[0.0006215239,0.0001180586,0.0001754898,0.0006776542,0.00006202712,0.0004159103,0.0001258648,0.00003181805,0.00003036637],"category_scores_gemma":[0.0001026417,0.000114123,0.00003526083,0.00005067094,0.00003802621,0.003231717,0.00006725575,0.0001208231,0.00001399054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000242679,"about_ca_system_score_gemma":0.00001971075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004171391,"about_ca_topic_score_gemma":0.00003290228,"domain_scores_codex":[0.9990595,0.00001480274,0.0004618567,0.000219112,0.0001073939,0.0001373384],"domain_scores_gemma":[0.9992509,0.000008989157,0.0004355833,0.00009177154,0.00019109,0.00002168092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001366115,0.001099603,0.1981671,0.001570083,0.001357545,0.0000801569,0.002079274,0.4069375,0.03189133,0.06640928,0.007812423,0.2812296],"study_design_scores_gemma":[0.01541819,0.0006408756,0.4997096,0.001992391,0.0004108656,0.002301824,0.02712774,0.3126693,0.03418961,0.01963018,0.08313261,0.002776864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897134,0.00007499776,0.0001403027,0.006943449,0.002530721,0.0002389217,5.628875e-7,0.00001908747,0.0003385566],"genre_scores_gemma":[0.9944712,0.0001101081,0.0002383646,0.000316872,0.004742288,0.000009751729,0.00000270632,0.00001550685,0.00009323625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3015425,"threshold_uncertainty_score":0.4653799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01357454442261401,"score_gpt":0.2124004088685589,"score_spread":0.1988258644459449,"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."}}