{"id":"W2274691548","doi":"10.1111/poms.12544","title":"Inventory Rationing for Multiple Class Demand under Continuous Review","year":2015,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Rationing; Lead time; Markov decision process; Mathematical optimization; Lost sales; Holding cost; Average cost; Computer science; Time horizon; Profit (economics); Economics; Operations research; Microeconomics; Mathematics; Markov process; Operations management; Statistics","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":[0.0009494806,0.0001801462,0.0001929906,0.0002296882,0.0003928515,0.0003416941,0.0001231181,0.0000303971,0.00007276573],"category_scores_gemma":[0.0002279176,0.0001721483,0.0000555056,0.0003010118,0.00005320985,0.001036563,0.0001455933,0.0000620035,0.0001003558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006845394,"about_ca_system_score_gemma":0.00001310926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004558674,"about_ca_topic_score_gemma":0.0001579759,"domain_scores_codex":[0.9987411,0.00002195638,0.0003435918,0.000437355,0.0002367944,0.0002192163],"domain_scores_gemma":[0.9993076,0.000008271112,0.0000930656,0.0002817652,0.0002775061,0.00003182928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006012174,0.000309772,0.002793885,0.00294943,0.0001979741,0.000002905557,0.0001567976,0.01151925,0.00005361408,0.3147646,0.6552856,0.01190605],"study_design_scores_gemma":[0.001025979,0.00002508758,0.000449739,0.0003508094,0.0002324521,0.000002116022,0.001576047,0.02962623,0.00002898267,0.001882409,0.9644718,0.000328334],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07310978,0.04530829,0.2814216,0.2719424,0.02215548,0.03733307,0.00002034267,0.002752961,0.2659561],"genre_scores_gemma":[0.903091,0.003365859,0.005650473,0.04000883,0.003968291,0.002654058,0.0004771296,0.0001014269,0.04068298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8299811,"threshold_uncertainty_score":0.7020006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05082724771990603,"score_gpt":0.2612461828819477,"score_spread":0.2104189351620416,"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."}}