{"id":"W2997440614","doi":"10.1016/j.cie.2019.106235","title":"Analysis of two substitute products newsvendor problem with a budget constraint","year":2019,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Beijing Social Science Fund; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Newsvendor model; Karush–Kuhn–Tucker conditions; Budget constraint; Order (exchange); Economic order quantity; Constraint (computer-aided design); Product (mathematics); Purchasing; Revenue; Mathematical optimization; Economics; Mathematics; Microeconomics; Operations management; Business; Supply chain","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002036087,0.001205325,0.003041131,0.001152568,0.0009123786,0.003000599,0.002023788,0.004255038,0.01259588],"category_scores_gemma":[0.006460269,0.001381283,0.00150457,0.001011849,0.001821294,0.003307293,0.001281319,0.002563932,0.0004374645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001797514,"about_ca_system_score_gemma":0.001281877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006036045,"about_ca_topic_score_gemma":0.002793666,"domain_scores_codex":[0.9991954,0.0003658322,0.0000332956,0.0001327257,0.00008911672,0.0001836013],"domain_scores_gemma":[0.9947857,0.004083272,0.0003654542,0.0001293255,0.0002681409,0.0003680846],"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":[0.0007420017,0.0003062379,0.001243899,0.000609272,0.0002155475,0.002154584,0.0002864698,0.6643458,0.001507689,0.3055081,0.01111678,0.01196367],"study_design_scores_gemma":[0.0001842946,0.0001129962,0.0005436836,0.00004743004,0.00009813749,0.0002087907,0.0002017543,0.9295692,0.0002862597,0.06618231,0.002517238,0.0000479262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.425458,0.008296024,0.4546025,0.01107205,0.0007657195,0.0002783866,0.0009275188,0.0004161277,0.09818367],"genre_scores_gemma":[0.9293197,0.001606663,0.02211494,0.0003016168,0.0002345059,0.0001303432,0.0002696702,0.000123555,0.04589907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01259588,"threshold_uncertainty_score":0.04213744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023447543447924,"score_gpt":0.1956318539386568,"score_spread":0.1753973785041775,"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."}}