{"id":"W1973912655","doi":"10.1016/j.ejor.2010.02.038","title":"The multi-product newsboy problem with supplier quantity discounts and a budget constraint","year":2010,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Newsvendor model; Lagrangian relaxation; Mathematical optimization; Budget constraint; Computer science; Integer programming; Product (mathematics); Constraint (computer-aided design); Relaxation (psychology); Nonlinear system; Extension (predicate logic); Nonlinear programming; Integer (computer science); Mathematics; Supply chain; Economics; Microeconomics","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":[0.006820928,0.00294753,0.008893222,0.002448213,0.001586045,0.005555085,0.005097932,0.009973678,0.0264955],"category_scores_gemma":[0.01522573,0.006156935,0.002571774,0.003534186,0.003701287,0.009689218,0.002977611,0.005034795,0.001856788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003329232,"about_ca_system_score_gemma":0.0023998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01036717,"about_ca_topic_score_gemma":0.006120828,"domain_scores_codex":[0.9972447,0.001095576,0.0001797999,0.0006897264,0.0003130844,0.0004770741],"domain_scores_gemma":[0.9848472,0.01203208,0.001193579,0.0003987028,0.0004045308,0.001123724],"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":[0.002598947,0.000687058,0.001415603,0.001826914,0.0004606376,0.001837444,0.0003704311,0.8527535,0.001918767,0.08845393,0.01834986,0.02932689],"study_design_scores_gemma":[0.001029293,0.000559844,0.001329588,0.0001990509,0.0002462244,0.0004866283,0.000269969,0.9006631,0.0008397788,0.08799396,0.006183402,0.0001991295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3231716,0.01191374,0.5767516,0.01633462,0.001407746,0.0012654,0.005891877,0.001018958,0.0622445],"genre_scores_gemma":[0.8101166,0.004384059,0.09256835,0.0009084143,0.001369535,0.000680136,0.00183718,0.0007554202,0.08738034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0264955,"threshold_uncertainty_score":0.08863622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06164355128224296,"score_gpt":0.312343960297699,"score_spread":0.250700409015456,"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."}}