{"id":"W3006592866","doi":"10.1177/1687814020902321","title":"Design of optimal quantity discounts for multi-period bilevel production planning under uncertain demands","year":2020,"lang":"en","type":"article","venue":"Advances in Mechanical Engineering","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Japan Society for the Promotion of Science; Beijing Social Science Fund; Beijing University of Chemical Technology; National Natural Science Foundation of China","keywords":"Stackelberg competition; Bilevel optimization; Supply chain; Production (economics); Production planning; Order (exchange); Mathematical optimization; Economic order quantity; Computer science; Microeconomics; Operations research; Economics; Business; Optimization problem; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00268852,0.0007646104,0.001365582,0.0005457022,0.0005745342,0.001762491,0.001211394,0.0008572453,0.002250652],"category_scores_gemma":[0.005056334,0.0009564848,0.0006437288,0.0006823413,0.0008913989,0.002279193,0.001450074,0.001522645,0.0001815693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00167586,"about_ca_system_score_gemma":0.002016879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002024228,"about_ca_topic_score_gemma":0.00141419,"domain_scores_codex":[0.9989511,0.0003303986,0.00006591684,0.0002381736,0.0002439573,0.0001703342],"domain_scores_gemma":[0.9981698,0.000904982,0.000366065,0.0001218815,0.0002165251,0.000220722],"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":[0.0001166506,0.00003487178,0.0003769063,0.00007039009,0.00001938881,0.0000743011,0.0001057271,0.9552252,0.001879369,0.02497827,0.0002139979,0.01690488],"study_design_scores_gemma":[0.00001098121,0.00003660021,0.00006617312,0.00000759145,0.000004839527,0.00001374183,0.00001956889,0.9920558,0.000622472,0.006829802,0.0003250078,0.000007430419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06205194,0.0002663726,0.9344195,0.0001525771,0.00002703717,0.000112699,0.00006645111,0.00008213461,0.002821315],"genre_scores_gemma":[0.9226378,0.0002717815,0.07547387,0.00002392135,0.00001363659,0.0001057239,0.00005956053,0.00004065287,0.001373112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00268852,"threshold_uncertainty_score":0.01421839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08296681235500146,"score_gpt":0.2952801782208893,"score_spread":0.2123133658658878,"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."}}