{"id":"W4387020807","doi":"10.1007/s11081-023-09846-4","title":"A multi-vendor multi-buyer integrated production-inventory model with greenhouse gas emissions","year":2023,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Greenhouse gas; Supply chain; Solver; Simulated annealing; Mathematical optimization; Computer science; Integer programming; Vendor; Production (economics); Linear programming; Metaheuristic; Total cost; Supply chain optimization; Nonlinear programming; Operations research; Supply chain management; Nonlinear system; Algorithm; Mathematics; Economics; Business; Microeconomics","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":[0.0001400367,0.0001830987,0.0001286549,0.0004026193,0.0001536619,0.0001358956,0.00008995685,0.00004519719,0.00007858931],"category_scores_gemma":[0.00006941298,0.0001591827,0.00003078976,0.0006934335,0.00002261223,0.0005943481,0.00008134261,0.00009968076,0.00004047838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000295327,"about_ca_system_score_gemma":0.00001120998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004116453,"about_ca_topic_score_gemma":0.00001803652,"domain_scores_codex":[0.9991656,0.000003631666,0.0001675227,0.0002825403,0.0001413528,0.0002393712],"domain_scores_gemma":[0.9996664,0.00000548659,0.00005636142,0.0001659144,0.00007538097,0.0000304436],"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":[0.000008756232,0.0000504115,0.0008399097,0.0001120431,0.00002383803,0.000005134005,0.0000994941,0.9952039,0.0003258896,0.0002139277,0.002826318,0.0002904416],"study_design_scores_gemma":[0.0005969788,0.000005136867,0.0002638289,0.00008324192,0.00002788851,0.000001164097,0.0002278992,0.98867,0.00002473942,0.000003012834,0.009885205,0.0002109222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08838248,0.00008369624,0.9048433,0.001474157,0.0008417674,0.0009095712,0.000005177441,0.002458179,0.001001684],"genre_scores_gemma":[0.7830115,0.000500603,0.1859881,0.001198491,0.00108354,0.0004474199,0.0006352058,0.0003728783,0.02676223],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7188551,"threshold_uncertainty_score":0.6491281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02536269849134322,"score_gpt":0.2099744873886243,"score_spread":0.1846117888972811,"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."}}