{"id":"W2253916853","doi":"10.1016/j.ijpe.2016.01.004","title":"Optimization of closed-loop supply chain of multi-items with returned subassemblies","year":2016,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Spare part; Remanufacturing; Reuse; Reverse logistics; Product (mathematics); Production (economics); Supply chain; Computer science; Closed loop; Business; Manufacturing engineering; Operations management; Unit (ring theory); Service (business); Marketing; Engineering; Economics; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002333274,0.001615877,0.002491509,0.001238884,0.00147987,0.003343496,0.002096047,0.002421029,0.006488017],"category_scores_gemma":[0.003324311,0.001244112,0.001080818,0.00128255,0.001203811,0.00215574,0.001613823,0.001106861,0.0005589818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001852927,"about_ca_system_score_gemma":0.002020466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01167713,"about_ca_topic_score_gemma":0.006459571,"domain_scores_codex":[0.9989628,0.0002799689,0.00003773921,0.0002781878,0.0001794325,0.0002619061],"domain_scores_gemma":[0.9981226,0.0009188083,0.0002943453,0.0001039869,0.0003847821,0.0001753494],"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":[0.000201376,0.00007859615,0.0003891829,0.00007650708,0.00002459819,0.0001087863,0.00003606206,0.989762,0.001689725,0.000860048,0.0002007611,0.006572356],"study_design_scores_gemma":[0.00001969519,0.0001658229,0.0002062074,0.000007429992,0.00001764878,0.00001528062,0.00002560451,0.9981254,0.00062011,0.0006577378,0.0001318946,0.000007230725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4023403,0.0007909904,0.5831811,0.0004909278,0.0001487476,0.000361071,0.0002563562,0.0006639858,0.0117666],"genre_scores_gemma":[0.9745034,0.0001274308,0.02115652,0.00002977724,0.00001368199,0.0001101715,0.00009813888,0.00004474823,0.003916037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01167713,"threshold_uncertainty_score":0.02321833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374778920532656,"score_gpt":0.2172893630508643,"score_spread":0.2035415738455378,"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."}}