{"id":"W2808442168","doi":"10.1016/j.cie.2018.06.007","title":"A hybrid multiple criteria decision making approach for measuring comprehensive performance of reverse logistics enterprises","year":2018,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Reverse logistics; Performance measurement; Analytic hierarchy process; Computer science; Process management; Fuzzy logic; Product (mathematics); Integrated logistics support; Operations research; Manufacturing engineering; Supply chain; Business; Engineering; Marketing; Artificial intelligence","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":[0.006842475,0.001576604,0.00187366,0.005064419,0.001021079,0.003012379,0.001529007,0.001437406,0.001839264],"category_scores_gemma":[0.005696539,0.000739845,0.001853537,0.003191664,0.0006054729,0.001440141,0.001337585,0.0008981618,0.0001678517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002886379,"about_ca_system_score_gemma":0.002435095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00707863,"about_ca_topic_score_gemma":0.008567885,"domain_scores_codex":[0.9946585,0.002660274,0.0003696856,0.0003892306,0.001535286,0.0003870237],"domain_scores_gemma":[0.9959253,0.002582264,0.0003103598,0.0001224178,0.0008489463,0.0002107039],"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":[0.001331983,0.0009423938,0.015409,0.0005951932,0.001054574,0.0004288811,0.0004981988,0.7715177,0.01669255,0.008504665,0.001007885,0.1820169],"study_design_scores_gemma":[0.00005134356,0.0004736946,0.003748168,0.00003407768,0.0001545261,0.00004193521,0.0002357537,0.9889693,0.002432724,0.003413551,0.000390434,0.00005443619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.415961,0.0004457463,0.5757582,0.0002563411,0.00008574403,0.0005880312,0.0003636839,0.0002339153,0.006307282],"genre_scores_gemma":[0.8940763,0.00007608034,0.104606,0.0000344266,0.00001643453,0.0002127396,0.0001573779,0.00001164471,0.0008089424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00707863,"threshold_uncertainty_score":0.03618693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0530185306262513,"score_gpt":0.2401771053107704,"score_spread":0.1871585746845191,"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."}}