{"id":"W2005170914","doi":"10.1016/j.apm.2012.09.039","title":"A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return","year":2012,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":426,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supply chain; Supply chain network; Mathematical optimization; Facility location problem; Constraint (computer-aided design); Linear programming; Computer science; Closed loop; Integer programming; Loop (graph theory); Operations research; Supply chain management; Engineering; Mathematics; Business; Control engineering","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":[0.001636282,0.001321642,0.002497032,0.001058136,0.0009146045,0.002849259,0.002853246,0.00388622,0.005141606],"category_scores_gemma":[0.003312672,0.001298569,0.001334651,0.001899581,0.001613818,0.002310191,0.001665428,0.001827122,0.0005449477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002870816,"about_ca_system_score_gemma":0.002193557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04078893,"about_ca_topic_score_gemma":0.01809292,"domain_scores_codex":[0.9991884,0.0002684647,0.00003421945,0.0002158621,0.0001344253,0.000158679],"domain_scores_gemma":[0.998484,0.0008025843,0.0002620468,0.00004838576,0.0003118246,0.00009113086],"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":[0.00000958621,0.000005208071,0.00005163755,0.00001109104,0.000005588393,0.00001911045,0.000008190282,0.9982291,0.00006968913,0.001153037,0.00005516357,0.0003826591],"study_design_scores_gemma":[0.000004413403,0.00000790718,0.00003049084,0.000002198894,0.000003411145,0.000002907263,0.000005062009,0.9992902,0.00002212653,0.0005683457,0.00005942064,0.000003344093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06911667,0.0006104488,0.9147975,0.0007137029,0.0001068034,0.0001254338,0.0006868909,0.0003597535,0.01348286],"genre_scores_gemma":[0.9620503,0.0003727218,0.02447295,0.00008512398,0.00003498185,0.0002340609,0.0003661152,0.00006780672,0.01231603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04078893,"threshold_uncertainty_score":0.08110309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04261703251180964,"score_gpt":0.250358478824328,"score_spread":0.2077414463125183,"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."}}