{"id":"W2111731757","doi":"10.1287/ijoc.1090.0335","title":"A Lagrangean Heuristic for Hub-and-Spoke System Design with Capacity Selection and Congestion","year":2009,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Subgradient method; Heuristic; Computer science; Queue; Branch and bound; Transshipment (information security); Nonlinear system; Upper and lower bounds; Flow network; Nonlinear programming; Integer (computer science); Piecewise linear function; Network congestion; Cutting-plane method; Integer programming; 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":[0.001910771,0.001007666,0.001324899,0.001046099,0.0008343355,0.0008911471,0.001278154,0.001143416,0.00374491],"category_scores_gemma":[0.002473665,0.0009782633,0.0007054072,0.001012243,0.001065629,0.00117896,0.001100769,0.001220175,0.000377643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00178392,"about_ca_system_score_gemma":0.003344392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009009489,"about_ca_topic_score_gemma":0.009513904,"domain_scores_codex":[0.9992412,0.0003810705,0.00002015544,0.00008980968,0.000144092,0.0001235914],"domain_scores_gemma":[0.9990554,0.0006543231,0.00009623721,0.00003330287,0.000101733,0.00005902408],"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":[0.00002995033,0.00002067417,0.00008739144,0.00002581826,0.000009464288,0.00002991105,0.00002156402,0.9885633,0.0002714314,0.004662735,0.0003378389,0.005939896],"study_design_scores_gemma":[0.00001200043,0.00002163622,0.00002703653,0.000004185665,0.000003123885,0.00000560051,0.000008225044,0.9970645,0.0001428508,0.002321491,0.0003854298,0.000003840293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0189999,0.0002190052,0.9758382,0.0002120741,0.00002934298,0.0001475654,0.00007430967,0.0001636392,0.004315888],"genre_scores_gemma":[0.5110273,0.0002894504,0.4807035,0.0001586838,0.00003320681,0.0005659094,0.0001855806,0.0001180005,0.006918275],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009009489,"threshold_uncertainty_score":0.01791406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03055224279145047,"score_gpt":0.271753126021591,"score_spread":0.2412008832301405,"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."}}