{"id":"W2078415021","doi":"10.1287/trsc.1110.0398","title":"Exact Solution of Large-Scale Hub Location Problems with Multiple Capacity Levels","year":2012,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; Concordia University","funders":"","keywords":"Mathematical optimization; Robustness (evolution); Benders' decomposition; Benchmark (surveying); Enumeration; Reduction (mathematics); Decomposition; Linear programming; Scale (ratio); Integer programming; Mathematics; Computer science; Algorithm","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":[0.001055595,0.00009345533,0.0001093573,0.0001207055,0.0001091516,0.00001377703,0.0001246905,0.00004074573,0.00002409197],"category_scores_gemma":[0.00003544674,0.00008781374,0.00001778245,0.001044933,0.0001495386,0.0009538803,0.000001741338,0.00007646903,0.000005666554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005592358,"about_ca_system_score_gemma":0.00004356823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002975921,"about_ca_topic_score_gemma":0.0001060737,"domain_scores_codex":[0.9989103,0.00002210987,0.0002530876,0.0001438498,0.0003767141,0.00029395],"domain_scores_gemma":[0.999387,0.00003742631,0.00008056591,0.0001656599,0.0002374277,0.00009194163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000004536198,0.00004800347,0.1275757,0.0001064505,0.000004456,5.527352e-8,0.00747419,0.7660756,0.09756491,0.0002986738,0.000004443696,0.000842966],"study_design_scores_gemma":[0.0002951181,0.00001741642,0.6267357,0.00003833729,0.00001181389,9.370501e-7,0.0001698671,0.2805793,0.09195728,0.00001398099,0.00005483202,0.0001253759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.474704,0.000017915,0.5248524,0.00000809325,0.0000689233,0.0001089982,0.00001197037,0.00008907908,0.0001386367],"genre_scores_gemma":[0.8687816,0.000002920793,0.1311452,0.000007510576,0.00001611805,0.00001222511,0.00001115988,0.00001211925,0.00001111734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4991601,"threshold_uncertainty_score":0.358094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03828081850268888,"score_gpt":0.276578853531369,"score_spread":0.2382980350286801,"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."}}