{"id":"W2551278857","doi":"10.5267/j.ijiec.2016.11.001","title":"An adaptive large neighborhood search heuristic for solving the reliable multiple allocation hub location problem under hub disruptions","year":2016,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Heuristic; Computer science; Location-allocation; Operations research; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008383747,0.0005947503,0.0009623772,0.0007102248,0.0004012763,0.0004654076,0.00106449,0.0009935393,0.001238154],"category_scores_gemma":[0.001738766,0.000409668,0.0005121076,0.0006755753,0.0004556871,0.0006515361,0.0005268641,0.0006004631,0.0001161915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007273746,"about_ca_system_score_gemma":0.001281213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009960188,"about_ca_topic_score_gemma":0.00915189,"domain_scores_codex":[0.9996761,0.0001609981,0.00001116607,0.00004498983,0.00005450622,0.00005221775],"domain_scores_gemma":[0.9992519,0.0005237458,0.00008742834,0.00002302713,0.00007075249,0.00004314392],"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":[0.00003510153,0.00002658525,0.0002006568,0.00001728163,0.00001205131,0.00002655578,0.00001256158,0.9906941,0.0002400638,0.001354789,0.0002678509,0.007112459],"study_design_scores_gemma":[0.000007463838,0.00001234849,0.00002666592,0.000001487216,0.000002038765,0.000003543246,0.000004404324,0.9994959,0.00004266996,0.0003420151,0.00006041781,0.000001072886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1221096,0.0007149779,0.8717602,0.0003009398,0.00006743793,0.000125694,0.00007783637,0.0004125861,0.004430713],"genre_scores_gemma":[0.8020683,0.0001992022,0.1954406,0.00008971102,0.00003554183,0.0002299767,0.0001305966,0.00005949844,0.001746492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009960188,"threshold_uncertainty_score":0.01980442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03040996231410991,"score_gpt":0.2731424699212379,"score_spread":0.2427325076071279,"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."}}