{"id":"W4211174428","doi":"10.1007/978-3-319-91086-4_3","title":"Variable Neighborhood Search","year":2018,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"ca_institutions":"Group for Research in Decision Analysis; Royal Military College of Canada; HEC Montréal","funders":"","keywords":"Variable neighborhood search; Metaheuristic; Mathematical optimization; Mathematical proof; Mathematics; Combinatorial optimization; Guided Local Search; Integer programming; Linear programming; Nonlinear programming; Nonlinear system; Computer science","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":["metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["sts","insufficient_payload"],"category_scores_codex":[0.02477787,0.0008014794,0.0005838557,0.01575762,0.003157632,0.0057998,0.009801193,0.0003179351,0.008143189],"category_scores_gemma":[0.001249342,0.0008907275,0.0001356389,0.007751512,0.009629058,0.008264717,0.006000623,0.002472893,0.001178676],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007404632,"about_ca_system_score_gemma":0.0009388069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003292406,"about_ca_topic_score_gemma":0.00179393,"domain_scores_codex":[0.9806645,0.0004147891,0.001875276,0.002702493,0.01181746,0.002525478],"domain_scores_gemma":[0.9923724,0.0002610453,0.00006682135,0.002064609,0.004728266,0.0005068391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006126873,0.0001262561,0.00008075803,0.00007810279,0.0001027214,0.0001114959,0.0005979398,0.4134805,0.0004098153,0.581275,0.001672349,0.002003721],"study_design_scores_gemma":[0.001415598,0.0002585584,0.001370129,0.001279223,0.0000228282,0.00005691547,0.004379775,0.7536773,0.0008463459,0.03979165,0.1953515,0.001550165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001326293,0.0001071298,0.01329151,0.004333376,0.003875945,0.003326142,0.0001763049,0.0003258701,0.9732375],"genre_scores_gemma":[0.08723812,0.007513738,0.1743564,0.0001832759,0.0009485151,0.00175403,0.0005125254,0.000277586,0.7272158],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5414834,"threshold_uncertainty_score":0.9998285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0602887393943799,"score_gpt":0.398312244958024,"score_spread":0.3380235055636441,"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."}}