{"id":"W2074030523","doi":"10.1007/s11590-014-0788-9","title":"A general variable neighborhood search variants for the travelling salesman problem with draft limits","year":2014,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Travelling salesman problem; Traveling purchaser problem; Variable neighborhood search; Descent (aeronautics); 2-opt; Mathematical optimization; Computational intelligence; Variable (mathematics); Set (abstract data type); Computer science; Limit (mathematics); Extension (predicate logic); Bottleneck traveling salesman problem; Context (archaeology); Mathematics; Metaheuristic; Artificial intelligence","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":[0.0007428374,0.0002236,0.0001940331,0.00010385,0.00028708,0.0001840939,0.0002563543,0.00008929081,0.00005591437],"category_scores_gemma":[0.00005358359,0.0001773663,0.00004485674,0.0004669531,0.00004568898,0.0002179491,0.00001640934,0.0001879922,0.00000571686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006033482,"about_ca_system_score_gemma":0.00002999087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006734333,"about_ca_topic_score_gemma":0.000001303336,"domain_scores_codex":[0.998647,0.0001228274,0.0002858521,0.0002867313,0.0002303913,0.0004271822],"domain_scores_gemma":[0.999034,0.0003703136,0.00006036071,0.0003327555,0.0001185058,0.00008403919],"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":[0.00001630301,0.00001214893,0.0001445841,0.00005069359,0.0000691245,4.030077e-7,0.0002682467,0.9932187,0.002138766,0.002044151,0.0004376511,0.001599179],"study_design_scores_gemma":[0.0008023037,0.00003905276,0.0001043141,0.00003581919,0.00005534663,0.000007610108,0.0000168727,0.9972998,0.0005616079,0.00002692202,0.0008006974,0.0002496254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008112057,0.00001931623,0.9947014,0.001411834,0.0001794175,0.0006356611,0.00000781512,0.0003451718,0.001888149],"genre_scores_gemma":[0.05407461,0.00002279312,0.9441194,0.001002055,0.0002942733,0.0001187126,0.000054929,0.000126123,0.0001871241],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0532634,"threshold_uncertainty_score":0.7232786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01278262418791783,"score_gpt":0.225733239364,"score_spread":0.2129506151760822,"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."}}