{"id":"W3016159714","doi":"10.1111/itor.12797","title":"Solving the clustered traveling salesman problem with ‐relaxed priority rule","year":2020,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Travelling salesman problem; Iterated local search; Mathematical optimization; Computer science; Class (philosophy); Iterated function; Traveling purchaser problem; 2-opt; Constraint (computer-aided design); Integer (computer science); Metaheuristic; Mathematics; 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":[0.001189537,0.0001332591,0.0001197461,0.0001570105,0.0003173098,0.000227131,0.0004377697,0.0000796246,0.0005552517],"category_scores_gemma":[0.0001483184,0.0001092642,0.00004296088,0.0006785876,0.0001108878,0.0003679498,0.00001815073,0.0009242691,0.00004684099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002455293,"about_ca_system_score_gemma":0.0001786964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004702479,"about_ca_topic_score_gemma":0.0001402378,"domain_scores_codex":[0.9979608,0.000227914,0.0003540666,0.000269856,0.0008976217,0.00028972],"domain_scores_gemma":[0.9988252,0.0005070759,0.00001954241,0.0001413464,0.0004142179,0.00009263449],"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":[0.00004732102,0.00003569543,0.000757843,0.00002144178,0.00005632968,0.000006088757,0.001702843,0.9894232,0.002003104,0.001295228,0.00008496771,0.004565976],"study_design_scores_gemma":[0.0006717733,0.00003670365,0.002364307,0.00005335482,0.00000503756,0.0000175445,0.0004910929,0.9915692,0.001581833,0.0002086943,0.002849818,0.0001507046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04076049,0.00006306254,0.9291964,0.01519438,0.0002324568,0.0006431398,0.00003649072,0.0002009023,0.01367266],"genre_scores_gemma":[0.9268923,0.00006580078,0.07216593,0.0001528161,0.0001850665,0.0001403663,0.00002584705,0.00003850637,0.000333419],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8861318,"threshold_uncertainty_score":0.6079617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07497900821825791,"score_gpt":0.3582295158604328,"score_spread":0.2832505076421749,"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."}}