{"id":"W2912451127","doi":"10.1109/access.2019.2894681","title":"Brainstorming-Based Ant Colony Optimization for Vehicle Routing With Soft Time Windows","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Hubei Province; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Ant colony optimization algorithms; Brainstorming; Simulated annealing; Computer science; Vehicle routing problem; Metaheuristic; Convergence (economics); Mathematical optimization; Ant colony; Routing (electronic design automation); Algorithm; Artificial intelligence; Mathematics","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":[0.0004032722,0.0002068872,0.0002564813,0.0001188618,0.0001052527,0.0001675056,0.0003225455,0.0001166254,0.0001269094],"category_scores_gemma":[0.00007640341,0.0002042652,0.00005389749,0.0003774822,0.00002362947,0.0005100495,0.00002187409,0.0001290451,0.00003750316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001217045,"about_ca_system_score_gemma":0.00006682186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001073465,"about_ca_topic_score_gemma":0.000003750582,"domain_scores_codex":[0.9988334,0.00004766118,0.0002748942,0.0002823823,0.0001962766,0.0003653643],"domain_scores_gemma":[0.9990609,0.0003067818,0.0001033814,0.0003069465,0.0001425118,0.00007951432],"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":[0.00004262657,0.00001761721,0.003767295,0.00008523498,0.00002563539,0.000001277812,0.00009670702,0.9909571,0.00317819,0.000008318849,0.0002684109,0.001551594],"study_design_scores_gemma":[0.001354776,0.00007413942,0.0003093799,0.00007436461,0.00002383892,0.000002098112,0.0000105165,0.9844454,0.01304391,0.000006365281,0.0003609951,0.0002942344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2638071,0.00001512462,0.7339743,0.00005118402,0.0003039435,0.0006652619,0.00001145896,0.0005178397,0.0006536602],"genre_scores_gemma":[0.8685802,0.000001257449,0.1305681,0.0002382682,0.0001382087,0.00007017272,0.00003613319,0.0001177732,0.0002499333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.604773,"threshold_uncertainty_score":0.8329694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01464693901849059,"score_gpt":0.2654492952613235,"score_spread":0.250802356242833,"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."}}