{"id":"W1839817983","doi":"10.1002/atr.1237","title":"A differential evolution approach for the vehicle routing problem with backhauls and time windows","year":2013,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vehicle routing problem; Backhaul (telecommunications); Mathematical optimization; Benchmark (surveying); Computer science; Extension (predicate logic); Integer programming; Operations research; Routing (electronic design automation); Mathematics; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001738886,0.0001045181,0.0001560515,0.00005203889,0.00008388993,0.00003717858,0.00006661096,0.00004251795,0.00001262019],"category_scores_gemma":[0.00001280219,0.00007060423,0.0000450678,0.0001231389,0.00002396705,0.0004600468,0.000001022709,0.0001377091,5.937512e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000372293,"about_ca_system_score_gemma":0.00001448543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002601873,"about_ca_topic_score_gemma":0.000001291822,"domain_scores_codex":[0.9992731,0.00002189844,0.0003333557,0.00008185393,0.0001507331,0.0001391112],"domain_scores_gemma":[0.9994207,0.0001107065,0.0001732112,0.00006485044,0.0001841328,0.00004638648],"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":[0.00005442726,0.00001435977,0.0008905494,0.00005257622,0.00004803383,2.705777e-7,0.0005661146,0.9660037,0.02316839,0.0001060505,0.00001508416,0.009080486],"study_design_scores_gemma":[0.002175883,0.0001917683,0.116691,0.00008094123,0.0001389055,0.00001560521,0.0005191248,0.8775607,0.002112153,0.0003033045,0.00004143111,0.0001691651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3139522,0.00008740459,0.6854542,0.00004496408,0.00004059782,0.0003567261,0.000002657084,0.00002902898,0.00003223078],"genre_scores_gemma":[0.7121539,0.00001759358,0.2876936,0.000005383357,0.00006231677,0.00002402928,0.000007297265,0.00002069126,0.00001520178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3982017,"threshold_uncertainty_score":0.2879157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006941668243279474,"score_gpt":0.2162647424069311,"score_spread":0.2093230741636516,"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."}}