{"id":"W3125828786","doi":"10.5267/j.ijiec.2020.11.003","title":"A specialized genetic algorithm for the fuel consumption heterogeneous fleet vehicle routing problem with bidimensional packing constraints","year":2021,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidad Tecnológica de Pereira","keywords":"Vehicle routing problem; Benchmark (surveying); Fuel efficiency; GRASP; Genetic algorithm; Routing (electronic design automation); Computer science; Set (abstract data type); Mathematical optimization; Reduction (mathematics); Consumption (sociology); Engineering; Automotive engineering; Mathematics; Embedded system","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":[0.0004020663,0.0001778076,0.0002306944,0.0001779459,0.00009277261,0.0001822002,0.0002396249,0.00009796551,0.00004442157],"category_scores_gemma":[0.0002562785,0.0001539299,0.0001321361,0.0002240318,0.00005170692,0.000141917,0.00003591521,0.0003440999,0.000002450906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001829888,"about_ca_system_score_gemma":0.0001949012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001844685,"about_ca_topic_score_gemma":0.00000142079,"domain_scores_codex":[0.998558,0.00005861394,0.0005840841,0.0001395523,0.0004478824,0.0002118588],"domain_scores_gemma":[0.9978306,0.0008727938,0.0002251421,0.00009898698,0.000884255,0.0000882695],"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":[0.00001762976,0.00002290875,0.0001730106,0.00000687132,0.0004602933,0.00005984102,0.000125148,0.9296077,0.0008228039,0.0001813049,0.0001089161,0.06841353],"study_design_scores_gemma":[0.002386811,0.00004846502,0.0003635355,0.0002380336,0.00008808732,0.0009290512,0.00004942908,0.9927714,0.001687089,0.00006348576,0.001195049,0.0001795064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02702936,0.0002887993,0.9694237,0.0003918974,0.002489129,0.0002193324,0.00004890197,0.00008929356,0.00001956983],"genre_scores_gemma":[0.4702051,0.00005522605,0.5282471,0.00004819057,0.001337263,0.00001480877,0.00002672177,0.00005409217,0.00001158775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4431757,"threshold_uncertainty_score":0.627708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03647383700614815,"score_gpt":0.2810366242157253,"score_spread":0.2445627872095772,"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."}}