{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005627241,0.00118162,0.00106683,0.0008995428,0.0003944352,0.0009218854,0.001109491,0.001897403,0.002498908],"category_scores_gemma":[0.001764956,0.0003672354,0.000909741,0.0009973295,0.0004751912,0.0005265378,0.0008254844,0.0009879542,0.000437345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000727376,"about_ca_system_score_gemma":0.001726143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005490886,"about_ca_topic_score_gemma":0.004008621,"domain_scores_codex":[0.9996859,0.00009649486,0.00001222592,0.00006738985,0.00007935922,0.0000585793],"domain_scores_gemma":[0.9996654,0.0001863745,0.00004004096,0.00002308028,0.00006082515,0.00002438848],"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":[0.00002821825,0.00005698482,0.0003288959,0.00005940461,0.00002788226,0.00007960117,0.00004672951,0.9454425,0.00141505,0.005455455,0.000990962,0.04606834],"study_design_scores_gemma":[0.00001501596,0.00003776453,0.00007218497,0.000008648077,0.000009380777,0.00002512023,0.00001216268,0.9973192,0.0002685271,0.001325612,0.0009023626,0.000004090103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02910991,0.0004011039,0.9639775,0.0001768668,0.00008677207,0.0001450542,0.00008450919,0.0003941823,0.005624205],"genre_scores_gemma":[0.3053042,0.0005846561,0.6877291,0.0002148277,0.00007407174,0.0006772915,0.0004729553,0.0001301353,0.004812858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005490886,"threshold_uncertainty_score":0.0109179,"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."}}