{"id":"W2884929083","doi":"10.1016/j.ijpe.2018.07.016","title":"Electric vehicle routing problem with recharging stations for minimizing energy consumption","year":2018,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":269,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; McMaster University","funders":"","keywords":"Energy consumption; Computer science; Heuristics; Vehicle routing problem; Greenhouse gas; Electric vehicle; Routing (electronic design automation); Mathematical optimization; Automotive engineering; Engineering; Computer network; Electrical engineering; Mathematics; Power (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008894684,0.001890896,0.001724597,0.0008359302,0.0006039335,0.001695903,0.001984905,0.002619583,0.008419918],"category_scores_gemma":[0.001957082,0.0009415325,0.001167942,0.001430721,0.0006625951,0.001657089,0.000858242,0.001181424,0.0005964322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245542,"about_ca_system_score_gemma":0.001198613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005382704,"about_ca_topic_score_gemma":0.003883973,"domain_scores_codex":[0.9995615,0.0001829636,0.00001379804,0.0001146777,0.00005807371,0.00006902787],"domain_scores_gemma":[0.9995435,0.0002832834,0.00005121543,0.00002650411,0.00006142622,0.00003391725],"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":[0.00007919654,0.00005105599,0.000186191,0.00007425741,0.00003684643,0.0001146857,0.00002280323,0.9799999,0.0006933903,0.00879083,0.001471203,0.008479541],"study_design_scores_gemma":[0.00003066755,0.00005347965,0.0001241515,0.000008660548,0.00003033423,0.00003490011,0.00003313238,0.9913809,0.0003998618,0.006763302,0.001133018,0.000007523186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09504169,0.0007140898,0.8705143,0.001692405,0.0003054695,0.0003800513,0.0009021446,0.0004022553,0.03004747],"genre_scores_gemma":[0.8044582,0.0007308566,0.1501571,0.0002702566,0.000192447,0.0004088742,0.000739592,0.0002464101,0.0427962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008419918,"threshold_uncertainty_score":0.02816749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02196577315842007,"score_gpt":0.2677034716093187,"score_spread":0.2457376984508987,"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."}}