{"id":"W4323342349","doi":"10.5267/j.ijiec.2023.2.001","title":"General variable neighborhood search for electric vehicle routing problem with time-dependent speeds and soft time windows","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science Fund of the Republic of Serbia; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja","keywords":"Vehicle routing problem; Benchmark (surveying); Metaheuristic; Mathematical optimization; Computer science; Electric vehicle; Variable (mathematics); Variable neighborhood search; Set (abstract data type); Routing (electronic design automation); Quality (philosophy); Integer (computer science); Local search (optimization); Operations research; Mathematics; Power (physics)","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":[0.0008345539,0.0001742197,0.0002474948,0.0005937105,0.00007502275,0.0001786714,0.0002573424,0.0001112189,0.00001974704],"category_scores_gemma":[0.0001942425,0.0001751098,0.00006010971,0.0006488038,0.00001272112,0.0002729926,0.00004543,0.0003893536,0.00001303831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001787531,"about_ca_system_score_gemma":0.0001469545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004228558,"about_ca_topic_score_gemma":1.151619e-7,"domain_scores_codex":[0.9986084,0.00004104916,0.0004741739,0.0001425668,0.0004421761,0.0002916884],"domain_scores_gemma":[0.9986625,0.0005293873,0.0001261429,0.00007310476,0.0004874394,0.0001213693],"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":[0.00003467113,0.00001675385,0.0003198544,0.000009864841,0.000280096,0.00001211838,0.0001138458,0.9784886,0.01360263,0.0004050058,0.0003772351,0.006339347],"study_design_scores_gemma":[0.001790095,0.0001245772,0.0002053044,0.0001171949,0.00004160174,0.0001202122,0.00001514033,0.9954289,0.00166165,0.00008285662,0.0002347027,0.0001777545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1464753,0.00003869501,0.851457,0.0004675103,0.0006411767,0.0003384902,0.00003472252,0.0003418562,0.0002051902],"genre_scores_gemma":[0.8503757,0.0000184767,0.1467741,0.00003249212,0.002187951,0.00002206417,0.00006906227,0.0001532782,0.0003668323],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7046829,"threshold_uncertainty_score":0.714077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02058644986154604,"score_gpt":0.2619748199565826,"score_spread":0.2413883700950365,"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."}}