{"id":"W4280650648","doi":"10.18280/jesa.550215","title":"Joint Scheduling of Charging and Service Operation of Electric Taxi Based on Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Xiamen University; Xiamen University of Technology","keywords":"Taxis; Scheduling (production processes); Reinforcement learning; Computer science; Charging station; Electricity; Grid; Computer network; Real-time computing; Electric vehicle; Automotive engineering; Transport engineering; Engineering; Power (physics); Electrical engineering; Operations management","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":[0.0005803558,0.0005091063,0.0006661926,0.0002373608,0.000372455,0.0004978001,0.0008041606,0.0004352995,0.0009917666],"category_scores_gemma":[0.001356732,0.0002148296,0.0002886631,0.0002197832,0.0005209431,0.0004213752,0.0004978864,0.0005942443,0.0001373059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007233543,"about_ca_system_score_gemma":0.001426017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01091889,"about_ca_topic_score_gemma":0.007086897,"domain_scores_codex":[0.9996575,0.00006927038,0.00001900264,0.00009305842,0.0000753008,0.00008590867],"domain_scores_gemma":[0.9994338,0.0001931851,0.0001083839,0.00003805869,0.0001448037,0.00008176595],"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":[0.0001265423,0.0001268418,0.001958604,0.00003355456,0.00003342705,0.00007799138,0.00005637333,0.9472739,0.003685581,0.001961173,0.0005398123,0.04412631],"study_design_scores_gemma":[0.00001016038,0.00002865646,0.0001663385,0.000001110801,0.000004480458,0.000006415475,0.000003906978,0.9989974,0.0003748187,0.0003216512,0.000082169,0.000002934522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1452158,0.0001942817,0.8493112,0.0001894318,0.00006516478,0.0001169031,0.00003068599,0.00064246,0.004234157],"genre_scores_gemma":[0.9868718,0.00003644443,0.01222477,0.00002457314,0.00001079913,0.00004645683,0.0000196916,0.000007906656,0.0007575138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01091889,"threshold_uncertainty_score":0.02171063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676037080870544,"score_gpt":0.2255580040704159,"score_spread":0.2087976332617105,"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."}}