{"id":"W2795898573","doi":"10.1007/978-3-319-89656-4_27","title":"Optimal Scheduling for Smart Charging of Electric Vehicles Using Dynamic Programming","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Mitacs","keywords":"Computer science; Dynamic programming; Schedule; Electricity; Scheduling (production processes); Operations research; Mathematical optimization; Real-time computing; Electrical engineering; Algorithm; Operating system; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038514,0.000419195,0.0004941264,0.0006987677,0.0001597401,0.0001298569,0.000663841,0.0003279609,0.0000153195],"category_scores_gemma":[0.00003402878,0.0004142455,0.000138201,0.0005262906,0.0002297897,0.0002062247,0.000125124,0.0005603103,0.000001772521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003319133,"about_ca_system_score_gemma":0.0001793129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004972645,"about_ca_topic_score_gemma":0.000009231273,"domain_scores_codex":[0.9978672,0.000006769475,0.0004666049,0.0005889749,0.0003762739,0.0006942261],"domain_scores_gemma":[0.9990441,0.0001242682,0.000168377,0.0003665623,0.000212952,0.00008373129],"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":[0.000007901792,0.000005311191,0.00003323649,0.0002225731,0.00002717769,0.000004033778,0.0002657435,0.5667722,0.02082175,0.0001532585,0.00000221934,0.4116846],"study_design_scores_gemma":[0.0001722449,0.000147086,0.00002306839,0.0003818473,0.00002379308,0.00003181521,3.801146e-7,0.9722397,0.01900244,0.007358704,0.0002024686,0.0004164019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06701173,0.001117073,0.9307671,0.00001382822,0.0004348271,0.0004337165,0.000005724045,0.0001267987,0.00008924923],"genre_scores_gemma":[0.5116847,0.00003088712,0.4878201,0.00003328092,0.0003333292,0.000005462822,0.000006370904,0.00007154771,0.00001433274],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4446729,"threshold_uncertainty_score":0.999831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.010267669129534,"score_gpt":0.2307428047396111,"score_spread":0.2204751356100771,"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."}}