{"id":"W4378219327","doi":"10.1287/ijoc.2022.0185","title":"Optimising Electric Vehicle Charging Station Placement Using Advanced Discrete Choice Models","year":2023,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Université de Montréal","funders":"","keywords":"Heuristics; Computer science; Mathematical optimization; Heuristic; Greedy algorithm; Bilevel optimization; Operations research; Electric vehicle; Charging station; Greedy randomized adaptive search procedure; Frame (networking); Optimization problem; Algorithm; Artificial intelligence; Engineering; Mathematics; Power (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001855376,0.001209109,0.001639794,0.0008023989,0.0004135181,0.002273754,0.001450991,0.001824637,0.005507952],"category_scores_gemma":[0.004565801,0.001225191,0.001562593,0.001431429,0.001033451,0.001394864,0.00136985,0.001792385,0.0005037788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002137829,"about_ca_system_score_gemma":0.002085418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008155492,"about_ca_topic_score_gemma":0.007396489,"domain_scores_codex":[0.9988244,0.0005820584,0.00003772312,0.0001750141,0.0002176446,0.0001631488],"domain_scores_gemma":[0.9973979,0.002014919,0.0002244455,0.00007543903,0.0001605368,0.0001267457],"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":[0.00001194033,0.00001029355,0.000118279,0.00002618075,0.000008408605,0.00001996614,0.00001312649,0.9924201,0.00009458924,0.005314608,0.0001773985,0.001785105],"study_design_scores_gemma":[0.000007301544,0.00001097211,0.00003662901,0.000006161225,0.000003621054,0.000005550364,0.000007782088,0.9949244,0.000067762,0.004509095,0.0004172531,0.000003406379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01972912,0.0003688039,0.9719021,0.0004747294,0.00005578428,0.0001094715,0.0002985725,0.0001172852,0.006944036],"genre_scores_gemma":[0.6877187,0.001024392,0.2951554,0.0001795588,0.00008098677,0.0006035364,0.0004888617,0.0001471196,0.01460138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008155492,"threshold_uncertainty_score":0.01842594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01585056574937413,"score_gpt":0.2570950592293931,"score_spread":0.241244493480019,"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."}}