{"id":"W4400046206","doi":"10.3390/en17133149","title":"Enhancing Electric Shuttle Bus Efficiency: A Case Study on Timetabling and Scheduling Optimization","year":2024,"lang":"en","type":"article","venue":"Energies","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Concordia University; Group for Research in Decision Analysis","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"Scheduling (production processes); Computer science; Operations research; Mathematical optimization; Automotive engineering; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001272829,0.0007233114,0.0003995938,0.0006188137,0.0008838308,0.0007599841,0.000823219,0.0007523391,0.002395515],"category_scores_gemma":[0.002132396,0.0002178205,0.0006303336,0.001264254,0.0004571089,0.0006681045,0.0004931453,0.0006610013,0.0001570503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002670118,"about_ca_system_score_gemma":0.002094294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04811209,"about_ca_topic_score_gemma":0.07056946,"domain_scores_codex":[0.9992127,0.0003597633,0.00002293411,0.00006102753,0.0001728259,0.0001706684],"domain_scores_gemma":[0.9983754,0.0009489509,0.00010883,0.00008352404,0.0003110583,0.0001721664],"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":[0.0004900293,0.001149139,0.01166929,0.0003796007,0.0000763601,0.002436237,0.0007077156,0.8972109,0.006665565,0.01016379,0.004796841,0.06425443],"study_design_scores_gemma":[0.0001757835,0.001010167,0.008076452,0.00003217824,0.00005952027,0.0002992047,0.001797555,0.9705784,0.006669376,0.001774207,0.009489651,0.0000374699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.949158,0.0003960803,0.0354027,0.0005551099,0.00004850562,0.0002979285,0.0002914122,0.0001609192,0.01368923],"genre_scores_gemma":[0.9664188,0.0003045782,0.02916827,0.00002958534,0.00001673599,0.00009136212,0.0002252354,0.00003752875,0.003708042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04811209,"threshold_uncertainty_score":0.09566408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005405818731516716,"score_gpt":0.2146052261895435,"score_spread":0.2091994074580268,"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."}}