{"id":"W7124217846","doi":"10.1109/codit66093.2025.11321233","title":"A lexicographic bi-objective approach to fleet sizing and routing for service vehicles in a real-world passenger transport system","year":2025,"lang":"","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Lexicographical order; Software deployment; Sizing; Service (business); Public transport; Vehicle routing problem; Sustainability; Linear programming","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003045734,0.0007321319,0.001142488,0.002014136,0.0003455953,0.0002162199,0.0003650645,0.0004114471,0.000003712181],"category_scores_gemma":[0.0001576978,0.0008419197,0.0002065058,0.006785438,0.00004721624,0.0003426728,0.0001237947,0.0005910157,0.000002237356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005319342,"about_ca_system_score_gemma":0.0001707029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002126795,"about_ca_topic_score_gemma":0.002058375,"domain_scores_codex":[0.9954153,0.0003742321,0.001532517,0.001261669,0.0003054894,0.001110744],"domain_scores_gemma":[0.9975544,0.001087759,0.0001671503,0.0005755693,0.0003424863,0.0002726065],"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":[0.0002804169,0.0002229684,0.06354664,0.01007996,0.0005630746,0.000008734653,0.01102133,0.8535975,0.003130267,0.04656911,0.00002745601,0.01095259],"study_design_scores_gemma":[0.001611784,0.00005053745,0.04536367,0.002299622,0.0002647236,0.000005617028,0.008804808,0.9390419,0.001469906,0.0001236106,0.0001297586,0.0008340416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1985968,0.000471466,0.7471083,0.0005847395,0.0004727553,0.003767894,0.00004783137,0.0009375191,0.04801263],"genre_scores_gemma":[0.7431428,0.00005272872,0.2555822,0.0002326663,0.00008777712,0.0004187473,0.00001435759,0.0001213624,0.0003473707],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.544546,"threshold_uncertainty_score":0.9994032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0242239793836698,"score_gpt":0.2839181024757135,"score_spread":0.2596941230920436,"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."}}