{"id":"W4391720525","doi":"10.1016/j.trc.2024.104516","title":"A column-generation matheuristic approach for optimizing first-mile ridesharing services with publicly- and privately-owned autonomous vehicles","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Public transport; Mile; Reservation; Transport engineering; Schedule; Operations research; Computer science; Integer programming; Column generation; Scheduling (production processes); Fleet management; TRIPS architecture; Last mile (transportation); Service quality; Service (business); Engineering; Business; Operations management; Computer network; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001860934,0.001815669,0.002695452,0.001687072,0.001088196,0.002422269,0.003007435,0.002959793,0.01550497],"category_scores_gemma":[0.005856431,0.001589533,0.001503072,0.001757539,0.001691889,0.001717062,0.00218088,0.001936773,0.001162982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002102943,"about_ca_system_score_gemma":0.003238174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03717649,"about_ca_topic_score_gemma":0.03728708,"domain_scores_codex":[0.9990749,0.0003876901,0.00002838491,0.0001431207,0.0001567251,0.0002092],"domain_scores_gemma":[0.9962675,0.002781081,0.0001827704,0.0001062783,0.0004636762,0.0001987097],"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":[0.00005622724,0.00003985151,0.0002865505,0.00003949465,0.00002662598,0.00005623904,0.00002035631,0.9885163,0.0001039738,0.004177438,0.001620088,0.005056916],"study_design_scores_gemma":[0.000007736326,0.00001068061,0.00004196681,0.000003853587,0.000004593322,0.000004575995,0.00001072715,0.9978232,0.00003045418,0.001903585,0.0001553199,0.000003342287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06571661,0.001117398,0.8938459,0.001903189,0.0003879151,0.0003329958,0.00147908,0.0009151417,0.03430175],"genre_scores_gemma":[0.8024251,0.0003812424,0.1741674,0.000956221,0.0002518066,0.0003825601,0.001190103,0.000378538,0.01986696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03717649,"threshold_uncertainty_score":0.07392019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05906875228969753,"score_gpt":0.3015748444004929,"score_spread":0.2425060921107953,"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."}}