{"id":"W2741021238","doi":"10.1287/trsc.2017.0798","title":"Branch-and-Price for the Pickup and Delivery Problem with Time Windows and Scheduled Lines","year":2018,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"TKI DINALOG","keywords":"Pickup; Vehicle routing problem; Mathematical optimization; Computer science; Scheduling (production processes); Set (abstract data type); Path (computing); Shortest path problem; Longest path problem; Public transport; Job shop scheduling; Column generation; Routing (electronic design automation); Mathematics; Theoretical computer science; Engineering; Transport engineering; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004851352,0.00007209401,0.00007264516,0.00004834322,0.0002196587,0.00006265652,0.00007309503,0.00002148522,0.00000678217],"category_scores_gemma":[0.00002214512,0.00005042084,0.000006142654,0.0002980665,0.0003835633,0.0002907911,0.000002587714,0.00003892954,0.000001124004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005830149,"about_ca_system_score_gemma":0.00002706852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006792389,"about_ca_topic_score_gemma":0.00002325051,"domain_scores_codex":[0.9994624,0.00000713045,0.0001126992,0.000165611,0.0001213225,0.0001308651],"domain_scores_gemma":[0.9995832,0.0001273336,0.00002660842,0.00008014635,0.0001321582,0.00005056697],"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":[0.0003316809,0.00004969748,0.1114515,0.000717337,0.0001231837,0.000002632866,0.0319073,0.4015771,0.3004611,0.006830064,0.0001153124,0.1464331],"study_design_scores_gemma":[0.0007773939,0.00009687788,0.2151015,0.00004449252,0.00004086028,0.000005971677,0.0001071074,0.7736349,0.009464159,0.0001801113,0.0003641831,0.0001825001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.650389,0.0001115455,0.3489808,0.0001204903,0.00002311761,0.0002235597,0.000004115087,0.00006579029,0.00008161203],"genre_scores_gemma":[0.84604,0.00004010665,0.1537809,0.00004806746,0.00002492776,0.00001596501,0.000001398323,0.00000896169,0.00003970632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3720577,"threshold_uncertainty_score":0.2056102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470324862353725,"score_gpt":0.2542139529624715,"score_spread":0.2395107043389342,"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."}}