{"id":"W4214807431","doi":"10.1016/j.cor.2022.105761","title":"The doubly open park-and-loop routing problem","year":2022,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"HEC Montréal; Canada First Research Excellence Fund","keywords":"Vehicle routing problem; Computer science; Routing (electronic design automation); Heuristic; Extension (predicate logic); Mathematical optimization; Set (abstract data type); Quality (philosophy); Loop (graph theory); Operations research; Mathematics; Artificial intelligence; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007667007,0.0005892461,0.0009629705,0.0004421664,0.0005730128,0.001693648,0.001076206,0.001937679,0.007563298],"category_scores_gemma":[0.003663287,0.0004485524,0.0005283507,0.000593649,0.001109437,0.002502796,0.001709296,0.001173226,0.0004785385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000516821,"about_ca_system_score_gemma":0.0008309805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001400636,"about_ca_topic_score_gemma":0.001467517,"domain_scores_codex":[0.9994685,0.0002180753,0.00001900818,0.0001296805,0.00008100875,0.00008372569],"domain_scores_gemma":[0.9987716,0.0007951206,0.00013425,0.00007570448,0.00009503787,0.0001282819],"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":[0.0004020355,0.0002607076,0.001300745,0.0003003047,0.00006682723,0.0004687947,0.0001590793,0.5668921,0.001924459,0.3464566,0.0103612,0.07140712],"study_design_scores_gemma":[0.00007811784,0.0001128626,0.0002541622,0.00002124805,0.0000230823,0.0001573224,0.0001171273,0.808763,0.0005905327,0.1839893,0.005872325,0.00002089412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1204605,0.0009838078,0.8376192,0.002277402,0.0003063395,0.0001236995,0.0007245812,0.000128387,0.03737605],"genre_scores_gemma":[0.8737066,0.0009801219,0.09399799,0.0003072861,0.0002731163,0.0001404839,0.0006750222,0.00009456466,0.02982479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007563298,"threshold_uncertainty_score":0.02530175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0847938384632459,"score_gpt":0.383109768783554,"score_spread":0.2983159303203081,"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."}}