{"id":"W2759685288","doi":"10.1287/trsc.2017.0783","title":"A Branch-and-Cut Algorithm for the Multidepot Rural Postman Problem","year":2017,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Integer programming; Traverse; Mathematical optimization; Linear programming; Extension (predicate logic); Branch and cut; Mathematics; Column generation; Heuristic; Binary number; Minimum weight; Algorithm; Set (abstract data type); Computer science; Combinatorics","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":[0.0007810095,0.001100622,0.001133992,0.0008750428,0.001039563,0.001163259,0.001572661,0.00160426,0.00906171],"category_scores_gemma":[0.00180278,0.0006953803,0.000689353,0.001501139,0.0005053978,0.001653103,0.001185129,0.001750757,0.001203227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087692,"about_ca_system_score_gemma":0.002224399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004860424,"about_ca_topic_score_gemma":0.006664798,"domain_scores_codex":[0.999449,0.0001678602,0.00002131766,0.000119574,0.0001328198,0.0001093921],"domain_scores_gemma":[0.9993958,0.0003706085,0.00005567936,0.00004124717,0.0000853617,0.00005133656],"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":[0.0002295967,0.000353814,0.0006501316,0.0002768239,0.0000617835,0.0001868656,0.0001326296,0.6397,0.002572201,0.03924316,0.01271646,0.3038766],"study_design_scores_gemma":[0.00007941601,0.0001004362,0.0001708416,0.00002334218,0.00001762629,0.000074437,0.00005623484,0.9715937,0.0009784143,0.02062756,0.006267034,0.0000109651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01271102,0.0002859942,0.9783197,0.0002877745,0.00005663191,0.0002457854,0.000218786,0.0004343857,0.007439945],"genre_scores_gemma":[0.080028,0.0002898563,0.9137589,0.0001201416,0.00003559244,0.0003241427,0.0005847104,0.0001858884,0.004672755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00906171,"threshold_uncertainty_score":0.03031451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02146653535264469,"score_gpt":0.3028421713022516,"score_spread":0.2813756359496069,"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."}}