{"id":"W2142169698","doi":"10.1287/trsc.2014.0525","title":"Service Network Design with Resource Constraints","year":2014,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Infrastructure Canada; Compute Canada","keywords":"Column generation; Solver; Benchmark (surveying); Heuristic; Network planning and design; Computer science; Service (business); Consolidation (business); Quality of service; Operations research; Mathematical optimization; Computer network; Engineering; Business; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001146005,0.00008912999,0.00008882144,0.00005725257,0.0001361982,0.00004368006,0.000201533,0.00002794958,0.00004251679],"category_scores_gemma":[0.0000217092,0.00008254968,0.000009757438,0.001210272,0.0001962694,0.0002189255,0.000001245841,0.00007757734,0.00001633717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001930523,"about_ca_system_score_gemma":0.00004412231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000364871,"about_ca_topic_score_gemma":0.00001625636,"domain_scores_codex":[0.9990802,0.00003781524,0.0001587991,0.000182341,0.0002945513,0.0002463025],"domain_scores_gemma":[0.9994856,0.0001077049,0.00003261075,0.0001690385,0.0001113709,0.00009367211],"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":[0.000004251299,0.000002021401,0.0007648841,0.00000951937,0.000001942657,5.756354e-7,0.0004575822,0.9915117,0.001043911,0.0008559976,0.00004389681,0.005303719],"study_design_scores_gemma":[0.0003341243,0.00003067131,0.02631764,0.00004297202,0.00001309605,0.000003615636,0.0001097123,0.9675389,0.004051827,0.0001605689,0.0011844,0.0002124572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04155078,0.000006407105,0.9554045,0.00006724775,0.0000588303,0.0001054935,0.000001357379,0.0003235554,0.002481837],"genre_scores_gemma":[0.6329598,9.921453e-7,0.3668183,0.0001692273,0.00002251877,0.000004879613,0.00000323474,0.00001154025,0.000009492124],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.591409,"threshold_uncertainty_score":0.3366278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02109923420486267,"score_gpt":0.2463486574531667,"score_spread":0.225249423248304,"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."}}