{"id":"W2155694204","doi":"10.1287/trsc.1120.0449","title":"Analysis and Branch-and-Cut Algorithm for the Time-Dependent Travelling Salesman Problem","year":2012,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Travelling salesman problem; Branch and cut; Bottleneck traveling salesman problem; Bounding overwatch; Mathematics; 2-opt; Traveling purchaser problem; Integer programming; Tree traversal; Branch and bound; Graph; Combinatorics; Hamiltonian path; Time complexity; Combinatorial optimization; Mathematical optimization; Algorithm; Computer science","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":[0.002069143,0.001777532,0.001690762,0.001739893,0.001150632,0.002431711,0.002309026,0.0016077,0.008574202],"category_scores_gemma":[0.005580031,0.0008790151,0.001406796,0.002542613,0.0009120643,0.002463385,0.001376034,0.003130209,0.001301286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002989517,"about_ca_system_score_gemma":0.004588085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01234697,"about_ca_topic_score_gemma":0.01012945,"domain_scores_codex":[0.9987293,0.0004082121,0.00005893316,0.0002013317,0.0003546397,0.0002475882],"domain_scores_gemma":[0.9976264,0.001697666,0.000163874,0.0001277122,0.00028821,0.00009622474],"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":[0.0002257512,0.0003228787,0.0007943417,0.0002551347,0.00007870045,0.0001292002,0.0001512552,0.7663187,0.002136842,0.08008197,0.00892004,0.1405852],"study_design_scores_gemma":[0.00002793878,0.00002595585,0.0000744937,0.00001298091,0.00001409866,0.0000160834,0.00001753265,0.9749634,0.0003271697,0.02348113,0.00103326,0.000005882826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01861703,0.0005924116,0.9695013,0.0005558546,0.00006472685,0.0002242151,0.0002588667,0.0006797677,0.009505817],"genre_scores_gemma":[0.1385281,0.0007019668,0.8540403,0.0001800655,0.000110874,0.0005185584,0.00106281,0.0003837812,0.004473486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01234697,"threshold_uncertainty_score":0.02868354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563645157465395,"score_gpt":0.270342152026532,"score_spread":0.2547057004518781,"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."}}