{"id":"W4220659795","doi":"10.1287/trsc.2022.1135","title":"A Branch-and-Price-and-Cut Algorithm for the Vehicle Routing Problem with Two-Dimensional Loading Constraints","year":2022,"lang":"en","type":"article","venue":"Transportation Science","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Transport Canada","funders":"","keywords":"Vehicle routing problem; Branch and cut; Benchmark (surveying); Mathematical optimization; Relaxation (psychology); Constraint (computer-aided design); Algorithm; Routing (electronic design automation); Mathematics; Computer science; Integer programming","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":[0.00106354,0.001550592,0.001941555,0.001377323,0.001097922,0.001621507,0.00158564,0.002025593,0.00792685],"category_scores_gemma":[0.00327839,0.0008090945,0.001087817,0.002901727,0.0005686317,0.001724532,0.001262965,0.002812423,0.001115498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456175,"about_ca_system_score_gemma":0.003827148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007493631,"about_ca_topic_score_gemma":0.006625161,"domain_scores_codex":[0.9992115,0.0002152773,0.00003599195,0.0001812502,0.000223663,0.0001322768],"domain_scores_gemma":[0.9988788,0.0006921759,0.00009187045,0.00009263666,0.0001658308,0.00007864926],"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":[0.0001388561,0.0003238009,0.0005377929,0.0002519321,0.00005976974,0.0001255099,0.00007387796,0.7185763,0.001791249,0.02061239,0.01163175,0.2458769],"study_design_scores_gemma":[0.00004780931,0.00006170692,0.00009747939,0.00001181217,0.00001160303,0.00005048129,0.00001940208,0.9899079,0.0004354095,0.007614166,0.001732799,0.000009398136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0148026,0.0005769447,0.9744342,0.000410472,0.0001377936,0.0003792703,0.0002936092,0.0007827188,0.008182224],"genre_scores_gemma":[0.1212709,0.0005992773,0.8720005,0.0001958057,0.00009652163,0.0007118848,0.001051288,0.0002717758,0.003802116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00792685,"threshold_uncertainty_score":0.02651799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01294203882816767,"score_gpt":0.2336807899293657,"score_spread":0.220738751101198,"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."}}