{"id":"W2165941447","doi":"10.1016/j.cor.2012.04.003","title":"Lower and upper bounds for the two-echelon capacitated location-routing problem","year":2012,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund","keywords":"Heuristics; Mathematical optimization; Computer science; Vehicle routing problem; Heuristic; Upper and lower bounds; Branch and bound; Routing (electronic design automation); Set (abstract data type); Neighbourhood (mathematics); 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":[0.002508827,0.0001188597,0.0001092017,0.0001584026,0.0008930481,0.0003700337,0.000209911,0.00006287257,0.0000188641],"category_scores_gemma":[0.000265487,0.00009791114,0.00002805122,0.0006466915,0.000129513,0.0003459399,0.00008323182,0.0003148741,0.00002141965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001188488,"about_ca_system_score_gemma":0.00005801924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000503619,"about_ca_topic_score_gemma":0.00001910493,"domain_scores_codex":[0.9986535,0.0002027788,0.0002312925,0.000173253,0.0002397548,0.0004994661],"domain_scores_gemma":[0.9982516,0.0008532175,0.000009524396,0.0002922952,0.0004824128,0.000110976],"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":[0.000004798086,0.00003042627,0.0006818323,0.0000453085,0.00004811453,1.774881e-7,0.003047946,0.9700231,0.001518198,0.006837207,0.002137025,0.01562587],"study_design_scores_gemma":[0.0002949612,0.00002416525,0.0008328829,0.00003188168,0.00000818542,0.0000066593,0.0002826512,0.9946434,0.0006509349,0.00004342612,0.003058378,0.0001224509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03701491,0.0006217326,0.9595526,0.0008397704,0.0004091489,0.0008115924,0.000005032779,0.0001765969,0.0005686469],"genre_scores_gemma":[0.7477053,0.00003193211,0.251486,0.00005166091,0.0002446765,0.0001761679,0.00001706543,0.00003611097,0.0002511448],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7106904,"threshold_uncertainty_score":0.6868694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07168323218806766,"score_gpt":0.3734654080494779,"score_spread":0.3017821758614102,"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."}}