{"id":"W4387087085","doi":"10.1016/j.ejor.2023.09.031","title":"Multi-attribute two-echelon location routing: Formulation and dynamic discretization discovery approach","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal; Transport Canada","funders":"","keywords":"Computer science; Scheduling (production processes); Vehicle routing problem; Transshipment (information security); Routing (electronic design automation); Discretization; Integer programming; Operations research; Synchronization (alternating current); Job shop scheduling; Mathematical optimization; Distributed computing; Computer network; Mathematics; Algorithm","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":[0.002079026,0.0006153367,0.001751554,0.001009158,0.0006384224,0.002294819,0.00308316,0.002104747,0.002815414],"category_scores_gemma":[0.004572813,0.001021179,0.001357202,0.001863408,0.001133985,0.002259232,0.001943295,0.001701547,0.0002698365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002024958,"about_ca_system_score_gemma":0.001696293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01058643,"about_ca_topic_score_gemma":0.007591256,"domain_scores_codex":[0.9990886,0.0003358205,0.00004264278,0.0002043389,0.0002178342,0.0001106905],"domain_scores_gemma":[0.9980397,0.001323655,0.0001693744,0.0001186427,0.0002598906,0.00008877728],"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":[0.00001976453,0.0000302439,0.0003333399,0.00004400999,0.00002149622,0.00005270965,0.00002954206,0.9714596,0.0002582539,0.0202465,0.0006304045,0.006874136],"study_design_scores_gemma":[0.000001247733,0.000002622005,0.00001966879,0.000002074489,0.000001939525,0.000005126166,0.000005054915,0.9976557,0.00003006171,0.002159353,0.0001152603,0.000001679338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008876169,0.0003007006,0.9873508,0.0004735664,0.00005717345,0.00003913284,0.0001082571,0.00005419757,0.002740025],"genre_scores_gemma":[0.6323116,0.0007527728,0.358691,0.0002623542,0.0001710481,0.0002372547,0.0004066363,0.00007669861,0.00709068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01058643,"threshold_uncertainty_score":0.02104962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08653340626897581,"score_gpt":0.3732754240698392,"score_spread":0.2867420178008633,"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."}}