{"id":"W2915502992","doi":"10.20944/preprints201902.0183.v1","title":"Two-Echelon Routing Problem for Parcel Delivery by Cooperated Truck and Drone","year":2019,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Distinguished Young Scholar Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Drone; Truck; Simulated annealing; Routing (electronic design automation); Tabu search; Computer science; Vehicle routing problem; Heuristic; Process (computing); Operations research; Transport engineering; Engineering; Computer network; Artificial intelligence; Automotive engineering; 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":[0.001693266,0.001633312,0.002344987,0.00101286,0.0008899918,0.002112886,0.002459359,0.002378876,0.00699648],"category_scores_gemma":[0.002331218,0.0008323062,0.001567269,0.001982104,0.0007587528,0.001913692,0.001328674,0.001327029,0.0003985332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00232539,"about_ca_system_score_gemma":0.001640063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01230297,"about_ca_topic_score_gemma":0.009171264,"domain_scores_codex":[0.9988484,0.0004405197,0.00004757009,0.0003266102,0.0001384612,0.0001982718],"domain_scores_gemma":[0.9988569,0.0007132896,0.0001321545,0.00006855561,0.0001049039,0.000124185],"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":[0.00006283605,0.00005831855,0.0003771152,0.0001225158,0.00005362145,0.000198471,0.00003705574,0.9848031,0.0005519654,0.006571335,0.0008460681,0.006317677],"study_design_scores_gemma":[0.00002494892,0.00005263434,0.0002154993,0.000006443585,0.0000155511,0.00006014918,0.00005531672,0.9945424,0.0002160273,0.003846113,0.0009553429,0.000009514867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1651073,0.001397872,0.8087403,0.001208907,0.0002624289,0.0005212971,0.001648881,0.0003295505,0.02078354],"genre_scores_gemma":[0.8207936,0.0009000894,0.1593183,0.0001895513,0.0001070683,0.0006238633,0.001358092,0.0001603952,0.01654911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01230297,"threshold_uncertainty_score":0.0244627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06495944926391745,"score_gpt":0.320628277966875,"score_spread":0.2556688287029575,"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."}}