{"id":"W2064155562","doi":"10.1109/jproc.2011.2158181","title":"Dynamic Vehicle Routing for Robotic Systems","year":2011,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":222,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Robotics; Variety (cybernetics); Routing (electronic design automation); Queueing theory; Distributed computing; Process (computing); Service (business); Vehicle routing problem; Quality of service; Mathematical optimization; Robot; Operations research; Artificial intelligence; Computer network; Engineering","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":[0.0006267339,0.0009566507,0.0006266608,0.00061488,0.000442792,0.001184817,0.0009173173,0.0007661763,0.003242707],"category_scores_gemma":[0.002144064,0.0003117652,0.0003907119,0.001034799,0.0007626819,0.00110121,0.001030174,0.001211642,0.0007787481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001628915,"about_ca_system_score_gemma":0.00126998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002528242,"about_ca_topic_score_gemma":0.00230911,"domain_scores_codex":[0.9994548,0.0001892956,0.00002505133,0.00009632546,0.0001926988,0.00004193033],"domain_scores_gemma":[0.9995308,0.0002715532,0.00005486161,0.00003784846,0.00008662343,0.00001841945],"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":[0.00001676879,0.00001988484,0.0001380668,0.0002039591,0.00002099897,0.00004956796,0.00006634039,0.4755617,0.001185069,0.4471866,0.005318645,0.07023232],"study_design_scores_gemma":[0.000009260583,0.00002164067,0.00008994085,0.00003860391,0.000006311768,0.00005212232,0.00003059,0.7067744,0.0002783963,0.2661162,0.02657005,0.00001247452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003016315,0.005014517,0.976187,0.0009057946,0.0002114187,0.00004686476,0.00007756017,0.0001889111,0.01435172],"genre_scores_gemma":[0.4775108,0.02146061,0.4688991,0.0005985678,0.00116477,0.0007231014,0.000522295,0.0002576277,0.02886319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003242707,"threshold_uncertainty_score":0.01181871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02758031398550404,"score_gpt":0.2406828134075412,"score_spread":0.2131024994220372,"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."}}