{"id":"W4224281875","doi":"10.1109/sm55505.2022.9758346","title":"Multi-criteria Optimal Routing for Last-mile Parcel Delivery","year":2022,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Motors (Canada); University of Toronto","funders":"","keywords":"Simulated annealing; Travelling salesman problem; Computer science; Genetic algorithm; Metaheuristic; Mathematical optimization; Cluster analysis; Vehicle routing problem; Routing (electronic design automation); Adaptive simulated annealing; Algorithm; Artificial intelligence; Mathematics; Computer network","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":[0.000743366,0.000848232,0.0009052503,0.0007686851,0.0006566816,0.00105169,0.00118951,0.0008033848,0.003810544],"category_scores_gemma":[0.001152085,0.0003425567,0.0007637296,0.00124829,0.0003680954,0.0007366384,0.0005850758,0.0006110723,0.0004215558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001417683,"about_ca_system_score_gemma":0.0009671628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005741864,"about_ca_topic_score_gemma":0.005280941,"domain_scores_codex":[0.99931,0.000292973,0.00002080625,0.0001032215,0.000166676,0.0001062537],"domain_scores_gemma":[0.9996593,0.0001501837,0.00004985748,0.00002751515,0.00006851293,0.00004464552],"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":[0.00005390018,0.00004096625,0.0002996473,0.00007765544,0.00003532565,0.0001179396,0.00003872914,0.9592717,0.002066177,0.009231566,0.001638327,0.02712821],"study_design_scores_gemma":[0.000004868515,0.00004128068,0.00013411,0.000004840316,0.000006262532,0.00003495896,0.00002247939,0.9946924,0.0005259676,0.002863284,0.001664116,0.000005489104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02693495,0.0005091259,0.9653815,0.0001907858,0.00007087056,0.00009398248,0.0001632756,0.0003334475,0.006322075],"genre_scores_gemma":[0.7304226,0.0004456668,0.2606033,0.00009031167,0.00003603382,0.0001875752,0.0003649007,0.0001930099,0.00765658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005741864,"threshold_uncertainty_score":0.01274759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150068932371387,"score_gpt":0.2871309209264886,"score_spread":0.2556302316027748,"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."}}