{"id":"W2673247911","doi":"10.23977/iotea.2016.11003","title":"Study of logistics distribution route based on improved genetic algorithm and ant colony optimization algorithm","year":2016,"lang":"en","type":"article","venue":"Internet of Things (IoT) and Engineering Applications","topic":"Modeling, Simulation, and Optimization","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing University of Posts and Telecommunications","keywords":"Ant colony optimization algorithms; Vehicle routing problem; Genetic algorithm; Algorithm; Mathematical optimization; Meta-optimization; Computer science; Ant colony; Distribution (mathematics); Metaheuristic; Routing (electronic design automation); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005806668,0.0006656973,0.0007860104,0.001061767,0.0004282091,0.0009911734,0.0009305998,0.0008702269,0.001233139],"category_scores_gemma":[0.001365689,0.0003913836,0.0009552603,0.001467276,0.0005252946,0.00177179,0.0004342528,0.0007514345,0.0001889666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008300422,"about_ca_system_score_gemma":0.001235403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009262551,"about_ca_topic_score_gemma":0.003866956,"domain_scores_codex":[0.9995302,0.000130669,0.00002043547,0.00007885938,0.0001961157,0.00004366315],"domain_scores_gemma":[0.9996951,0.000132525,0.00003340807,0.0000183779,0.0001083079,0.00001238876],"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":[0.00002899117,0.00002846486,0.0009123574,0.0001308498,0.00006256344,0.0001091198,0.00007677914,0.9087212,0.001700699,0.04015154,0.001179402,0.04689804],"study_design_scores_gemma":[0.000006495267,0.00001856466,0.0001662797,0.000006887246,0.00001069522,0.00004043723,0.00001784924,0.9934056,0.0003214857,0.004505222,0.001493349,0.000007122751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0183554,0.001873517,0.9710255,0.0003328334,0.0001282527,0.00004169642,0.00002332417,0.0001306717,0.008088807],"genre_scores_gemma":[0.6668113,0.005719313,0.317147,0.0001458166,0.0002038567,0.0001841241,0.0001549296,0.0001447948,0.009488793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009262551,"threshold_uncertainty_score":0.0184173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0141419745912968,"score_gpt":0.2469556206708999,"score_spread":0.2328136460796031,"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."}}