{"id":"W4297145754","doi":"10.1155/2022/5052897","title":"The Value of Preemptive Pick-Up Services in Dynamic Vehicle Routing for Last-Mile Delivery: Space-Time Network-Based Formulation and Solution Algorithms","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hohai University; Arizona State University","keywords":"Computer science; Service (business); Lagrangian relaxation; Process (computing); Last mile (transportation); Task (project management); Vehicle routing problem; Dynamic programming; Flow network; Routing (electronic design automation); Service delivery framework; Operations research; Distributed computing; Algorithm; Mathematical optimization; Computer network; Engineering; Mile","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":[0.001694459,0.001565501,0.001401669,0.001059158,0.0007174436,0.002101999,0.001762882,0.002146501,0.002490437],"category_scores_gemma":[0.003700303,0.0008665125,0.001040871,0.001493657,0.001232535,0.001585989,0.001371904,0.002004457,0.0002614858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002953917,"about_ca_system_score_gemma":0.002950229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01627183,"about_ca_topic_score_gemma":0.01123242,"domain_scores_codex":[0.9994528,0.0002214445,0.0000153554,0.00008447434,0.0001296063,0.00009626264],"domain_scores_gemma":[0.9987692,0.0008966122,0.0001062973,0.00002326036,0.0001402868,0.00006428968],"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":[0.000009764122,0.00001103287,0.00006944998,0.00002869052,0.000006003328,0.00001770629,0.00001156298,0.9891934,0.00008592948,0.007345794,0.000269258,0.002951382],"study_design_scores_gemma":[0.000001694425,0.000004272209,0.00001133178,0.000003945089,0.000002119235,0.00000284623,0.000005709327,0.997707,0.00002846989,0.002056155,0.000175022,0.000001437733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009670503,0.001319464,0.9818766,0.0006265115,0.0000914242,0.00008266604,0.00007488045,0.00008827172,0.006169557],"genre_scores_gemma":[0.6745657,0.004142561,0.3072089,0.0003316485,0.0003288909,0.0006577768,0.0002972166,0.0002029045,0.0122643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01627183,"threshold_uncertainty_score":0.03235424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00563360387497039,"score_gpt":0.2381974020183943,"score_spread":0.2325637981434239,"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."}}