{"id":"W4408221131","doi":"10.1145/3721983","title":"SED2AM: Solving Multi-Trip Time-Dependent Vehicle Routing Problem Using Deep Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Vehicle routing problem; Computer science; Routing (electronic design automation); Artificial intelligence; Reinforcement; Mathematical optimization; Machine learning; Mathematics; Engineering; Computer network","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000648618,0.0003723897,0.0003956702,0.0003871805,0.0005628671,0.0004154977,0.001495642,0.0001783062,0.0001625558],"category_scores_gemma":[0.000153267,0.0004083715,0.0001099839,0.0006335167,0.00005278358,0.001258114,0.0002086543,0.0008489722,0.0004159987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006060851,"about_ca_system_score_gemma":0.000161879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005649583,"about_ca_topic_score_gemma":0.0004061587,"domain_scores_codex":[0.9974098,0.0001805008,0.0006323584,0.0007394305,0.0003785205,0.0006594099],"domain_scores_gemma":[0.9971582,0.0005315887,0.00006946154,0.002044558,0.00008554073,0.0001106622],"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":[0.00004730753,0.0001910545,0.00118794,0.0002534302,0.0005918757,0.00001168034,0.0009142538,0.931405,0.04156744,0.00001241007,0.0003453953,0.02347224],"study_design_scores_gemma":[0.001009894,0.00002723444,0.0002605358,0.0007102435,0.000120671,0.000002839489,0.0004104542,0.9825137,0.01224628,0.00002371706,0.002275222,0.0003991825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03606544,0.0008615599,0.9587902,0.00003335871,0.0008935205,0.0006405949,0.00015437,0.0005642659,0.001996735],"genre_scores_gemma":[0.9896431,0.00007387958,0.00546591,0.00001298159,0.0001374647,0.00006211149,0.0003804405,0.00009589556,0.004128198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9535777,"threshold_uncertainty_score":0.9998368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04577542143170239,"score_gpt":0.3068384094486623,"score_spread":0.2610629880169599,"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."}}