{"id":"W3199472824","doi":"","title":"Off-line approximate dynamic programming for the vehicle routing problem with stochastic customers and demands via decentralized decision-making","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Markov decision process; Computer science; Mathematical optimization; Vehicle routing problem; Reinforcement learning; Heuristic; Benchmark (surveying); Dynamic programming; Set (abstract data type); Routing (electronic design automation); Dimension (graph theory); Stochastic programming; Operations research; Markov process; Artificial intelligence; Mathematics; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001988457,0.0002638859,0.0002671303,0.0001286141,0.0002385984,0.0001027983,0.0002149908,0.0001462178,0.000008768877],"category_scores_gemma":[0.00002446525,0.0002395015,0.00009422546,0.0004877005,0.00009303071,0.0001346557,0.00007298698,0.0003586878,6.969637e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001315558,"about_ca_system_score_gemma":0.00007122083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000180729,"about_ca_topic_score_gemma":0.0004991931,"domain_scores_codex":[0.9988574,0.00001711399,0.0002601845,0.0004649961,0.00007592078,0.0003243788],"domain_scores_gemma":[0.9990302,0.0002697092,0.0001146867,0.0003383159,0.0001783112,0.00006878673],"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":[0.00007118734,0.00003163341,0.0007149616,0.0002307975,0.0002132819,0.00001490826,0.0004821478,0.9803334,0.00004697604,0.001679106,0.000001662593,0.0161799],"study_design_scores_gemma":[0.0008168861,0.00002339956,0.001347466,0.0003939311,0.0003168026,0.000004695165,0.0008523353,0.995205,0.00001087172,0.0006636517,0.00006947942,0.000295501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4019682,0.0001268363,0.596927,0.00001636588,0.00008216116,0.0006762535,0.00001589056,0.0001706255,0.00001665033],"genre_scores_gemma":[0.98997,0.0001044315,0.009727373,0.00001641251,0.00001253054,0.00002164523,0.0000795441,0.000045329,0.00002267872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5880018,"threshold_uncertainty_score":0.9766589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02276891013377397,"score_gpt":0.2002714649641362,"score_spread":0.1775025548303622,"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."}}