{"id":"W4383070024","doi":"10.1016/j.cor.2023.106338","title":"Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Computer science; Context (archaeology); Markov decision process; Mathematical optimization; Set (abstract data type); Variable (mathematics); Dynamic programming; State variable; Routing (electronic design automation); Markov process; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002057266,0.001147548,0.002038683,0.0007190004,0.0004958509,0.001677222,0.001709183,0.001817052,0.003358056],"category_scores_gemma":[0.007640521,0.001188601,0.0008832685,0.001309674,0.001073369,0.001851593,0.001142689,0.001936586,0.0003291819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179966,"about_ca_system_score_gemma":0.001915211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01216059,"about_ca_topic_score_gemma":0.009951784,"domain_scores_codex":[0.9988267,0.0005852466,0.00003367885,0.0001407247,0.0002304115,0.0001833012],"domain_scores_gemma":[0.9960579,0.003148153,0.0002512439,0.0001325321,0.0002816303,0.0001285187],"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":[0.00004390324,0.00002709812,0.0001122013,0.00002111638,0.000009328303,0.00001605427,0.000009788246,0.9921633,0.0001022435,0.003792468,0.0003042085,0.003398188],"study_design_scores_gemma":[0.000002878521,0.000006982678,0.00001602457,0.000001202752,0.000001247744,0.000002384735,0.000002478836,0.9984629,0.00001995642,0.001438344,0.00004442596,0.000001156796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05419,0.0003184876,0.9400373,0.0005867527,0.00005910798,0.00008486156,0.0002324361,0.0001710947,0.004319999],"genre_scores_gemma":[0.8558604,0.0004530094,0.1333179,0.0001916006,0.0001133028,0.0002786103,0.0005436615,0.0001711845,0.009070394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01216059,"threshold_uncertainty_score":0.02417964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03419786777119948,"score_gpt":0.3140108607773663,"score_spread":0.2798129930061668,"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."}}