{"id":"W2124521189","doi":"10.1287/trsc.1060.0166","title":"Solving a Dynamic and Stochastic Vehicle Routing Problem with a Sample Scenario Hedging Heuristic","year":2006,"lang":"en","type":"article","venue":"Transportation Science","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":198,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Norges Forskningsråd","keywords":"Vehicle routing problem; Heuristic; Mathematical optimization; Stochastic programming; Routing (electronic design automation); Sample (material); Dynamic programming; Problem statement; Computer science; Statement (logic); Operations research; Mathematics; Engineering; Management science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001879069,0.0008365083,0.001363209,0.0007185582,0.0003493746,0.0009497494,0.001313202,0.001650904,0.002535275],"category_scores_gemma":[0.002855649,0.0008664763,0.0007306865,0.0009938793,0.0008112759,0.001120176,0.0007776716,0.001025256,0.0001613109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001021783,"about_ca_system_score_gemma":0.001571245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005138972,"about_ca_topic_score_gemma":0.00437988,"domain_scores_codex":[0.9994239,0.000302112,0.00002342157,0.00009917879,0.0000688387,0.00008254391],"domain_scores_gemma":[0.9981592,0.001427347,0.0001360607,0.00007466276,0.0001105393,0.00009218142],"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":[0.0000256262,0.00002187444,0.0001165332,0.00001417524,0.00001392732,0.0000209681,0.00001016225,0.9932066,0.0001064006,0.002640014,0.0001525327,0.003671204],"study_design_scores_gemma":[0.00001160976,0.00001666296,0.00003360328,0.000001942752,0.000003924466,0.000004306407,0.000006428269,0.9978472,0.00006182734,0.001923884,0.00008625045,0.000002355687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09429652,0.0003001849,0.9005771,0.0003500133,0.0000372205,0.0001564345,0.0001294406,0.0001876872,0.00396533],"genre_scores_gemma":[0.7317626,0.0001891189,0.2652012,0.0001051062,0.00003813497,0.0003158856,0.0002138029,0.0000525949,0.002121584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005138972,"threshold_uncertainty_score":0.01021808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007078761078080346,"score_gpt":0.2323668125458855,"score_spread":0.2252880514678052,"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."}}