{"id":"W4394910141","doi":"10.1016/j.ejor.2024.04.011","title":"A hybrid genetic search and dynamic programming-based split algorithm for the multi-trip time-dependent vehicle routing problem","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Polytechnique Montréal","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; National University's Basic Research Foundation of China; Ministry of Education - Singapore","keywords":"Vehicle routing problem; Solver; Computer science; Algorithm; Monotone polygon; Queue; Mathematical optimization; Genetic algorithm; Computation; Routing (electronic design automation); Dynamic programming; Mathematics","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":[0.01032474,0.0001375136,0.0001458439,0.0002740086,0.0003607261,0.0007856217,0.0003413371,0.00002248858,0.00004627393],"category_scores_gemma":[0.0003410618,0.0001028175,0.00007983228,0.0002971829,0.0001080676,0.0001689172,0.00008244923,0.00073301,0.0000425129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001588477,"about_ca_system_score_gemma":0.0002497683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002757106,"about_ca_topic_score_gemma":7.366639e-7,"domain_scores_codex":[0.9972157,0.0008714183,0.0005015736,0.0002116149,0.0008048596,0.000394826],"domain_scores_gemma":[0.9979548,0.001049978,0.00002912648,0.0001490757,0.0006716401,0.0001453244],"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":[0.00001542159,0.0000290514,0.00004031116,0.00008359244,0.00007739665,0.0001079727,0.0002348211,0.5921427,0.002744144,0.00004115286,0.0001862874,0.4042972],"study_design_scores_gemma":[0.0006197063,0.0001946593,0.000729633,0.0001486647,0.00001679465,0.0001374046,0.00005644965,0.9948096,0.0007330181,0.00001135767,0.002434824,0.0001079459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02088133,0.002500172,0.9748149,0.0008367301,0.0001272116,0.0006346604,0.00002195121,0.00007215312,0.0001109189],"genre_scores_gemma":[0.4146913,0.0000840395,0.5842236,0.00002176937,0.0002749178,0.00001907165,0.00000749957,0.00009664716,0.0005811087],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4041892,"threshold_uncertainty_score":0.7575768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04608592239922939,"score_gpt":0.3465077906686401,"score_spread":0.3004218682694107,"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."}}