{"id":"W2470126219","doi":"10.1016/j.dam.2016.05.030","title":"A heuristic for cumulative vehicle routing using column generation","year":2016,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column generation; Mathematical optimization; Vehicle routing problem; Rounding; Linear programming; Heuristic; Cover (algebra); Routing (electronic design automation); Scalability; Linear programming relaxation; Mathematics; Set (abstract data type); Randomized rounding; Relaxation (psychology); Computer science; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003975483,0.0001848747,0.0002427603,0.00005639889,0.0001346757,0.00005391207,0.0001138718,0.00008997892,0.00002326828],"category_scores_gemma":[0.0001816115,0.0001535033,0.00005896102,0.0001485059,0.00003467154,0.00009535432,0.000032084,0.0000576501,0.00001576664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001208948,"about_ca_system_score_gemma":0.00001705773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001042665,"about_ca_topic_score_gemma":0.000001239398,"domain_scores_codex":[0.9989295,0.00001560993,0.000402602,0.0001958335,0.00015858,0.0002978596],"domain_scores_gemma":[0.999162,0.0003445434,0.0001080901,0.0002552701,0.00006480097,0.00006528776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001051401,0.0000353389,0.00003770477,0.0003151021,0.0001013252,9.046151e-7,0.00228488,0.322314,0.6007656,0.06577017,0.0002752692,0.008089109],"study_design_scores_gemma":[0.0005292565,0.00001047864,0.00001017109,0.00005899744,0.00004642616,0.000001897981,0.0001446295,0.9653466,0.02928499,0.004265796,0.00006348604,0.0002372232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1066192,0.00001240578,0.8908612,0.00002129233,0.000103149,0.0005076337,0.00002683394,0.0003127911,0.001535524],"genre_scores_gemma":[0.5329017,0.000002839981,0.4667883,0.00001371133,0.0001175262,0.00005494074,0.000007949715,0.00006498214,0.00004802149],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6430326,"threshold_uncertainty_score":0.6259683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05004641109890581,"score_gpt":0.2985779523229441,"score_spread":0.2485315412240383,"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."}}