{"id":"W2523297565","doi":"10.1080/00207543.2016.1231940","title":"A column generation based heuristic for the capacitated vehicle routing problem with three-dimensional loading constraints","year":2016,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Column generation; Mathematical optimization; Heuristic; FIFO and LIFO accounting; Benchmark (surveying); Tabu search; Vehicle routing problem; Computer science; Routing (electronic design automation); Computation; Algorithm; Mathematics; FIFO (computing and electronics)","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":[0.003861303,0.0001032546,0.0001231133,0.0002956619,0.000215938,0.0001194164,0.0002937891,0.00004938071,0.00008446461],"category_scores_gemma":[0.001577098,0.00006246263,0.00005611318,0.0002821248,0.0002225814,0.0002800434,0.00002209883,0.000286726,0.000006346687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000322225,"about_ca_system_score_gemma":0.000212439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000826721,"about_ca_topic_score_gemma":0.00002224436,"domain_scores_codex":[0.9979835,0.0001802508,0.0004374067,0.0001724056,0.0009784627,0.0002480138],"domain_scores_gemma":[0.9955044,0.000873495,0.0001625373,0.0001340616,0.003257513,0.00006800175],"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":[0.0002926024,0.0000641338,0.001858148,0.00002609923,0.0003105623,0.00001423966,0.0001877842,0.7482549,0.198679,0.0006042649,0.003628867,0.04607937],"study_design_scores_gemma":[0.002124421,0.0002517751,0.001297755,0.0004299562,0.00003277565,0.0002611523,0.000114574,0.9165145,0.07714944,0.0005400214,0.00109028,0.0001933747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1950693,0.00007777772,0.7953889,0.007602798,0.001228164,0.0004863464,0.00001549906,0.00005591482,0.00007532832],"genre_scores_gemma":[0.9409953,0.000009637097,0.05778225,0.00002528464,0.001002062,0.00003325725,0.000003606101,0.00003078315,0.0001178601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.745926,"threshold_uncertainty_score":0.2547152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09263538036986169,"score_gpt":0.354699556474901,"score_spread":0.2620641761050393,"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."}}