{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003467676,0.001080683,0.0009947821,0.001010301,0.0006628156,0.0007001447,0.001002351,0.0008361915,0.004050237],"category_scores_gemma":[0.0009057257,0.0004801389,0.0006758599,0.001454073,0.0005015605,0.0006181491,0.000633644,0.000843862,0.0004999664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007059193,"about_ca_system_score_gemma":0.001398332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006409558,"about_ca_topic_score_gemma":0.006976879,"domain_scores_codex":[0.9997172,0.00009120044,0.000009254793,0.00003927573,0.0000795192,0.00006353194],"domain_scores_gemma":[0.9994861,0.0002854834,0.00005711673,0.00004009217,0.00009071265,0.0000405747],"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":[0.00007056793,0.000118742,0.0002934568,0.0001174777,0.00003508443,0.0001371502,0.00004583757,0.8967862,0.003411038,0.005373437,0.003731315,0.08987975],"study_design_scores_gemma":[0.00002544588,0.00007182683,0.00008678202,0.000008559206,0.00001455333,0.00004320547,0.00001732639,0.9956216,0.001036441,0.001913136,0.001150157,0.00001103232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0268232,0.0005505076,0.9651595,0.0001662804,0.0001279185,0.0002520852,0.0002040592,0.000785421,0.005930942],"genre_scores_gemma":[0.3751037,0.0005171161,0.6193118,0.0002644175,0.00007744025,0.0005066397,0.0007345897,0.0002044667,0.003279758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006409558,"threshold_uncertainty_score":0.01354933,"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."}}