{"id":"W2031089199","doi":"10.1007/s11590-014-0789-8","title":"Characterizing the optimality gap and the optimal packings for the bin packing problem","year":2014,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bin packing problem; Column generation; Benchmark (surveying); Mathematical optimization; Bin; Heuristic; Set (abstract data type); Lagrangian relaxation; Column (typography); Relaxation (psychology); Packing problems; Mathematics; Set packing; Computer science; Algorithm","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":[0.005183872,0.001553254,0.002808924,0.002857462,0.001389617,0.004984097,0.00239511,0.002996428,0.01001874],"category_scores_gemma":[0.03616881,0.001405633,0.001072708,0.003496233,0.003508423,0.01059451,0.003927223,0.004866849,0.0009280969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002343572,"about_ca_system_score_gemma":0.001963913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001417002,"about_ca_topic_score_gemma":0.0008102167,"domain_scores_codex":[0.9964766,0.00136797,0.0001743478,0.0004679738,0.0009276792,0.0005854283],"domain_scores_gemma":[0.9789464,0.01705985,0.001297208,0.0008960043,0.001094204,0.0007064334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004289437,0.000220799,0.0008255948,0.0003791354,0.000045229,0.00009955531,0.0002566007,0.1642796,0.00154061,0.7904323,0.009249244,0.03224234],"study_design_scores_gemma":[0.00002503049,0.00007405158,0.0004047988,0.00008162828,0.00001537992,0.00009021358,0.0001461401,0.3219482,0.0005311844,0.67434,0.002322151,0.00002124804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1680501,0.005808266,0.7508518,0.005760178,0.0005690313,0.0001905918,0.001046431,0.0003147807,0.06740885],"genre_scores_gemma":[0.8382918,0.004544779,0.141705,0.0009393593,0.0009239885,0.0003987433,0.001172862,0.000583769,0.01143967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01001874,"threshold_uncertainty_score":0.03351599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01115533893944705,"score_gpt":0.202790661698188,"score_spread":0.191635322758741,"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."}}