{"id":"W4281701211","doi":"10.1145/3543516.3456271","title":"Competitive Algorithms for the Online Multiple Knapsack Problem with Application to Electric Vehicle Charging","year":2021,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Electrical, Communications and Cyber Systems; University of Toronto; National Science Foundation","keywords":"Knapsack problem; Competitive analysis; Continuous knapsack problem; Online algorithm; Computer science; Algorithm; Mathematical optimization; Limiting; Identification (biology); Dual (grammatical number); Generalized assignment problem; Change-making problem; Optimization problem; Mathematics; Upper and lower bounds; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004615946,0.002040214,0.001971081,0.001228758,0.001091287,0.003282186,0.003543488,0.002338514,0.004631998],"category_scores_gemma":[0.01528156,0.0008471657,0.001223288,0.002956659,0.001790925,0.003436725,0.002083069,0.003843185,0.0007736805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002791245,"about_ca_system_score_gemma":0.003068865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004190935,"about_ca_topic_score_gemma":0.003896281,"domain_scores_codex":[0.9965196,0.001487916,0.0001487897,0.0005550225,0.0008805839,0.0004081161],"domain_scores_gemma":[0.9892671,0.008063804,0.0007964003,0.0006188163,0.000892583,0.0003612442],"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":[0.00015798,0.0003700277,0.0004761047,0.0002939668,0.00006547335,0.00008583949,0.0001253999,0.7790534,0.001099987,0.1430921,0.005301762,0.06987786],"study_design_scores_gemma":[0.00002572587,0.00005732304,0.00006732548,0.00001362183,0.00001016028,0.00003936438,0.00002649608,0.9570644,0.0003673336,0.04045084,0.001865587,0.00001180718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008177564,0.0008611297,0.9801748,0.0004665056,0.0001229874,0.0001502651,0.00006247636,0.00020818,0.009776042],"genre_scores_gemma":[0.3650512,0.001725895,0.6246551,0.0004577621,0.0003963415,0.0005059864,0.0002588671,0.0003283982,0.006620592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004631998,"threshold_uncertainty_score":0.02441174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06932257938094619,"score_gpt":0.3470856595915804,"score_spread":0.2777630802106342,"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."}}