{"id":"W2145197897","doi":"10.1287/mnsc.1070.0824","title":"Staffing Multiskill Call Centers via Linear Programming and Simulation","year":2008,"lang":"en","type":"article","venue":"Management Science","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":183,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Staffing; Heuristics; Cutting-plane method; Computer science; Sample (material); Integer programming; Mathematical optimization; Linear programming; Sequence (biology); Service (business); Mathematics; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004082572,0.001273953,0.00171588,0.001051683,0.0007728968,0.001724587,0.001957168,0.001989367,0.00290865],"category_scores_gemma":[0.0103686,0.001231413,0.001027873,0.001342203,0.001653194,0.001495175,0.001265054,0.001964134,0.0002637385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003159262,"about_ca_system_score_gemma":0.002757519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01613055,"about_ca_topic_score_gemma":0.010625,"domain_scores_codex":[0.9982086,0.001142662,0.00003835338,0.0001500841,0.0002215298,0.0002387308],"domain_scores_gemma":[0.9883121,0.01002425,0.0006653229,0.0002246379,0.0004724413,0.0003012559],"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":[0.00001902167,0.0000167538,0.0001221405,0.000007715384,0.000005381009,0.000005690687,0.000009443544,0.9970477,0.0000487657,0.001668154,0.00005261934,0.000996626],"study_design_scores_gemma":[0.000005505663,0.000006980616,0.00001578218,0.000001510499,0.000001228136,8.689833e-7,0.000003734773,0.9991843,0.00003234485,0.000714596,0.00003194257,0.000001181785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1225547,0.0002746233,0.8690001,0.0006306756,0.00003339913,0.0001439051,0.0001128119,0.0004897945,0.006760031],"genre_scores_gemma":[0.7558177,0.0002361217,0.2405877,0.0001434232,0.00003593173,0.0004789376,0.0001812929,0.0001525568,0.002366383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01613055,"threshold_uncertainty_score":0.03207332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534793979028453,"score_gpt":0.2547545107693148,"score_spread":0.2394065709790302,"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."}}