{"id":"W3034347614","doi":"10.71781/10847","title":"Stochastic optimization of staffing for multiskill call centers","year":2019,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Université de Montréal; Hydro-Québec","keywords":"Staffing; Stochastic optimization; Computer science; Operations research; Mathematical optimization; Mathematics; Economics; Management","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0001653951,0.0003118312,0.0004294414,0.0005367873,0.001726527,0.00005546254,0.0003261242,0.0002338504,0.00003087267],"category_scores_gemma":[0.0001899268,0.0003657889,0.0003268203,0.0003158852,0.00006861128,0.0007812164,0.00009095777,0.000169116,0.00001919652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267785,"about_ca_system_score_gemma":0.0002483723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005419491,"about_ca_topic_score_gemma":0.001443831,"domain_scores_codex":[0.9985031,0.00001689091,0.000386611,0.0004503416,0.0003656973,0.000277336],"domain_scores_gemma":[0.9981104,0.0001106899,0.0008893688,0.0002944989,0.0005668429,0.00002827232],"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":[0.001126802,0.0001036802,0.0004248939,0.0005762021,0.0002964335,0.00003468356,0.001379448,0.9764373,0.002081648,0.01627673,0.0001713941,0.001090778],"study_design_scores_gemma":[0.004068506,0.00007527175,0.001649558,0.001417778,0.002764338,0.00002814402,0.02529936,0.949155,0.001639897,0.001402332,0.01089603,0.001603818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6367072,0.005600766,0.3037542,0.0002116358,0.00547594,0.003236206,0.0002330469,0.0005611719,0.04421974],"genre_scores_gemma":[0.9696994,0.0000339595,0.002622153,0.00009598668,0.0003122484,0.0000332684,0.002256162,0.00007074919,0.02487609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3329921,"threshold_uncertainty_score":0.9998794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004865129990927832,"score_gpt":0.1780326187358718,"score_spread":0.1731674887449439,"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."}}