{"id":"W4317792384","doi":"10.1109/wsc57314.2022.10015281","title":"A Logistic Regression and Linear Programming Approach for Multi-Skill Staffing Optimization in Call Centers","year":2022,"lang":"en","type":"article","venue":"2022 Winter Simulation Conference (WSC)","topic":"Advanced Queuing Theory Analysis","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Staffing; Logistic regression; Computer science; Quality of service; Key (lock); Mathematical optimization; Quality (philosophy); Linear programming; Service (business); Operations research; Machine learning; Computer network; Mathematics; Algorithm; Operating system","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":[0.002565059,0.001156225,0.001423998,0.0011758,0.0008102279,0.001169236,0.00235244,0.002240859,0.004040157],"category_scores_gemma":[0.00804826,0.0008608736,0.001193513,0.001636002,0.001083443,0.001506603,0.001670723,0.002568111,0.0004299863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00166008,"about_ca_system_score_gemma":0.001726074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009982043,"about_ca_topic_score_gemma":0.00574675,"domain_scores_codex":[0.9985691,0.000842928,0.00003094608,0.000185386,0.0001801208,0.000191492],"domain_scores_gemma":[0.9963675,0.002780639,0.0002995995,0.0000744212,0.0003145765,0.0001633876],"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":[0.00001695405,0.00002169043,0.0002305635,0.00001692824,0.00001236229,0.00003087295,0.00001569061,0.9898981,0.000131313,0.004644556,0.0002093688,0.004771726],"study_design_scores_gemma":[0.00000409717,0.00000940397,0.00003621311,0.000001784259,0.000002111306,0.000005165299,0.000004982783,0.9973603,0.00004610156,0.002414853,0.0001123663,0.00000259629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02147787,0.000295323,0.9749111,0.0004774727,0.00003085709,0.00004189322,0.00004317593,0.0001199297,0.002602291],"genre_scores_gemma":[0.6192721,0.0007194469,0.3684582,0.0004045719,0.0002023304,0.0004337519,0.0001931605,0.0002149149,0.01010146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009982043,"threshold_uncertainty_score":0.01984787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07365406406088998,"score_gpt":0.3150370860579116,"score_spread":0.2413830219970217,"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."}}