{"title":"Workforce Management and Optimization using Stochastic Network Models","authors":"Y. Lu, A. Radovanovic, M. Squillante","doi":"10.1109/SOLI.2006.328911","DOIUrl":null,"url":null,"abstract":"We develop a model based on stochastic loss networks to characterize the dynamics and uncertainty in general workforce management and optimization. We formulate profit maximization problems with serviceability constraints under different assumptions on demand and supply. Though these optimization problems are in general nonlinear programming problems, we are able to observe some intrinsic properties of the functions that facilitate efficient computation of the optimal solution. Numerical results demonstrate that our model provides capacity planning decisions that yield better results than available in current practice","PeriodicalId":325318,"journal":{"name":"2006 IEEE International Conference on Service Operations and Logistics, and Informatics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Service Operations and Logistics, and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOLI.2006.328911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
We develop a model based on stochastic loss networks to characterize the dynamics and uncertainty in general workforce management and optimization. We formulate profit maximization problems with serviceability constraints under different assumptions on demand and supply. Though these optimization problems are in general nonlinear programming problems, we are able to observe some intrinsic properties of the functions that facilitate efficient computation of the optimal solution. Numerical results demonstrate that our model provides capacity planning decisions that yield better results than available in current practice