Abstract: | There has been work in either OM or Statistics addressing the problem of how call centers - and other high volume service businesses - can better manage the capacity-demand mismatch that results from arrival-rate uncertainty. OM papers account for uncertainty when making staffing and scheduling decisions. Statistical models have sought to better characterize the distribution of arrival rates, by time of day, as they evolve. While each line of research has made important progresses in addressing certain elements of the problem caused by arrival-rate uncertainty, neither addresses the whole problem. I will present a data-driven integrated forecasting and stochastic programming framework. |
Affiliation: | Haipeng Shen received his PhD in Statistics from The Wharton School of Business, University of Pennsylvania in 2003. He is a full professor of Statistics and Operations Research, at the University of North Carolina at Chapel Hill, and a visiting professor of Innovation and Information Management, at the School of Business, University of Hong Kong. His research evolves around the theme of data-driven decision making in the face of uncertainty, including fundamental statistical research about challenges imposed by big data (high dimensionality and complex structure), as well as interdisciplinary analytical research in business analytics, neuroimaging, bioinformatics, and network traffic modeling. His work has been supported by US NSF Statistics, NSF Service Enterprise Systems, NIH, The Xerox Foundation, and Hong Kong Stanley Ho Challenge Fund. He has published research articles in top journals in both Statistics (JASA and AOAS) and Operations Management (MSOM). He has collaborated with industry partners such as Allcatel-Lucent, Bank of America, Xerox, and i-MD. |