Stochastic Modeling and Simulation Research
Faculty
Risk and uncertainty is inherent in all real-world systems, and understanding its impact is essential in performance analysis and optimization. Researchers in the IEOR Department at UC Berkeley are developing stochastic models and simulations for applications ranging from call centers to cloud computing, as well as expanding fundamental theory in areas such as stochastic control, semi-Martingale and filtration expansions, the economics of queueing systems, and design of simulation experiments.
Selected Publications
A scalable approach to enhancing stochastic kriging with Gradients
Haojun Huo; Xiaowei Zhang; Zeyu Zheng. “A scalable approach to enhancing stochastic kriging with Gradients”. Proceedings of the Winter Simulation Conference.
Approximating Systems Fed by Poisson Processes with Rapidly Changing Arrival Rates
Zeyu Zheng, Harsha Honnappa, Peter W. Glynn. “Approximating Systems Fed by Poisson Processes with Rapidly Changing Arrival Rates“. Operations Research
Stochastic Comparison of Discounted Rewards
- R.Righter, “Stochastic Comparison of Discounted Rewards,” Journal of Applied Probability, vol. 48, pp. 293-294, 2011.
The Stochastic Sequential Assignment Problem with Arrivals
R. Righter, “The Stochastic Sequential Assignment Problem with Arrivals,” Probability in the Engineering and Informational Sciences, 2011.
Optimal Production Policies with Multistage Stochastic Leadtimes
J.-H. Kim, H.-S. Ahn and R. Righter, “Optimal Production Policies with Multistage Stochastic Leadtimes,” Probability in the Engineering and Informational Sciences, vol. 23, pp. 515-543, 2009.
A constrained non-linear regular-singular stochastic control problem, with applications
X. Guo, J. Liu, and X. Y. Zhou. A constrained non-linear regular-singular stochastic control problem, with applications, Stochastic Processes and their Applications, 109(2):167-187, 2004.
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