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
Solving Nonsmooth Nonconvex Compound Stochastic Programs with Applications to Risk Measure Minimization
Liu, Junyi & Cui, Ying & Pang, Jong-Shi. (2020). Solving Nonsmooth Nonconvex Compound Stochastic Programs with Applications to Risk Measure Minimization.
Matching queues with reneging: a product form solution
Castro, Francisco & Nazerzadeh, Hamid & Yan, Chiwei. (2020). Matching queues with reneging: a product form solution. Queueing Systems. 96. 10.1007/s11134-020-09662-y.
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method
Liu, Heyuan & Grigas, Paul. (2021). Risk Bounds and Calibration for a Smart Predict-then-Optimize Method.
Gradient-Based Simulation Optimization Algorithms via Multi-Resolution System Approximations
Xu, Jingxu & Zheng, Zeyu. (2023). Gradient-Based Simulation Optimization Algorithms via Multi-Resolution System Approximations. INFORMS Journal on Computing. 35. 10.1287/ijoc.2023.1279.
A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation
Yufeng, Zheng & Zheng, Zeyu. (2020). Doubly Stochastic Generative Arrivals Modeling.
A Class of Stochastic Games and Moving Free Boundary Problems
X. Guo, W. Tang, R. Xu, “A class of stochastic games and moving free boundary problems”. SIAM Journal on Control and Optimization 60 (2), 758-785
A General Framework for Learning Mean-Field Games
Guo, X., Hu, A., Xu, R., & Zhang, J. (2022). A General Framework for Learning Mean-Field Games. Mathematics of Operations Research. https://doi.org/10.1287/moor.2022.1274
MFGs for partially reversible investment
H. Cao, X. Guo, “MFGs for partially reversible investment”. Stochastic Processes and their Applications, 150, 995-1014.
Dynamic Routing Problems with Delayed Information
Hyytiä, E., Righter, R. (2021). Dynamic Routing Problems with Delayed Information. In: Zhao, Q., Xia, L. (eds) Performance Evaluation Methodologies and Tools. VALUETOOLS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 404. Springer, Cham. https://doi.org/10.1007/978-3-030-92511-6_11