Presented By: Industrial & Operations Engineering
Departmental Seminar (899): Lewis Ntaimo, Texas A&M University — Stochastic Decomposition for Risk-Averse Multistage Stochastic Programming
The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
Title:
Stochastic Decomposition for Risk-Averse Multistage Stochastic Programming
Abstract:
Mean-risk multistage stochastic programming (MR-MSP) provides a framework for modeling sequential decision-making problems under uncertainty and risk. However, MR-MSP problems are difficult to solve due to their large-scale nature and the incorporation of risk measures in the objective. This work derives multistage stochastic decomposition (MSD) for solving large-scale MR-MSP instances with deviation and quantile risk measures. We show that risk-averse MSD converges asymptotically to an optimal solution and report on a computational study on the application of MSD to long-term hydrothermal scheduling. The study provides several insights into how optimal solutions vary across different risk levels as well as across different risk measures. In particular, the results reveal that conditional value at-risk exhibits desirable control over extreme scenarios than other risk measures.
Keywords:
Multistage stochastic programming, stochastic decomposition, interior sampling, mean-risk measures.
Bio:
Lewis Ntaimo is a Professor in the Department of Industrial and Systems Engineering at Texas A&M University and has been with the university since 2004. He obtained his Ph.D. in Systems and Industrial Engineering in 2004, his M.S. in Mining and Geological Engineering in 2000, and B.S. in Mining Engineering, all from the University of Arizona. Dr. Ntaimo’s research interests are in models and algorithms for large-scale stochastic optimization, systems modeling and process optimization, and computer simulation. Recent applications include patient and resource management in healthcare, wildfire response planning, aircraft assembly line production optimization, energy reduction in data centers, and wind farm operations and maintenance. His research has been funded by the National Science Foundation, Department of Homeland Security, and industry. Dr. Ntaimo is a member of INFORMS and IISE, and he is the 2018-2019 President of the INFORMS Minority Issues Forum. He currently serves as associate editor for INFORMS Journal on Computing, IISE Transactions, IISE Transactions on Healthcare Systems Engineering, and is on the Editorial Board of the Journal of Global Optimization.
The seminar will be followed by a reception in the IOE Commons (Room 1709) from 4 p.m. to 5 p.m.
Title:
Stochastic Decomposition for Risk-Averse Multistage Stochastic Programming
Abstract:
Mean-risk multistage stochastic programming (MR-MSP) provides a framework for modeling sequential decision-making problems under uncertainty and risk. However, MR-MSP problems are difficult to solve due to their large-scale nature and the incorporation of risk measures in the objective. This work derives multistage stochastic decomposition (MSD) for solving large-scale MR-MSP instances with deviation and quantile risk measures. We show that risk-averse MSD converges asymptotically to an optimal solution and report on a computational study on the application of MSD to long-term hydrothermal scheduling. The study provides several insights into how optimal solutions vary across different risk levels as well as across different risk measures. In particular, the results reveal that conditional value at-risk exhibits desirable control over extreme scenarios than other risk measures.
Keywords:
Multistage stochastic programming, stochastic decomposition, interior sampling, mean-risk measures.
Bio:
Lewis Ntaimo is a Professor in the Department of Industrial and Systems Engineering at Texas A&M University and has been with the university since 2004. He obtained his Ph.D. in Systems and Industrial Engineering in 2004, his M.S. in Mining and Geological Engineering in 2000, and B.S. in Mining Engineering, all from the University of Arizona. Dr. Ntaimo’s research interests are in models and algorithms for large-scale stochastic optimization, systems modeling and process optimization, and computer simulation. Recent applications include patient and resource management in healthcare, wildfire response planning, aircraft assembly line production optimization, energy reduction in data centers, and wind farm operations and maintenance. His research has been funded by the National Science Foundation, Department of Homeland Security, and industry. Dr. Ntaimo is a member of INFORMS and IISE, and he is the 2018-2019 President of the INFORMS Minority Issues Forum. He currently serves as associate editor for INFORMS Journal on Computing, IISE Transactions, IISE Transactions on Healthcare Systems Engineering, and is on the Editorial Board of the Journal of Global Optimization.
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