Lewis Ntaimo, Ph.D.
Professor and Head of the Wm Michael Barnes ’64 Department of Industrial & Systems Engineering at Texas A&M University
Abstract:Risk-Averse Multistage Stochastic Programs with Expected Conditional Risk Measures
Risk-averse multistage stochastic programming (MSSP) provides a framework for modeling sequential decision-making problems involving uncertainty and risk. It offers the advantage of enabling optimal decision-making in the face of unforeseen future outcomes. However, despite having many real-world applications, risk-averse MSSP problems are very challenging to solve due to their large-scale nature and the incorporation of risk measures into the formulation. Therefore, this calls for novel decomposition methods to tackle these problems. In this talk, we introduce the expected conditional risk measure (ECRM) for both deviation and quantile risk measures. ECRMs are attractive because they are time-consistent, which means that a plan made today will not be changed in the future if the problem is re-solved given a realization of the random variables. We show that solving risk-averse problems based on ECRMs is as complex as solving risk-neutral ones and therefore, amenable to the stochastic dual decomposition (SDDP) method. We illustrate our results with extensive numerical computations for problems from two applications: hydrothermal scheduling and portfolio selection. The results show that ECRMs provide superior performance over the risk-neutral approach in terms of costs. In particular, for the hydrothermal scheduling problem the new approach shows higher expected costs in the early stages to hedge against cost spikes in later stages. For the portfolio selection problem, the ECRM approach results in well-diversified portfolios over time compared to the risk-neutral approach.
Tune in via Zoom: https://www.ise.ufl.edu/events/ise-distinguished-seminar-series/
Department of Industrial & Systems Engineering