Collaborative Research: Risk-Averse Stochastic Optimization

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This grant provides funding for the development of conceptually new models and solution methods for optimization of stochastic systems under high uncertainty and risk. The research will be based on the idea of integrating the theory of risk, stochastic optimization, and cutting-edge numerical techniques. It will focus on novel optimization models involving stochastic dominance constraints and risk measures in the objectives and in the constraints. This approach will capture the entire distribution of outcomes, including events of small probability but high consequences, rather than just the average performance. New solution methods will be developed that exploit the structure of the models and their probabilistic character. Models and methods for dynamic decision problems, which arise in many applications, will constitute a significant component of the project.The project has a high potential of providing a breakthrough in decision problems under high uncertainty and risk. If successful, it will provide decision-makers and analysts in many areas with novel tools for risk control. The results of the research will provide a qualitative advance in economics, finance, supply-chain management, manufacturing, military problems, telecommunication, electricity generation, and distribution, and many other areas involving high uncertainty and risk.