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Integrating Uncertainty in Decision Making: A Target-Oriented Robust Optimization Approach
It is well-acknowledged in decision theory that real-world agents rarely optimize their decisions. Doing so implies a need for a perfect model of a system. However, this can never be the case since reality does not produce the exact conditions as initially planned. Thus, arriving at an optimal decision is virtually impossible, even when faced with simple problems. The need to achieve real, rather than ideal, representations of systems has imbued an attitude of satisficing in decision-makers. The talk presents a target-oriented robust optimization approach that preserves computational tractability and allows for ambiguity in uncertain information. The efficacy of the approach will be demonstrated using a generation and transmission network planning problem.
Bio:Prof. Charlle Sy is a Professor of Practice at Cornell University’s Systems Engineering Program. She received the 2023 Underwriters Laboratories ASEAN-U.S. Science Prize for Women. She was also recognized as one of 2021’s Outstanding Young Scientists by the Philippine National Academy of Science and Technology. She serves as a board member of the Asia Pacific Industrial Engineering and Management Systems. Her research interests are robust optimization and systems thinking. She developed an algorithmic framework called Target-Oriented Robust Optimization which integrates uncertainty at the point of decision-making. TORO has extensively been used to design and plan engineering systems, including infrastructure and network planning in energy, production, and water networks. Charlle Sy received her Ph.D. at the National University of Singapore.