报告内容摘要：

Abstract: We consider a project selection problem where each project has an uncertain return with partially characterized probability distribution. The decision maker selects a feasible subset of projects so that the risk of the portfolio return not meeting a specified target is minimized. To model and evaluate this risk, we propose and justify a general performance measure, the underperformance riskiness index (URI) We define a special case of the URI, the entropic underperformance riskiness index (EURI), for the project selection problem. We minimize the EURI of the project portfolio, which is the reciprocal of the absolute risk aversion (ARA) of an ambiguity-averse individual with constant ARA who is indifferent between the target return with certainty and the uncertain portfolio return. The EURI extends the riskiness index of Aumann and Serrano (2008) by incorporating the target and distributional ambiguity, and controls the underperformance probability (UP) for any target level. Our model includes correlation and interaction effects such as synergies. Since the model is a discrete nonlinear optimization problem, we derive the optimal solution using Benders decomposition techniques. We show that computationally efficient solution of the model is possible. Furthermore, the project portfolios generated by minimizing the underperformance risk are more than competitive in achieving the target with those found by benchmark approaches, including maximization of expected return, minimization of UP, mean-variance analysis, and maximization of Roy’s safety-first ratio (1952). When there is only a single constraint for the budget, we describe a heuristic which routinely provides project portfolios with near-optimal underperformance risk.
 
报告人简介：

Jin QI is an assistant professor in the Department of Industrial Engineering and Logistics Management at Hong Kong University of Science and Technology. She graduated from NUS Business School at National University of Singapore in 2014, and holds a Master’s degree in Management Science and a Bachelor’s degree in Mechanical Engineering from Tsinghua University. Jin QI is broadly interested in the analytics of services systems, with the goals of eliciting operational insights and providing solutions for supporting decision-making in practice. Her research interests include robust optimization, healthcare operations, project management and transportation. She has collaborated with several hospitals in Singapore and Hong Kong.

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