TITLE: Ambiguous Risk Constraints with Moment and Structural Information: Three Example Ruiwei Jiang

ABSTRACT:

Optimization problems face random constraint violations when uncertainty arises in constraint parameters. Effective ways of controlling such violations include risk constraints, e.g., chance constraints and Conditional Value-at-Risk (CVaR) constraints. This talk discusses risk constraints when the distributional information of the uncertain parameters consists of moment information (e.g., mean, covariance, support) and certain structural information, for which we mention three specific examples: alpha-unimodality, log-concavity, and dominance on the tail. We find that the ambiguous risk constraints in these settings can be recast or approximated using conic constraints that facilitate computation. Finally, we demonstrate the theoretical results via case studies on power system operation and appointment scheduling.

BIO: Ruiwei Jiang is an Assistant Professor of Industrial and Operations Engineering at the University of Michigan at Ann Arbor. His research interests include stochastic optimization and integer programming. Application areas of his work include power and water systems, healthcare, and transportation systems. Recognition of his research includes the Stochastic Programming Society student paper award, the INFORMS George E. Nicholson student paper award, and the INFORMS Junior Faculty Interest Group paper award (honorable mention).