Abstract: A method, system, and computer program product are disclosed for guiding users in optimization-based planning under uncertainty. In one embodiment, the invention provides a method comprising identifying one or more characterizations of a specified uncertainty in a defined process; generating a set of plans based on the uncertainty characterization; and finding a new plan based on the existing set of plans, including identifying an added constraint, and finding a new plan that satisfies this added constraint. The new plan is analyzed to determine whether the new plan satisfies defined criteria; and when the new plan satisfies the defined criteria, the new plan is added to the set of plans. One of the plans is identified as a recommended plan for the defined process. In an embodiment, the recommended plan is identified based on a trade-off analysis of the plans using at least two defined aspects of the plans.

Abstract: Embodiments of the disclosure include a method for providing data-driven distributionally robust optimization. The method includes receiving a plurality of samples of one or more uncertain parameters for a complex system and calculating a distribution uncertainty set for the one or more uncertain parameters. The method also includes receiving a deterministic problem model associated with the complex system that includes an objective and one or more constraints and creating a distributionally robust counterpart (DRC) model based on the distribution uncertainty set and the deterministic problem model. The method further includes formulating the DRC as a generalized problem of moments (GPM), applying a semi-definite programing (SDP) relaxation to the GPM and generating an approximation for a globally optimal distributionally robust solution to the complex system.

Abstract: Embodiments of the disclosure include a system for providing data-driven distributionally robust optimization the system including a processor, the processor configured to perform a method. The method includes receiving a plurality of samples of one or more uncertain parameters for a complex system and calculating a distribution uncertainty set for the one or more uncertain parameters. The method also includes receiving a deterministic problem model associated with the complex system that includes an objective and one or more constraints and creating a distributionally robust counterpart (DRC) model based on the distribution uncertainty set and the deterministic problem model. The method further includes formulating the DRC as a generalized problem of moments (GPM), applying a semi-definite programming (SDP) relaxation to the GPM and generating an approximation for a globally optimal distributionally robust solution to the complex system.