This paper presents an application of Graphics Processing Units (GPU) technology for speeding up a schedule optimization problem under uncertainty and provides a fast decision support algorithm to solve an air traffic management problem. In terminal airspace, integrated departure and arrival operations using shared resources have the potential to increase operations efficiency. However, results...

A fractionated spacecraft is an innovative application of a distributive space system. To fully understand the impact of various uncertainties on its development, launch and in-orbit operation, we use the stochastic missioncycle cost to comprehensively evaluate the survivability, flexibility, reliability and economy of the ways of dividing the various modules of the different configurations of ...

We introduce a novel optimization-based motion planner, Stochastic Extended LQR (SELQR), which computes a trajectory and associated linear control policy with the objective of minimizing the expected value of a user-defined cost function. SELQR applies to robotic systems that have stochastic non-linear dynamics with motion uncertainty modeled by Gaussian distributions that can be stateand contr...

A procedure based on convex optimization techniques for deriving normbounded uncertainty models for MIMO systems is presented. The procedure is developed for unstructured additive uncertainty models, but in principle this is no limitation since any uncertainty model of LFT type can be transformed into such a model. The models are determined by matching to process data available in the form of f...

Robust optimization can provide safe and tractable analytical approximation for the chance constrained optimization problem. In this work, we studied the application of robust optimization approximation in solving chance constrained planning and scheduling problem under uncertainty. Four different robust optimization approximation methods for improving the quality of robust solution were invest...

ABSTRACT Metamodeling techniques have been widely used in engineering design to improve the efficiency in simulation and optimization of design systems that involve computationally expensive simulation programs. Many existing applications are restricted to deterministic optimization. Very few studies have been conducted on studying the accuracy of using metamodels for optimization under uncerta...

Modern computing system applications or workloads can bring significant non-uniform temperature gradient on-chip, and hence can cause significant temperature uncertainty during clock-tree synthesis. Existing designs of clock-trees have to assume a given time-invariant worst-case temperature map but cannot deal with a set of temperature maps under a set of workloads. For robust clock-tree synthe...

A very general and robust approach to solving optimization problems involving probabilistic uncertainty is through the use of Probabilistic Ordinal Optimization. At each step in the optimization problem, improvement is based only on a relative ranking of the probabilistic merits of local design alternatives, rather than on crisp quantification of the alternatives. Thus, we simply ask: "Is that ...

In this paper, we identify two distinct notions of accuracy of land-use models and highlight a tension between them. A model can have predictive accuracy: its predicted land-use pattern can be highly correlated with the actual land-use pattern. A model can also have process accuracy: the process by which locations or land-use patterns are determined can be consistent with real world processes. ...

There has been an increased interest in recent years to incorporate uncertainty in Description Logics (DLs), and a number of proposals have been put forward for modeling uncertainty in DL frameworks. While much progress has been made on syntax, semantics, and query processing issues, optimizing queries in this context has received little attention. In this paper, we study query processing for a...