A critical aspect of smart machining systems is the appropriate management of knowledge and information to support effective decision-making. The uncertainty of model-based predictions of machining performance plays an important role in decision-making for machining optimization and adaptive control optimization. This paper presents a technique for managing modeling and measurement uncertaintie...

Based on the recent approach of Bertsimas and Sim (2004, 2003) to robust optimization in the presence of data uncertainty, we prove an easily computable and simple bound on the probability that the robust solution gives an objective function value worse than the robust objective function value, under the assumption that only cost coefficients are subject to uncertainty.We exploit the binary nat...

Chance-constrained programming is a relevant model for many concrete problems. However, it is known to be very hard to tackle directly. In this paper, the chance-constrained knapsack problem (CKP) is addressed. Relying on the recent advances in robust optimization, a tractable combinatorial algorithm is proposed to solve CKP. It always provides feasible solutions for CKP. Moreover, for two spec...

Optimization is often difficult to apply to robots due to the presence of modeling errors, which may cause constraints to be violated during execution on a real robot. This work presents a method to optimize trajectories with large modeling errors using a combination of robust optimization and parameter learning. In particular it considers the problem of computing a dynamically-feasible traject...

Resonance and wave-propagation problems are known to be highly sensitive towards parameter variations. This paper discusses topology optimization formulations for creating designs that perform robustly under spatial variations for acoustic cavity problems. For several structural problems, robust topology optimization methods have already proven their worth. However, it is shown that direct appl...

The present paper presents the rst steps towards a theory to build Robust Nonlinear Predictive Control based on Fuzzy Models. The main idea behind this theory is to write the Predictive Control Problem as a Robust Optimization problem and apply Semideenite Programming to solve in a eecient way the optimization. The information about the plant behavior and its uncertainties will be provided by t...

In this paper, the minimum channel gain flow with uncertainty in the demand vector is examined. The approach is based on a transformation of uncertainty in the demand vector to uncertainty in the gain vector. OFDM systems are known to overcome the impairment of the wireless channel by splitting the given system bandwidth into parallel sub-carriers, on which data-symbols can be transmitted simul...

For its high coefficient of performance and zero local emissions, the heat pump (HP) has recently become popular in North Europe and China. However, the integration of HPs may aggravate the daily peak-valley gap in distribution networks significantly.

Robust Optimization and Data Approximation in Machine Learning

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. This paper develops a robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply our approach to address the problem of analyzing competing biomass supply chains subject to stochastic de...