In this paper, we survey the primary research on the theory and applications of
distributionally robust optimization (DRO). We start with reviewing the modeling power and …

This paper reviews the state-of-the-art in robust design optimization–the search for designs
and solutions which are immune with respect to production tolerances, parameter drifts …

Distributionally robust optimization is a paradigm for decision making under uncertainty
where the uncertain problem data are governed by a probability distribution that is itself …

This is the first comprehensive introduction to the powerful moment approach for solving
global optimization problems (and some related problems) described by polynomials (and …

In this paper, we study a probabilistically robust transmit optimization problem under
imperfect channel state information (CSI) at the transmitter and under the multiuser multiple …

Robust optimization is still a relatively new approach to optimization problems affected by
uncertainty, but it has already proved so useful in real applications that it is difficult to tackle …

In this paper we survey the primary research, both theoretical and applied, in the area of
robust optimization (RO). Our focus is on the computational attractiveness of RO …

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents
an accessible treatment of the two main components of support vector machines (SVMs) …

Flexibility is a widely used term in planning process and real-time operation. The existing
research on flexibility uses different techniques to study flexibility property from different …

Manufacturers need to satisfy consumer demands in order to compete in the real world. This
requires the efficient operation of a supply chain planning. In this research we consider a …