In this paper, we study a distributionally robust optimization (DRO) problem with affine decision rules. particular, construct an ambiguity set based on new family of Wasserstein metrics, shortfall–Wasserstein which apply normalized utility-based shortfall risk measures to summarize the transportation cost random variables. demonstrate that multi-dimensional ball can be affinely projected onto ...

The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuracies or human’s unpredictability. This study addresses the robust uncapacitated multiple allocation hub location problem with a set of ...

We present an optimization-based method to plan the motion of autonomous robot under uncertainties associated with dynamic obstacles, such as humans. Our bounds marginal risk collisions at each point in time by incorporating chance constraints into planning problem. This problem is not suitable for online optimization outright arbitrary probability distributions. Hence, we sample from these usi...

We focus on the scenario of robust information clustering (RIC) based on the minimax optimization of mutual information (MI). The minimization of MI leads to the standard mass-constrained deterministic annealing clustering, which is an empirical risk-minimization algorithm. The maximization of MI works out an upper bound of the empirical risk via the identification of outliers (noisy data point...

ion to application users supported by efficient and robust optimization techniques. In this paper, we propose, MiSense, a service-oriented component-based middleware layer in order to support distributed sensor applications with various performance requirements. MiSense reduces complexity by imposing a structure on top of the component model in the form of composability restrictions and by offe...

background and objectives: a sudden jump in blood demand during natural disasters may have strong negative impact on the performance of blood supply chain. appropriate response to the emergency situations, requires predictive approach to determining the optimal allocation of blood supply chain resources for various disaster scenarios. the present study, thus, presents an optimization model aime...

This entry describes the basic setup of linear robust control and the difficulties typically encountered when designing optimization algorithms to cope with robust stability and performance specifications.

Robust discrete optimization is a technique for structuring uncertainty in the decision-making process. The objective is to find a robust solution that has the best worst-case performance over a set of possible scenarios. However, this is a difficult optimization problem. This paper proposes a two-space genetic algorithm as a general technique to solve minimax optimization problems. This algori...