Many real-life applications lead to the definition of robust optimization problems where the objective function is a black box. This may be due, for example, to the fact that the objective function is evaluated through computer simulations, and that some parameters are uncertain. When this is the case, existing algorithms for optimization are not able to provide good-quality solutions in genera...

Mathematical optimization plays an important role in engineering design, leading to greatly improved performance. Deterministic optimization however, can lead to undesired choices because it neglects input and model uncertainty. Reliability-based design optimization (RBDO) and robust design improve optimization by considering uncertainty. A design is called reliable if it meets all performance ...

During the tactical planning of multi-period vehicle routing operations, dispatchers must incorporate enough flexibility in their routing plans to accommodate potential customers who have not yet called in to place their order. We develop an Adaptive Robust Optimization framework to address this challenging problem and solve instances to guaranteed optimality.

In this paper, we will discuss the use of new methods from robust control and especially H°° theory for the explicit construction optimal feedback compensators for several practical distributed parameter systems. Indeed, based on operator and interpolation theoretic methods one can now solve the standard H control problem for a broad class of systems modelled by PDEs. In our approach, the compl...

An efficient formulation for the robust shape optimization of aerodynamic objects is introduced in this paper. The formulation has three essential features. First, an Euler solver based on a second-order Godunov scheme is used for the flow calculations. Second, a genetic algorithm with binary number encoding is implemented for the optimization procedure. The third ingredient of the procedure is...

The multi-objective optimization model studied in this paper deals with simultaneous minimization of finitely many linear functions subject to an arbitrary number of uncertain linear constraints. We first provide a radius of robust feasibility guaranteeing the feasibility of the robust counterpart under affine data parametrization. We then establish dual characterizations of robust solutions of...

The term “robust optimization” is quite popular, and the descriptor “robust” so commonly applied in the water resources literature that its specific meaning, and its implications for model results, have become elusive. We here provide a brief summary of the applications of robust optimization to water resources planning and management problems in the past 15 years. We describe and demonstrate c...

this paper discusses the portfolio selection based on robust optimization. since the parameters values of the portfolio optimization problem such as price of the stock, dividends, returns, etc. of per share are unknown, variable and their distributions are uncertain because of the market and price volatility, therefore, there is a need for the development and application of methodologies for de...