Abstract Methods based on Gaussian stochastic process (GSP) models and expected improvement (EI) functions have been promising for box-constrained expensive optimization problems. These include robust design problems with environmental variables having set-type constraints. However, the methods that combine GSP EI sub-optimizations suffer from following problem, which limits their computational...

In this paper, we introduce a model  to optimization of milk run system that is one of VRP problem with time window and uncertainty in inventory. This approach led to the routes with minimum cost of transportation while satisfying all inventory in a given bounded set of uncertainty .The problem is formulated as a robust optimization problem. Since the resulted problem illustrates that grows up ...

this study proposes a combination of a fuzzy sliding mode controller (fsmc) with integral-proportion-derivative switching surface based superconducting magnetic energy storage (smes) and pid tuned by a multi-objective optimization algorithm to solve the load frequency control in power systems. the goal of design is to improve the dynamic response of power systems after load demand changes. in t...

In this paper, we introduced a novel method for asymmetric uncertainty set construction based on the distributional information of sampling data. Deterministic robust counterpart optimization formulation is derived for D-norm induced uncertainty set with the proposed method. Furthermore, the asymmetric set induced robust optimization model is compared with the classical symmetric set induced ro...

For expensive black-box optimization problems, surrogatemodel based approaches like Efficient Global Optimization are frequently used in continuous optimization. Their main advantage is the reduction of function evaluations by exploiting cheaper, data-driven models of the actual target function. The utilization of such methods in combinatorial or mixed search spaces is less common. Efficient Gl...