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...

The classical single-band uncertainty model introduced by Bertsimas and Sim [8] has represented a breakthrough in the development of tractable robust counterparts of Linear Programs. However, adopting a single deviation band may be too limitative in practice: in many real-world problems, observed deviations indeed present asymmetric distributions over asymmetric ranges, so that getting a higher...

This paper presents a multi-objective model for location-transportation problem under uncertainty that has been developed to respond to crisis. In the proposed model, humanitarian aid distribution centers (HADC), the number and location of them, the amount of relief goods stored in distribution centers, the amount of relief goods sent to the disaster zone, the number of injured people transferr...

This study has been inspired by the paper "An efficient 3D topology optimization code written in MATLAB” written by Liu and Tovar (2014) demonstrating that SIMP-based three-dimensional (3D) topology optimization of continuum structures can be implemented in 169 lines of MATLAB code. Based on the above paper, we show here that, by simple and easy-to-understand modificati...

In this paper, a novel compensator based on Magnetically Controlled Reactor with Fixed Capacitor banks (FC-MCR) is introduced and then power system stability in presence of this compensator is studied using an intelligent control method. The problem of robust FC-MCR-based damping controller design is formulated as a multi-objective optimization problem. The multi-objective problem is concocted ...

this research provides an optimization tool for use by supply chain managers in the design and operation of manufacturing- distribution networks under uncertain demand conditions. the problem under consideration consists of determining the supply chain infrastructure; raw material purchases, shipments, and inventories; and finished product production quantities, inventories, and shipments neede...

optimization models have been used to support decision making in production planning for a long time. however, several of those models are deterministic and do not address the variability that is present in some of the data. robust optimization is a methodology which can deal with the uncertainty or variability in optimization problems by computing a solution which is feasible for all possible ...

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 ...

A framework for robust optimization under uncertainty based on the use of the generalized inverse distribution function (GIDF), also called quantile function, is here proposed. Compared to more classical approaches that rely on the usage of statistical moments as deterministic attributes that define the objectives of the optimization process, the inverse cumulative distribution function allows ...

Topology optimization (TO), which is a design technique that does not require parameters, has been attracting attention. TO high degree of freedom and can obtain novel shape suitable for desired purposes. However, the characteristics obtained may be greatly damaged due to uncertainty such as variations in material properties. Also, when multi-objective considered, an increase calculation load b...