In a classical optimization problem, we are usually given a system with some known parameters and constraints and the goal is to find a feasible solution of minimum cost (or maximum profit) with respect to the constraints. Often these parameters and constraints, which heavily influence the optimum solution, are assumed to be precisely known. For example, we might be given a graph G(V,E) with we...

in today’s world renewable energy is highly demanding because of increasing oil and fuelprices, desire and necessity to avert irreversible climate damage and also the unreliability of the non -renewablesources. in renewable energy, water is an important source. previously water is used only for survivalof human being as much as food but no attentionhas been givenen to economical use and conserv...

optimization of reservoir parameters is an important issue in petroleum exploration and production. the ant colony optimization(aco) is a recent approach to solve discrete and continuous optimization problems. in this paper, the ant colony optimization is usedas an intelligent tool to estimate reservoir rock properties. the methodology is illustrated by using a case study on shear wave velocity...

In this paper, we present a model to support decision-makers about where to locate safety barriers and mitigate the consequences of an accident triggering domino effects. Based on the features of an industrial area that may be affected by domino accidents, and knowing the characteristics of the safety barriers that can be installed to stall the fire propagation between installations, the decisi...

Robust optimal experiment design for dynamic system identification is cast as a minmax optimization problem, which is infinite-dimensional. If the input spectrum is discretized (either by considering a Riemmann approximation, or by restricting it to the span of a finite dimensional linear space), this problem falls within the class of semi-infinite convex programs. One approach to this optimiza...

In two-stage robust optimization the solution to a problem is built in two stages: In the first stage a partial, not necessarily feasible, solution is exhibited. Then the adversary chooses the “worst” scenario from a predefined set of scenarios. In the second stage, the first-stage solution is extended to become feasible for the chosen scenario. The costs at the second stage are larger than at ...

Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the partic...

Optimization of noisy non-linear problems plays a key role in engineering and design problems. These optimization problems can't be solved effectively by using conventional optimization methods. However, metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) seem very efficient to approach in these problems and became very popular. The efficiency of these ...

index tracking is the process of developing a portfolio that reproduces the performance of an index. the tracker portfolio has relatively good diversity and low turnover and low transaction costs. in this paper we applied a binary programming model for index tracking problem. in this model the number of assets for portfolio construction is defined by portfolio manager. the robust optimization f...