optimization techniques can be efficiently utilized to achieve an optimal shape for arch dams. this optimal design can consider the conditions of the economy and safety simultaneously. the main aim is to present an applicable and practical model and suggest an algorithm for optimization of concrete arch dams to enhance their seismic performance. to achieve this purpose, a preliminary optimizati...

nowadays, different methods are used to solve optimization problems which ,one of the newest is bco method that are inspired by the nature and derived from the social life of bees of these methods can be noted that bco is derived from the social life of bees. we use in this paper fuzzy logic for bee‟s decision stage. at the end, we solve two different problem that have already been solved by es...

The paper discusses the application of multi-stage stochastic optimization for managing and optimizing expected returns versus risk, and contrasts static (single-stage) versus dynamic (multi-stage) portfolio optimization. We present how to best fund a pool of similar fixed rate mortgages through issuing bonds, callable and non-callable, of various maturities using stochastic optimization. We di...

A two-stage optimization method is presented by employing the evolutionary structural optimization (ESO) and ant colony optimization (ACO), which is called ESO-ACO method. To implement ESO-ACO, size optimization is performed using ESO, first. Then, the outcomes of ESO are employed to enhance ACO. In optimization process, the weight of double layer grid is minimized under various constraints whi...

in the present study, water allocation of latian dam between agricultural products was evaluated by using inexact two-stage optimization model and the final results were compared to a fuzzy interval. data were collected from the regional water and agricultural organization of tehran province for 1991 to 2009 years. the results showed that by using interval fuzzy programming, 63%, 69%, 49% and 3...

Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizations of the data. We consider the well-studied paradigm of stochastic recourse models, in which the realized input is revealed through a series of stages and one can take decisions in each stage in response to the new in...

In this article, modeling and optimization of power consumption of two–stage compressed air system has been investigated. To do so, the two – stage compressed air cycle with intercooler of FAJR Petroleum Company was considered. This cycle includes two centrifugal compressors, a shell, and a tube intercooler. For modeling of power consumption, actual compressors isentropic efficiencies and inter...

In this article, modeling and optimization of power consumption of two–stage compressed air system has been investigated. To do so, the two – stage compressed air cycle with intercooler of FAJR Petroleum Company was considered. This cycle includes two centrifugal compressors, a shell, and a tube intercooler. For modeling of power consumption, actual compressors isentropic efficiencies and inter...

In the last decade, multi-stage stochastic programs with recourse have been broadly used to model real-world applications. This paper reviews the main optimization methods that are used to solve multi-stage stochastic programs with recourse. In particular, this paper reviews four types of optimization approaches to solve multi-stage stochastic programs with recourse: direct methods, decompositi...

In the field of data-driven optimization under uncertainty, scenario reduction is a commonly used technique for computing smaller number scenarios to improve computational tractability and interpretability. However traditional approaches do not consider decision quality when these scenarios. “Optimization-Based Scenario Reduction Data-Driven Two-Stage Stochastic Optimization,” Bertsimas Mundru ...