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...

This paper presents a new robust two-stage Data Envelopment Analysis (DEA) for efficiency evaluation of the electricity power production and distribution companies. DEA has been widely used for benchmarking the electricity companies. Traditional studies in DEA consider systems as a whole when measuring the efficiency, ignoring the operation of individual processes within a system. To tackle thi...

We describe a two-stage robust optimization approach for solving network flow and design problems with uncertain demand. In two-stage network optimization one defers a subset of the flow decisions until after the realization of the uncertain demand. Availability of such a recourse action allows one to come up with less conservative solutions compared to single-stage optimization. However, this ...

The development of optimization and mathematical models for closed loop supply chain (CLSC) design has attracted considerable interest over the past decades. However, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of the developed tools. The aim of this paper, therefore, is to propose a n...

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...

Distillation columns are complex processes for modeling and controlling. These columns are significant parts of most chemical industries for separation of components. Control of this process is essential for achieving certain purity for products with a minimum cost. However, nonlinearities, multivariable interaction, non-stationary behavior and severity of disturbances inside the column made th...

In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new...

We describe a two-stage robust optimization approach for solving network flow and design problems with uncertain demand. In two-stage network optimization, one defers a subset of the flow decisions until after the realization of the uncertain demand. Availability of such a recourse action allows one to come up with less conservative solutions compared to singlestage optimization. However, this ...

It is important to share demand information among the members in supply chains. In recent years, production and inventory systems with advance demand information (ADI) have been discussed, where advance demand information means the information of demand which the decision maker obtains before the corresponding actual demand arrives. Appropriate production and inventory control using demand info...

in this paper, a stochastic two-stage model is offered for optimization of the day-ahead scheduling of the microgrid. system uncertainties including dispatchable distributed generation and energy storage contingencies are considered in the stochastic model. for handling uncertainties, monte carlo simulation is employed for generation several scenarios and then a reduction method is used to d...