Sharif Digital Repository

Ant colony optimization (ACO) is a well-known metaheuristic in which a colony of artificial ants cooperate in exploring good solutions to a combinatorial optimization problem. In this paper, an ACO algorithm is presented for the graph coloring problem. This ACO algorithm conforms to max-min ant system structure and exploits a local search heuristic to improve its performance. Experimental results on DIMACS test instances show improvements over existing ACO algorithms of the graph coloring problem  

Owing to wind power inherent characteristics and technical constraints of power systems operation, a considerable amount of wind energy cannot be delivered to load centers and gets curtailed. Transmission congestion together with temporal mismatch between load and available wind power can be accounted as the main reasons for this unpleasant event. This paper aims to concentrate on the wind energy curtailment for which it provides a combinatorial planning model to maximize wind power utilization. Jointly operating the wind power generation system with pumped hydro energy storage (PHES), the planning procedure tries to reach schemes with the minimum level of wind energy curtailment as well as... 

Presence of energy hubs in the future vision of energy networks creates a great opportunity for system planners and operators to move towards more efficient systems. The role of energy hubs as the intermediate in multi-carrier energy (MCE) systems calls for a generic framework to study the new upcoming technical as well as economical effects on the system performance. In response, this paper attempts to develop a general optimization and modeling framework for coupled power flow studies on different energy infrastructures. This, as a large-scale nonlinear problem, is approached through a robust optimization technique, i.e., multi-agent genetic algorithm (MAGA). The proposed procedure... 

Multiple energy carriers (MECs) have been broadly engrossing power system planners and operators toward a modern standpoint in power system studies. Energy hub, though playing an undeniable role as the intermediate in implementing the MECs, still needs to be put under examination in both modeling and operating concerns. Since wind power continues to be one of the fastest-growing energy resources worldwide, its intrinsic challenges should be also treated as an element of crucial role in the vision of future energy networks. In response, this paper aims to concentrate on the online economic dispatch (ED) of MECs for which it provides a probabilistic ED optimization model. The presented model... 

Planning of distribution networks is usually accomplished using a cost-based conventional model that minimizes the total cost of construction and reinforcement of substations and feeders. The resulted expansion plan designed by this cost-based model is not necessarily the best choice for an electrical distribution company (DISCO) in the nowadays deregulated structure. This happening is mainly due to the fact that the utility seeks to maximize its profit in this competitive environment. On the other hand, this model does not consider the upcoming new challenges ahead of distribution systems, such as smart grid and distribution automation technologies. This paper presents a novel profit-based... 

This paper is the second part of a two-paper set which comprehensively sets forth an innovative approach in transmission grid reinforcement studies in the presence of wind energy. Part I thoroughly defined the theory and algorithms. Here, to trace the feasibility of the proposed algorithm, three different case studies are implemented on the 24-Bus IEEE Reliability Test System (IEEE-RTS). The optimal solutions in Pareto fronts of different cases are reached, analyzed, and the final solution (optimal plan) of each case is obtained using the fuzzy decision making method. Moreover, in order to analyze the effects of variations in the large-scale wind farm generation on the transmission expansion... 

With increment in the penetration of wind energy in power systems, the necessity of considering its impacts on transmission expansion planning (TEP) studies, especially for large scale wind farms, is inevitable. A new multi-objective (MO) optimization transmission expansion planning algorithm considering wind farm generation is presented in this two-paper set. Part I is mainly devoted to derivation of the theory and algorithm. The objective functions used in the TEP studies take into account investment cost, risk cost and congestion cost. The combination of Monte Carlo simulation (MCS) and Point Estimation Method (PEM) is implemented to investigate the effects of network uncertainties. Due... 

Differential Evolution (DE) and Genetic Algorithms (GA) are population based search algorithms that come under the category of evolutionary optimization techniques. In the present study, these evolutionary methods have been utilized to conduct the optimum design of a fuzzy controller for mobile robot trajectory tracking. Comparison between their performances has also been conducted. In this paper, we will present a fuzzy controller to the problem of mobile robot path tracking for a CEDRA rescue robot. After designing the fuzzy tracking controller, the membership functions will be optimized by evolutionary algorithms in order to obtain more acceptable results  

The discrete fracture network (DFN) and Multiple-Continua concept are among the most widely used methods to model naturally fractured reservoirs. Each faces specific limitations. The recently introduced recovery curve method (RCM) is believed to be a compromise between these two current methods. In this method the recovery curves are used to determine the amount of mass exchanges between the matrix and fracture mediums. Two recovery curves are assigned for each simulation cell, one curve for gas displacement in the presence of the gravity drainage mechanism, and another for water displacement in the case of the occurrence of the imbibition mechanism. These curves describe matrix-fracture... 

Optimization of any production operation is a tool for increasing production rates and reducing production costs. Water flooding is one of the techniques that frequently be used to increase oil recovery after primary depletion. A methodology for optimizing the production by using the net present value of a heterogeneous reservoir under water flooding has been presented, which is based on controlling the bottomhole pressures of the production wells, using smart well technology. For this purpose, a numerical flow simulator is coupled with an optimization program. The technique was implemented on a synthetic two dimensional oil reservoir with heterogeneous permeability. This optimization... 

Decision on the location of new wells through infill drilling projects is a complex problem that depends on the reservoir rock and fluid properties, well and surface facilities specifications, and economic measures. Conventional approach to address this is a direct optimization that uses the numerical flow simulation. However, this is computationally very extensive. In this study the authors use a hybrid genetic algorithm (HGA) optimization technique based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the neural network. This hybridization introduces hill-climbing into the stochastic search and makes use of proxies created and calibrated iteratively... 

Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. In the literature on machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variables. There are endeavors to use regression methods to... 

Fast charging stations are vital components for public acceptance of electric vehicle (EV). The stations are connected to the electric grid and can recharge an electric vehicle in less than 20. min. Charging station development is highly influenced by the government policy in allocating station development costs. This paper presents a Mixed-Integer Non-Linear (MINLP) optimization approach for optimal placing and sizing of the fast charging stations. The station development cost, EV energy loss, electric gird loss as well as the location of electric substations and urban roads are among the factors included in the proposed approach. Geographic information has been used to determine EV energy... 

Vendor-managed inventory (VMI) is a popular policy in supply chain management (SCM) to decrease bullwhip effect. Since the transportation cost plays an important role in VMI and because the demands are often fuzzy, this paper develops a VMI model in a multi-retailer single-vendor SCM under the consignment stock policy. The aim is to find optimal retailers' order quantities so that the total inventory and transportation cost are minimized while several constraints are satisfied. Because of the NP-hardness of the problem, an algorithm based on particle swarm optimization (PSO) is proposed to find a near optimum solution, where the centroid defuzzification method is employed for... 

When the reservoir energy is too low for the well to flow, or the production rate desired is greater than the reservoir energy can deliver, using some kind of artificial lift method to provide the energy to bring the fluid to the surface, seems to be necessary. Continuous flow gas lift is one of the most common artificial lift methods widely used in the oil industry during which, at appropriate pressure, gas is injected in a suitable depth into the tubing to gasify the oil column, and thus assist the production. Each well has an optimal point at which it will produce the most oil. In ideal conditions, at which there is no limitation in the total amount of available gas, a sufficient amount... 

The aim of the present work is to propose an eco-design method for sustainable development of methanol production by implementing a multi-objective optimization model based on CO2-efficiency. Two different approaches for the methanol production, i.e. a conventional reference methanol case (RMC) and proposed green integrated methanol case (GIMC) were compared from the view point of eco-design. Using life cycle assessment and process simulation, the environmental features as well as operational decision variables of the RMC and GIMC were assessed. Based on the inventory analysis of LCA, it was found that carbon dioxide is the major emitted pollutant from methanol production. Thus the... 

The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a single-vendor single-buyer supply chain system, in which the vendor is responsible to manage the buyer's inventory. To include an extended applicability in real-world environments, the multiproduct economic production quantity model with backordering under three constraints of storage capacity, number of orders, and available budget is considered. The nonlinear programming model of the problem is first developed to determine the near optimal order quantities along with the maximum backorder levels of the products in a cycle such that the total VMI inventory cost of the system is minimized. Then, a genetic... 

In this paper, a seasonal multi-product multi-period inventory control problem is modeled in which the inventory costs are obtained under inflation and all-unit discount policy. Furthermore, the products are delivered in boxes of known number of items, and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the total storage space and total available budget are limited. The objective is to find the optimal number of boxes of the products in different periods to minimize the total inventory cost (including ordering, holding, shortage, and purchasing costs). Since the integer nonlinear model of the problem is hard to solve using exact methods, a... 

Optimal well placement is a crucial step in reservoir development process. The key points in such an optimization process are using a fast function evaluation tool and development of an efficient optimization algorithm. This study presents an approach that uses particle swarm optimization algorithm in conjunction with streamline simulation to determine the optimum well locations within a reservoir, regarding a modified net present value as the objective. Performance of this algorithm was investigated through several different examples, and compared to that of genetic algorithm (GA) and simulated annealing (SA) methods. It was observed that particle swarm optimization algorithm outperformed... 

The redundancy allocation is one of the most important and useful problems in system optimization, especially in electrical and mechanical systems. The object of this problem is to maximize system reliability or availability within a minimum operation cost. Many works have been proposed in this area so far to draw the problem near to real-world situations. While in classic models the system components are assumed to have two states of working and failed, in this paper, parallel components of serial sub-systems are considered to work in three states, each with a certain performance rate. The component states are classified into two working states of working with full performance and working...