Sharif Digital Repository

Employing profiles using variable sample size (VSS) may improve its effectiveness and hence is investigated in this paper. Besides, the implementation cost of a profile is an important factor in determining the main parameters. In this paper, the Lorenzen-Vance function is first extended to model implementation cost of VSS linear profiles. Then, the resulted economic model is solved using a genetic algorithm (GA). The average run length criterion when process is in control, ARL0 and the average run length measure when process goes out of control, ARL1 , is used to evaluate statistical properties of the designed profile. Moreover, a sensitivity analysis on the main parameters of the... 

The present paper introduces a novel two-part self-adaptive technique in designing the genetic algorithm for project scheduling problems. One part of the algorithm includes a self-adaptive mechanism for genetic operators like crossover and mutation. The second part contains another self-adaptive mechanism for genetic parameters such as crossover probability. The parts come in turn repeatedly within a loop feeding each other with the information regarding the performance of operators or parameters. The capability of the method is tested and confirmed in comparison to metaheuristic and exact algorithms based on well-known benchmarks  

In this work, a bi-objective multi-facility location-allocation problem is investigated, in which the locations of the customers and their arrivals are stochastic. We first formulate the problem as a continuous location-allocation model with no constraints on the capacity of the facilities. Then, we develop an approximated discrete model in which the facilities with limited capacities can be located on a set of candidate points. The proposed model has two objective functions that are evaluated using discrete event system simulation. The first objective is to minimize the expected total time the customers spend in the system until their services begin. The time that each customer spends in... 

Optimal queuing system design includes deciding about some variables such as location, capacity, price, and delivery time. In order to develop an optimized system, it is essential to solve the optimization problem for all the decision variables simultaneously. In this paper, a make-to-order system is considered. In this system, some facilities are developed in different points. According to the make-to-order system definition, these facilities keep no inventory from final product. Orders are received from customers and the requested products are assembled. Due to existence of some differences among customers, they are divided into two distinct categories including express and regular... 

Sensor networks have recently emerged as a premier research topic. Sensor networks pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. While designing the sensor networks according to performed computation, the limited number of sensors to cover an area will be considered, so the proper placing of this limited number of sensors will cause costs to reduce regarding to coverage and development of the network in the next stage. In this paper we will present a genetic algorithm to solve the designing issue of the sensor network. The most important... 

A method based on the Genetic Algorithm (GA) was developed to study the phase behavior of multicomponent and multiphase systems. Upon application of the GA to the thermodynamic models which are commonly used to study the VLE, VLLE and LLE phase equilibria, the physically meaningful values for the Binary Interaction Parameters (BIP) of the models were obtained. Using the method proposed in this work the activity coefficients for components at infinite dilution, obtained from the local composition based models, can be accurately predicted comparing to the experimental data available in the literature. In this work, a Global Optimization Procedure (GOP) based on the GA was developed to obtain... 

For large and complex reacting systems, computational efficiency becomes a critical issue in process simulation, optimization and model-based control. Mechanism simplification is often a necessity to improve computational speed. We present a novel approach to simplification of reaction networks that formulates the model reduction problem as an optimization problem and solves it using genetic algorithm (GA).The aim of simplification kinetics modeling is to derive the simplest reaction system, which retains the essential features of the full system. Numerical results for H2/O2 combustion reaction mechanism illustrate the potential and proficiency of this approach  

4E analysis is used on a Brayton-Rankine combined cycle power plant (CCPP) with a dual pressure heat recovery steam generation (HRSG) system. A multi-objective genetic-based evolutionary optimization has been used to estimate the most optimal exergy efficiency status, exergy cost reduction, carbon emission reduction, and NOx emission reduction. For the validation of the data, the simulation results are compared with the plant's data. This study investigates the effect of every decisive parameter on the objective performance parameters of the CCPP. The primary estimated results are the emission rates, efficiencies, and the exergoeconomic cost of the system. At the optimum operational state,... 

Nowadays, deliberate or unwanted fire incidents have created much attention to the behavior of structures against these types of events. Since the properties of structural members are influenced by the increase in the temperature of the members, it is more difficult to predict the general and local behavior of the structures during the fire. In this research, a method has been proposed to calculate the critical temperature in two-dimensional structures at its collapse with desirable accuracy. In this process, the upper-bound theory of plastic analysis is used. The plastic analysis is performed by applying the initial fire scenario to the structure, and its collapse load factor with the... 

This study is focused on the development of a systematic computational approach which implements Genetic Algorithm (GA) to find the optimal rigorous kinetic models. A general Kinetic model for hydrogenolysis of dibenzothiophene (DBT) based on Langmuir Hinshelwood type has been obtained from open literature. This model consists of eight continuous parameters(e.g., Arrhenus and Van't Hoff parameters) and six discrete parameters representing the order of the reaction with respect to each concentration. The optimal value of these parameters have been obtained based on Genetic Algorithm. Furthermore, the best type of Genetic operators and their corresponding parameters for this type of problems... 

Shape optimization of a submarine for minimized noise emission has been the objective of this work. Boundary element method and finite element methods have been employed to determine the acoustic field around the object. A combined neural network and genetic algorithm scheme is developed to find the optimum external geometric ratios of the submarine for a minimized emitted acoustic energy in certain reference points. The obtained optimum geometric values stay in normal range for minimum hydrodynamic forces and show a close agreement with the trend of change for models coming to operation. © 2004 IEEE  

In this paper we discuss an improved method for the calculation of Available Transfer Capability (ATC) in a restructured power system. The ATC calculation is performed through an optimal power flow approach. The objective function is to maximize the sum of the receiving-end load in a specified area as well as to send generation to other control areas while a minimum cost of generation is continuously achieved. The constraints are ac power flow equations and system operation limits. It is assumed that the generation cost curve of each generator is available. A genetic algorithm is used for the optimization process. An IEEE 30 bus test system is used for testing the proposed algorithm and the... 

A special hybrid genetic algorithm (GA) is designed to solve the long-term coordination of hydro-thermal power systems with cascaded reservoirs and stochastic inflows. Since decision variables are continuous, in the proposed GA we employ real number rather than binary encoding. To create superior children we introduce dynamic tuning of the weights of operators. An exponential normalization is also developed such that better chromosomes have more chance to reproduce. To test the proposed method, 16 GAs are investigated which differ based on real or binary encoding, dynamic tuning or fixed weights for operators, inverse or exponential normalization and mixed or pure random initial populations.... 

This paper presents a meta-heuristic algorithm for solving bi-objective fuzzy job shop scheduling problems. These objectives are to minimize the makespan and minimize the early and late penalty. Processing time and due date are considered as fuzzy triangular numbers. This paper also introduces a novel use of data mining algorithm for solving of combinatorial optimization problems. The proposed algorithm combines genetic algorithms and an attribute-oriented induction algorithm, which is much quicker than previous methods providing the optimal solution. By considering the structure of proposed algorithm, the whole feasible solutions of a special job shop-scheduling problem are considered as a... 

This article presents an overview of the mechanical design features, fabrication and control of a Rescue Robot (CEDRA) for operation in unstructured environments. As a preliminary step, the essential characteristics of a robot in damaged and unstable situations have been established. According to these features and kinematical equations of the robot, design parameters are optimized by means of Genetic Algorithm. Optimum parameters are then utilized in construction. Upon fabrication, this unit has been tested in clean laboratory environment, as well as, ill-conditioned arenas similar to earthquake zones. The obtained results have been satisfactory in all aspects, and improvements are... 

Control of MMA polymerization batch reactor has intensively investigated. The nonlinear and time varying behavior of the system makes its control a challenging task. MPC algorithm is enjoying an increasing application for control of chemical processes. A sequential linearized model based predictive controller based on the DMC algorithm was designed to control the temperature of a batch MMA polymerization reactor. A genetic algorithm (GA) is suggested to optimize the cost function of DMC. The controller performance was studied via simulation. The controller performance in tracking the profile, noise and disturbances rejection is very good  

Due to the deregulation and restructuring in many countries, it is expected that amount of small-scale generations connected to the distribution networks increase. So it is necessary that impact of these kinds ofgenerators on VoltNar control would be investigated. This paper presents a new approach for VoltNar control in distribution system with Distributed Generation (DG) and it has shown that DG can improve the entire performance of network, by means of better controlling of the system and decreasing losses in network. In this approach Genetic Algorithm (GA) has been used as the optimization method where the amount of Dispersed Generation and its controlling parameters, Voltage regulators... 

In this paper, two new approaches, using the combination of Hopfield Neural Network and Genetic Algorithm for solving the Robot Motion Planning Problem both in Crisp and Fuzzified environments are presented. Based on the hypothesis of Genetic Algorithms, the Genomes and Chromosomes of the algorithm are modified so that they can he used to solve the Motion Planning Problem. Because of some problem restrictions and limits hinder us to use the generic Genetic Algorithm; some modifications are applied to the main algorithm to able us to solve the problem. Although the proposed algorithms are both rely on Genetic Algorithm, the heart of both is based on Hopfield Neural Network Robot Path Planner... 

The sidelobe level (SLL) of a linear array is optimized using modified continuous genetic algorithms (GAs) in this work. The amplitude and phase of the current as well as the separation of the antennas are all taken as variables to be controlled. The results of the design using modified GA versions are compared with other methods. Two design problems were studied several continuous modified GA version and the results are presented as several plots. As a final example, the design specifications for an array with 200 elements is given. The effectiveness and advantages of the modified GA version is outlined  

Nowadays, deliberate or unwanted fire incidents have created much attention to the behavior of structures against these types of events. Since the properties of structural members are influenced by the increase in the temperature of the members, it is more difficult to predict the general and local behavior of the structures during the fire. In this research, a method has been proposed to calculate the critical temperature in two-dimensional structures at its collapse with desirable accuracy. In this process, the upper-bound theory of plastic analysis is used. The plastic analysis is performed by applying the initial fire scenario to the structure, and its collapse load factor with the...