Sharif Digital Repository

In this paper, a multi-item multiperiod inventory control problem with all-unit and/or incremental quantity discount policies under limited storage capacity is presented. The independent random demand rates of the items in the periods are known and the items are supplied in distinct batch sizes. The cost consists of ordering, holding, and purchasing. The objective is to find the optimal order quantities of all items in different periods such that the total inventory cost is minimized and the constraint is satisfied. A mixed binary integer programming model is first developed to model the problem. Then, a parameter-tuned genetic algorithm (GA) is employed to solve it. Since there is no... 

In this paper, a multi-product economic production quantity problem with limited warehouse-space is considered in which the orders are delivered discretely in the form of multiple pallets and the shortages are completely backlogged. We show that the model of the problem is a constrained non-linear integer program and propose a genetic algorithm to solve it. Moreover, design of experiments is employed to calibrate the parameters of the algorithm for different problem sizes. At the end, a numerical example is presented to demonstrate the application of the proposed methodology  

The Stability of machining process depends on the dynamics of the machine tool, among other things. However, the dynamics of the machine tool changes when the tool is changed. To avoid the need for repeating the measurements, sub-structuring analysis may be used to couple the tool and spindle frequency response functions. A major difficulty in this approach is the determination of joint stiffness and damping between the two sub-structures. In particular, the measurement of rotational responses (RDOFs) at joints is a difficult task. In this research, a simple joint model that accounts for RDOFs is proposed. It is shown that this model avoids RDOF measurement while taking into account the... 

A hybrid genetic algorithm (HGA) was introduced to allocate optimum amount of gas. This method was applied to a group of wells in gas lift in the case of availability limited amount of gas. For testing the ability of the proposed HGA, the results of this work with those of previous works in a field with six wells were compared. Then for an Iranian southern west oil field with nine wells, gas allocation is performed using different amount of available gas. The results show that the introduced method (HGA) is very efficient tool in gas allocation issue  

A flexible flow shop is a generalized flow shop with multiple machines in some stages. This system is fairly common in flexible manufacturing and in process industry. In most practical environments, scheduling is an ongoing reactive process where the presence of real time information continually forces reconsideration of pre-established schedules. This paper studies a flexible flow shop system considering non-deterministic and dynamic arrival of jobs and also sequence dependent setup times. The problem objective is to determine a schedule that minimizes average tardiness of jobs. Since the problem class is NP-hard, a novel dispatching rule and hybrid genetic algorithm have been developed to... 

In this paper, the capabilities of tuned mass dampers (TMDs) for the mitigation of response of nonlinear frame structures subjected to earthquakes have been studied. To determine the optimal parameters of a TMD, including its mass, stiffness and damping, we developed an optimization algorithm based on the minimization of a performance index, defined as a function of the response of the nonlinear structure to be controlled. Distributed genetic algorithm has been used to solve the optimization problem. For illustration, the method has been applied to the design of a linear TMD for an eight-story nonlinear shear building with bilinear hysteretic material behavior subjected to earthquake. The... 

In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at the core of simulated annealing heuristic. Results of proposed approach have been compared with several well-known methods such as Naïve Bayes, Support Vector Machine, Decision Tree, k-NN, and GBML, and show that our method performs the classification task as well as other famous algorithms. ©... 

This paper presents a novel approach for power allocation and cooperative node selection to enhance physical layer security in presence of an eavesdropper in a wireless network. Our network consists of a source–destination pair and a number of cooperative nodes which can be used as relays to increase throughput at the destination, or as friendly jammers to confuse eavesdropper. First, we introduce a low complexity method, for which relay−jammer selection and power allocation are performed, simultaneously. Then, we propose self-adaptive genetic algorithm to solve the non-linear non-convex programing problem. Using the proposed method, the number of friendly jammers that ensure the secrecy... 

In this paper, the effect of various arrangements of displacement, velocity and acceleration related weighting matrices on the performance of active control systems on nonlinear frames has been studied. Different arrangements of weighting matrices and feedback combinations of the response have been considered to design the active controllers using a single actuator for reducing the response of an eight-storey bilinear hysteretic frame under white noise excitations. The nonlinear Newmark-based instantaneous optimal control algorithm has been used, where the distributed genetic algorithm (DGA) is employed to determine the proper set of weighting matrices. For each set of feedback and weighting... 

The goal of this work is to propose a hybrid algorithm called real-coded self-organizing migrating genetic algorithm by combining real-coded genetic algorithm (RCGA) and self-organizing migrating algorithm (SOMA) for solving bound-constrained nonlinear optimization problems having multimodal continuous functions. In RCGA, exponential ranking selection, whole-arithmetic crossover and non-uniform mutation operations have been used as different operators where as in SOMA, a modification has been done. The performance of the proposed hybrid algorithm has been tested by solving a set of benchmark optimization problems taken from the existing literature. Then, the simulated results have been... 

We are concerned with the design of a model and an algorithm for computing the shortest path in a network having various types of fuzzy arc lengths. First, a new technique is devised for the addition of various fuzzy numbers in a path using α-cuts by proposing a least squares model to obtain membership functions for the considered additions. Due to the complexity of the addition of various fuzzy numbers for larger problems, a genetic algorithm is presented for finding the shortest path in the network. For this, we apply a recently proposed distance function for comparison of fuzzy numbers. Examples are worked out to illustrate the applicability of the proposed approach  

The architecture of the Cascading TMR (Triple Modular Redundancy) System is commonly found in aerospace applications. The system is potentially capable of containing a large number of modules and sub modules. In this paper a novel method for optimization of a Cascading TMR system configuration using GA (Genetic Algorithm) is proposed. The proposed algorithm provides a configuration that maximizes reliability and minimizes overhead area simultaneously  

LBP coefficients are essential and determine the priority of gray differences. The objectives of this paper are to reveal this and propose a method for finding an optimal priority through the genetic algorithm. On the other hand, the genetic operators such as initialization and cross-over operators, generate invalid coefficients, defective chromosomes. This paper also recommends a rectifying method for correcting defective chromosomes. Results on the FERET and Extended Yale B datasets indicate that the proposed method has markedly higher recognition rates than LBP. © 2017 IEEE  

Robustness against attacks is an important requirement in image watermarking. This paper presents a robust watermarking algorithm in wavelet transform domain. Firstly, original image is decomposed into its subbands using three level wavelet transform, then, significant coefficients with the same position in HL, LH and HH subbands of the last level are extracted to make a triplet. To embed a watermark bit into a triplet, the standard deviation of triplet coefficients magnitude is set to zero for zero bit or increased for a one bit. Three constant are used to increase the standard deviations of a triplet coefficients. The value of these constants affects the robustness of algorithm and the... 

The canonical problems in control of the biped robots arise from underactuation, impulsive nature of the impact with the environment and existence of the many degrees of freedom in their mechanism. Since biped walkers have fewer actuators than degrees of freedom, they are underactuated mechanical systems. In this paper according to the humans and animals locomotion algorithms, the stability of an underactuated biped walker with point feet is done by Central Pattern Generator (CPG) and feedback networks. For tuning the parameters of the CPG network, the control problem is defined as an optimization problem. This optimization problem is solved by using of Genetic algorithm. Also a new feedback... 

In this paper, two methods to solve multi-response statistical problems are presented. In these methods, desirability function, genetic algorithm and simulation methodology are applied. The desirability function is responsible for modeling the multi-response statistical problem, the genetic algorithm tries to optimize the model and, finally, the simulation approach generates the required input data from a simulated system. The methods differ from each other in controlling the randomness of the problem. In the first method, replications control this randomness, while, in the second method, the randomness is controlled by a statistical test. Furthermore, these methods are compared by designed... 

In this research, a bi-objective optimisation model for the joint replenishment problem (JRP) of a supply chain comprised of a single supplier and multiple retailers is developed, in which the retailers are assumed to be members of a unique distribution company. The mathematical model minimises the supplier's as well as the retailers' cost subject to some constraints. The constraints are the required storage spaces for any retailer, for any product, and for all the products. The benefit of using the JRP policy is shown based on minimising total cost of supplier and retailers. Since the developed model of the problem is NP-hard, the multi-objective meta-heuristic optimisation algorithm of... 

In this research, a bi-objective optimisation model for the joint replenishment problem (JRP) of a supply chain comprised of a single supplier and multiple retailers is developed, in which the retailers are assumed to be members of a unique distribution company. The mathematical model minimises the supplier's as well as the retailers' cost subject to some constraints. The constraints are the required storage spaces for any retailer, for any product, and for all the products. The benefit of using the JRP policy is shown based on minimising total cost of supplier and retailers. Since the developed model of the problem is NP-hard, the multi-objective meta-heuristic optimisation algorithm of...