Sharif Digital Repository

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... 

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 objective of this research is geometrical and kinematical optimization of full-toroidal continuously variable transmission (CVT) in order to achieve high power transmission efficiency and low mass. At first, a dynamic analysis is performed for the system. A computer model is developed to simulate elastohydrodynamic (EHL) contact between disks and roller and consequently, calculate CVT efficiency. The validity of EHL model is investigated by comparing output of this model and experimental data. Geometrical parameters are obtained by means of Particle Swarm Optimization algorithm, while the optimization objective is to maximize CVT efficiency and minimize its mass. The algorithm is run for... 

High penetration of constant-power loads (CPL) in dc microgrids may cause a destabilizing effect on the system that can lead to severe voltage oscillations. This paper addresses stability problems of the CPLs and proposes a simple active damping technique to damp the oscillations caused by CPLs. The particle swarm optimization algorithm has been used to find the best values of the parameters of the proposed active damper to achieve maximum damping of the oscillations. The effectiveness of the proposed approach is verified by simulations  

This paper presents an effective voltage control strategy for the autonomous operation of a medium voltage (MV) microgrid under nonlinear and unbalanced load conditions. The main objectives of this strategy are to effectively compensate the harmonic and negative-sequence currents of nonlinear and unbalanced loads using distributed generation (DG) units. The proposed control strategy consists of a multi-proportional resonant controller (MPRC) whose parameters are assigned with particle swarm optimization (PSO) algorithm. The optimization function is defined to minimize the tracking error at the specific harmonics considering the stability limitations. In this paper the performance of the... 

Optimizing highway alignment requires a versatile set of cost functions and an efficient search method to achieve the best design. Because of numerous highway design considerations, this issue is classified as a constrained problem. Moreover, because of the infinite number of possible solutions for the problem and the continuous search space, highway alignment optimization is a complex problem. In this study, a customized particle swarm optimization algorithm was used to search for a near-optimal highway alignment, which is a compound of several tangents, consisting of circular (for horizontal design) and parabolic (for vertical alignment) curves. The selected highway alignment should meet... 

Implementation of various incentive-based demand response strategies has great potential to decrease peak load growth and customer electricity bill cost. Using advanced metering and automatic demand management makes it possible to optimize energy consumption, to reduce grid loss, and to release generation capacities for the sake of providing sustainable electricity supply. Executing an incentive-based program is a simple way for customers to monitor and manage their energy consumption, and therefore, to reduce their electricity bill. With these objectives, this paper examines the previously suggested load scheduling programs and proposes a new practical one for residential energy management.... 

This paper presents a novel method fortuning the parameters of an sliding mode (SM) controller to obtain near-optimal performance. In order to do so the Linear Quadratic Regulator (LQR) was implemented on a linearized system. The input history of the LQR was used as a reference to obtain an optimal space for sliding mode controller parameters. Afterwards, the optimal space boundaries were dedicated to Genetic Algorithm (GA) to search for the optimal parameter for the nonlinear model. Also, the center of the obtained optimal space was used as an initial guess to the Particle Swarm Optimization (PSO) Algorithm. The proposed algorithm was implemented to regulate SM controller for the attitude... 

In this paper, a multiproduct inventory control problem is considered in which the periods between two replenishments of the products are assumed independent random variables, and increasing and decreasing functions are assumed to model the dynamic demands of each product. Furthermore, the quantities of the orders are assumed integer-type, space and budget are constraints, the service-level is a chance-constraint, and that the partial back-ordering policy is taken into account for the shortages. The costs of the problem are holding, purchasing, and shortage. We show the model of this problem is an integer nonlinear programming type and to solve it, a harmony search approach is used. At the... 

Hydrogen is a flexible energy carrier and storage medium and can be generated by electrolysis of water. In this research, hydrogen generation is maximized by optimizing the optimal sizing and operating condition of an electrolyzer directly connected to a PV module. The method presented here is based on Particle swarm optimization algorithm (PSO). The hydrogen, in this study, was produced using a proton exchange membrane (PEM) electrolyzer. The required power was supplied by a photovoltaic module rated at 80 watt. In order to optimize Hydrogen generation, the cell number of the electrolyser and its activity must be 9 and 3, respectively. As a result, it is possible to closely match the... 

The objective of this research is geometrical optimization of half toroidal Continuously Variable Transmission (CVT) in order to achieve high power transmission efficiency. The dynamic analysis of CVT is implemented and contact between the disk and the roller is modeled viaelastohydrodynamic (EHL) lubrication principles. Computer model is created using geometrical, thermal and kinetic parameters to determine the efficiency of CVT. Results are compared by other models to confirm the model validity. Geometrical parameters are obtained by means of Particle Swarm Optimization (PSO) algorithm, while the optimization objective is to maximize the power transmission efficiency. Optimization was... 

In this paper, a new algorithm for solving constrained nonlinear programming problems is presented. The basis of our proposed algorithm is none other than the necessary and sufficient conditions that one deals within a discrete constrained local optimum in the context of the discrete Lagrange multipliers theory. We adopt a revised particle swarm optimization algorithm and extend it toward solving nonlinear programming problems with continuous decision variables. To measure the merits of our algorithm, we provide numerical experiments for several renowned benchmark problems and compare the outcome against the best results reported in the literature. The empirical assessments demonstrate that... 

This paper focuses on multi-objective reliability optimization of a two-stage launch vehicle using a hybridized Genetic Algorithm-Particle Swarm Optimization with provisions of relative weighting between the objectives. In this respect, the launch vehicle key subsystems as well as their functions are initially introduced. Subsequently, the system reliability block diagram is constructed using the launch vehicle working order of the subsystems augmented with the requirements for a robust fault/failure tolerant design and performance. Next, based on the proposed reliability block diagram arrangement a bi-objective optimization is formulated to maximize the system reliability while minimizing... 

The main core of HMM-based speech recognition systems is Viterbi algorithm. Viterbi algorithm uses dynamic programming to find out the best alignment between the input speech and a given speech model. In this paper, dynamic programming is replaced by a search method which is based on particle swarm optimization algorithm. The major idea is focused on generating an initial population of segmentation vectors in the solution search space and improving the location of segments by an updating algorithm. Several methods are introduced and evaluated for the representation of particles and their corresponding movement structures. In addition, two segmentation strategies are explored. The first... 

Color quantization is a technique for processing and reduction colors in image. The purposes of color quantization are displaying images on limited hardware, reduction use of storage media and accelerating image sending time. In this paper a hybrid algorithm of GA and Particle Swarm Optimization algorithms with FCM algorithm is proposed. Finally, some of color quantization algorithms are reviewed and compared with proposed algorithm. The results demonstrate Superior performance of proposed algorithm in comparison with other color quantization algorithms  

As a cost-effective and reliable alternative to supply remote areas, standalone hybrid energy systems (HESs) are recently under investigation to address various concerns associated with technical, financial, and environmental issues. This paper presents a comprehensive algorithm that can simultaneously optimize the component size, operation strategy, and slope of the photovoltaic panels of a standalone HES using an improved variant of particle swarm optimization (PSO), designated as the passive congregation PSO. A new operation strategy is proposed based on the set points of the control system. The optimization algorithm determines the optimal values of the set points to efficiently optimize... 

A novel bi-objective three-echelon supply chain problem is formulated in this paper in which cross-dock facilities to transport the products are modeled as an M/M/m queuing system. The proposed model is validated using the epsilon constraint method when applied to solve some small-size problems. Since the problem belongs to the class of NP-hard and that it is of a bi-objective type, a multi-objective particle swarm optimization (MOPSO) algorithm with a new solution structure that satisfies all of the constraints is developed to find Pareto solutions. As there is no benchmark available in literature, three other multi-objective meta-heuristic algorithms called non-dominated ranking genetic... 

Refrigerators have considerable share of residential consumption. They can be, however, flexible loads because their operating time and consumption patterns can be changed to some extent. Accordingly, they can be selected as a target for the study of Demand Side Management plans. In this paper, two experimental models for a refrigerator are derived. In obtaining the first model, following assumptions are made: the ambient temperature of refrigerator is assumed to be constant and the refrigerator door is remained closed. However, in the second model the variation of ambient temperature and door-opening effects are considered according to some general patterns. Further, two strategies are... 

Disease association study is of great importance among various fields of study in bioinformatics. Computational methods happen to be advantageous specifically when experimental approaches fail to obtain accurate results. Haplotypes are believed to be the most responsible biological data for genetic diseases. In this paper, the problem of reconstructing haplotypes from error-containing SNP fragments is discussed For this purpose, two new methods have been proposed by a combination of k-means clustering and particle swarm optimization algorithm. The methods and their implementation results on real biological and simulation datasets are represented which shows that they outperform the methods...