Islamic Azad University at Firoozkooh

www.iaufb.ac.ir
Mazandaran, Iran
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Hajjari T.,Islamic Azad University at Firoozkooh | Abbasbandy S.,Islamic Azad University at Tehran
Expert Systems with Applications | Year: 2011

Recently, Asady [(2010). The revised method of ranking LR fuzzy number based on deviation degree. Expert Systems with Applications, 37, 5056-5060] pointed out that Wang et al.'s method has some drawback by a numerical example and then Wang's method is modified to present an easy way to rank fuzzy numbers. In this note, we will indicate that Asady's revision has a shortcoming exactly as the same as Wang's method. © 2010 Elsevier Ltd. All rights reserved.


Kia R.,Islamic Azad University at Firoozkooh | Baboli A.,INSA Lyon | Javadian N.,Mazandaran University of Science and Technology | Tavakkoli-Moghaddam R.,University of Tehran | And 2 more authors.
Computers and Operations Research | Year: 2012

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems. © 2012 Elsevier Ltd. All rights reserved.


Behzadian M.,University of Tehran | Khanmohammadi Otaghsara S.,Islamic Azad University at Firoozkooh | Yazdani M.,Islamic Azad University at Firoozkooh | Ignatius J.,Universiti Sains Malaysia
Expert Systems with Applications | Year: 2012

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners. © 2012 Elsevier Ltd. All rights reserved.


Rouhani S.,Islamic Azad University at Firoozkooh | Ravasan A.Z.,Allame Tabatabaee University
Scientia Iranica | Year: 2013

The Enterprise Resource Planning system (ERP) has been pointed out as a new information systems paradigm. However, achieving a proper level of ERP success relies on a variety of factors that are related to an organization or project environment. In this paper, the idea of predicting ERP postimplementation success based on organizational profiles has been discussed. As with the need to create the expectations of organizations of ERP, an expert system was developed by exploiting the Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP success. The expert system role is in preparation to obtain data from the new enterprises that wish to implement ERP, and to predict the probable system success level. To this end, factors of organizational profiles are recognized and an ANN model is developed. Then, they are validated with 171 surveyed data obtained from Middle East-located enterprises that experienced ERP. The trained expert system predicts, with an average correlation coefficient of 0.744, which is respectively high, and supports the idea of dependency of ERP success on organizational profiles. Besides, a total correct classification rate of 0.685 indicates good prediction power, which can help firms predict ERP success before system implementation. © 2013 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.


Mosleh M.,Islamic Azad University at Firoozkooh
Iranian Journal of Fuzzy Systems | Year: 2014

In this paper, a novel hybrid method based on learning algorithm of fuzzy neural network and Newton-Cotes methods with positive coefficient for the solution of linear Fredholm integro-differential equation of the second kind with fuzzy initial value is presented. Here neural network is considered as a part of large field called neural computing or soft computing. We propose a learning algorithm from the cost function for adjusting fuzzy weights. This paper is one of the first attempts to derive learning algorithms from fuzzy neural networks with real input, fuzzy output, and fuzzy weights. Finally, we illustrate our approach by numerical examples.


Mosleh M.,Islamic Azad University at Firoozkooh
Applied Soft Computing Journal | Year: 2013

Fuzzy neural network (FNN) can be trained with crisp and fuzzy data. This paper presents a novel approach to solve system of fuzzy differential equations (SFDEs) with fuzzy initial values by applying the universal approximation method (UAM) through an artificial intelligence utility in a simple way. The model finds the approximated solution of SFDEs inside of its domain for the close enough neighborhood of the fuzzy initial points. We propose a learning algorithm from the cost function for adjusting of fuzzy weights. At the same time, some examples in engineering and economics are designed. © 2013 Elsevier B.V.


Akbari A.,Islamic Azad University at Firoozkooh | Arjmandi M.K.,Islamic Azad University at Firoozkooh
Biomedical Signal Processing and Control | Year: 2014

In this work, we are interested in developing an efficient voice disorders classification system by using discrete wavelet packet transform (DWPT), multi-class linear discriminant analysis (MC-LDA), and multilayer neural network (ML-NN). The characteristics of normal and pathologic voices are well described with energy and Shannon entropy extracted from the coefficients in the output nodes of the best wavelet packet tree with eight decomposition level. The separately extracted wavelet packet-based features, energy and Shannon entropy, are optimized with the usage of multi-class linear discriminant analysis to reduced 2-dimensional feature vector. The experimental implementation uses 258 data samples including normal voices and speech signals impaired by three sorts of disorders: A-P squeezing, gastric reflux, and hyperfunction. The voice disorders classification results achieved on Kay Elemetrics databases, developed by Massachusetts Ear and Eye Infirmary (MEEI), show average classification accuracy of 96.67% and 97.33% for the structure composed of wavelet packet-based energy and entropy features, respectively. In these structures, feature vectors are optimized by multi-class linear discriminant analysis and, finally classified by multilayer neural network. The obtained results from confusion matrix and cross-validation tests prove that this novel voice pathology classification system is capable of significant classification improvement with low complexity. This research claims that the proposed voice pathology classification tool can be employed for application of early detection of laryngeal pathology and for assessment of vocal improvement following voice therapy in clinical setting. © 2013 Elsevier Ltd.


Otadi M.,Islamic Azad University at Firoozkooh
Neural Computing and Applications | Year: 2012

In this paper, a new approach for solving system of fully fuzzy nonlinear equations based on fuzzy neural network is presented. This method can also lead to improve numerical methods. In this work, an architecture of fuzzy neural networks is also proposed to find a fuzzy root of a system of fuzzy nonlinear equations (if exists) by introducing a learning algorithm. We propose a learning algorithm from the cost function for adjusting of fuzzy weights. Finally, we illustrate our approach by numerical examples. © 2012 Springer-Verlag London Limited.


Mosleh M.,Islamic Azad University at Firoozkooh | Otadi M.,Islamic Azad University at Firoozkooh
Applied Soft Computing Journal | Year: 2012

In this paper, a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large field called neural computing or soft computing. The model finds the approximated solution of fuzzy differential equation inside of its domain for the close enough neighborhood of the fuzzy initial point. We propose a learning algorithm from the cost function for adjusting of fuzzy weights. Finally, we illustrate our approach by numerical examples and an application example in engineering. © 2012 Elsevier B.V. All rights reserved.


Otadi M.,Islamic Azad University at Firoozkooh
Neurocomputing | Year: 2014

In this paper a polynomial fuzzy regression model with fuzzy independent variables and fuzzy parameters is discussed. Within this paper the fuzzy neural network model is used to obtain an estimate for the fuzzy parameters in a statistical sense. Based on the extension principle, a simple algorithm from the cost function of the fuzzy neural network is proposed, in order to find the approximate parameters. Finally, we illustrate our approach by some numerical examples. © 2014 Elsevier B.V.

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