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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. Source


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. Source


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. Source


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. Source


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. Source

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