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Showing 11 results for Fuzzy Logic

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Volume 20, Issue 1 (5-2009)
Abstract

Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrated via real data of a purchase system in order to simulate the system’s behavior.
Hosein Saghaei, Hosein Didehkhani ,
Volume 20, Issue 4 (4-2010)
Abstract

This research aims at presenting a fuzzy model to evaluate and select Six-Sigma projects.  For this purpose, a model of fuzzy analytic network process (ANP) was designed to consider the relation and mutual impact among the factors. In order to evaluate the projects, nine sub-criteria were considered which were classified into three categories of business, finance and procedural ones. Also to consider the ambiguity related to the pairwise comparisons being used in the research, the fuzzy logic was employed. The fuzzy algorithm being used is in the method of Mikhailov which has various advantages such as the presentation of consistency index and weight vector in a crisp form. At the end, in order to show the applicability, the proposed methodology was applied in an automobile part manufacturing firm.
A. Doostparast Torshizi, S.r. Hejazi,
Volume 21, Issue 2 (5-2010)
Abstract

In highly competitive industrial market, the concept of failure analysis is an unavoidable fact in complex industrial systems. Reliability of such systems not only depends on the reliability of each element of these systems, but also depends on occurrence of sequence of failures. In this paper, a novel approach to sequential failure analysis is proposed which is based upon fuzzy logic and the concept of Petri nets which is utilized to track all the risky behaviors of the system and to determine the potential failure sequences and then prioritizing them in order to perform corrective actions. The process of prioritizing failure sequences in this paper is done by a novel similarity measure between generalized fuzzy numbers. The proposed methodology is demonstrated with an example of two automated machine tools and two input/output buffer stocks.
Iman Nosoohi , Seyed Nader Shetab-Boushehri,
Volume 22, Issue 2 (6-2011)
Abstract

  Selection of appropriate infrastructure transportation projects such as highways, plays an important role in promotion of transportation systems. Usually in evaluation of transportation projects, because of lack of information or due to long time and high expenditures needed for gathering information, different effective factors are ignored. Thus, in this research, regarding multi criteria nature of transportation projects selection and using fuzzy logic, an appropriate conceptual framework for ranking and selecting transportation projects is proposed. Also, unlike the previous researches, we've applied a fuzzy inference system (FIS) to account value of each project with respect to each criterion, in the proposed methodology. The FIS helps us to set rule-based systems for paying attention to expert's experience and professional knowledge in decision making. The proposed methodology is explained in detail through an applicable example. We've considered most common criteria including effect of transportation project on traffic flow, economical growth and environment beside budget constraint, in the descriptive example.


Gholam Reza Jalali Naieni, Ahmad Makui, Rouzbeh Ghousi,
Volume 23, Issue 1 (3-2012)
Abstract

Fuzzy Logic is one of the concepts that has created different scientific attitudes by entering into various professional fields nowadays and in some cases has made remarkable effects on the results of the practical researches. However, the existence of stochastic and uncertain situations in risk and accident field, affects the possibility of the forecasting and preventing the occurrence of the accident and the undesired results of it.

In this paper, fuzzy approach is used for risk evaluating and forecasting, in accidents caused by working with vehicles such as lift truck. Basically, by using fuzzy rules in forecasting various accident scenarios, considering all input variables of research problem, the uncertainty space in the research subject is reduced to the possible minimum state and a better capability of accident forecasting is created in comparison to the classic two-valued situations. This new approach helps the senior managers make decisions in risk and accident management with stronger scientific support and more reliably.


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Volume 23, Issue 2 (6-2012)
Abstract

The science parks have important role in development of technology and are able to make economic growth of the countries. The purpose of this paper is the presentation of a Fuzzy Expert System (FIS) as Intelligent Systems to evaluate the science and technology parks. One of the problems for evaluating Science and Technology parks is to have the high number of criteria and science parks which AHP method and some other MCDM methods that with them have evaluated parks are not suitable practically. Therefore presenting a system which is able to compare this high number of science parks with many criteria is one of the findings of this paper. At the end, we have described a numerical example. This paper is a useful information resource for managers of Science and Technology parks and interested parties to invest and to recognize the science parks better.
M.h. Fazel Zarandi, M. Zarinbal,
Volume 23, Issue 4 (11-2012)
Abstract

Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-2 fuzzy clustering is the most preferred method. In recent years, neurology and neuroscience have been significantly advanced by imaging tools, which typically involve vast amount of data and many uncertainties. Therefore, Type-2 fuzzy clustering methods could process these images more efficient and could provide better performance. The focus of this paper is to segment the brain Magnetic Resonance Imaging (MRI) in to essential clusters based on Type-2 Possibilistic C-Mean (PCM) method. The results show that using Type-2 PCM method provides better results.
Farnad Nasirzadeh, Hamid Reza Maleki, Mostafa Khanzadi, Hojjat Mianabadi,
Volume 24, Issue 1 (2-2013)
Abstract

Implementation of the risk management concepts into construction practice may enhance the performance of project by taking appropriate response actions against identified risks. This research proposes a multi-criteria group decision making approach for the evaluation of different alternative response scenarios. To take into account the uncertainties inherent in evaluation process, fuzzy logic is integrated into the revaluation process. To evaluate alternative response scenarios, first the collective group weight of each criterion is calculated considering opinions of a group consisted of five experts. As each expert has its own ideas, attitudes, knowledge and personalities, different experts will give their preferences in different ways. Fuzzy preference relations are used to unify the opinions of different experts. After computation of collective weights, the best alternative response scenario is selected by the use of proposed fuzzy group decision making methodology which aggregates opinions of different experts. To evaluate the performance of the proposed methodology, it is implemented in a real project and the best alternative responses scenario is selected for one of the identified risks.
Mahdi Ruhparvar, Hamed Mazandarani Zadeh, Farnad Nasirzadeh,
Volume 25, Issue 2 (5-2014)
Abstract

An equitable risk allocation between contracting parties plays a vital role in enhancing the performance of the project. This research presents a new quantitative risk allocation approach by integrating fuzzy logic and bargaining game theory. Owing to the imprecise and uncertain nature of players’ payoffs at different risk allocation strategies, fuzzy logic is implemented to determine the value of players’ payoffs based on the experience and subjective judgment of experts involved in the project. Having determined the players' payoffs, bargaining game theory is then applied to find the equitable risk allocation between the client and contractor. Four different methods including symmetric Nash, non-symmetric Nash, non-symmetric Kalai–Smorodinsky and non-symmetric area monotonic are implemented to determine the equitable risk allocation. To evaluate the performance of the proposed model, it is implemented in a pipeline project and the quantitative risk allocation is performed for the inflation risk as one of the most significant identified risks.
Dr. Yahia Zare Mehrjerdi, Mahnaz Zarei,
Volume 26, Issue 2 (7-2015)
Abstract

Abstract Nowadays supply chain management has become one of the powerful business concepts for organizations to gain a competitive advantage in global market. This is the reason that now competition between the firms has been replaced by competitiveness among the supply chains. Moreover, the popular literature dealing with supply chain is replete with discussions of leanness and agility. Agile manufacturing is adopted where demand is volatile while lean manufacturing is used in stable demands. However, in some situations it is advisable to utilize a different paradigm, called leagility, to enable a total supply chain strategy. Although, various generic hybrids have been defined to clarify means of satisfying the conflicting requirements of low cost and fast response, little research is available to provide approaches to enhance supply chain leagility. By linking Leagile Attributes and Leagile Enablers (LAs and LEs), this paper, based upon Quality Function Deployment (QFD), strives to identify viable LEs to achieve a defined set of LAs. Due to its wide applicability, AHP is deployed to prioritize LAs. Also, fuzzy logic is used to deal with linguistics judgments expressing relationships and correlations required in QFD. To illustrate the usefulness and ease of application of the approach, the approach was exemplified with the help of a case study in chemical industry.

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Eng Fateme Zare Baghabad, Dr Hassan Khademi Zare,
Volume 26, Issue 3 (9-2015)
Abstract

In this paper an efficient three- stage algorithm is developed for software production cost and time estimation. First stage includes a hybrid model composed of COCOMO and Function Points methods to increase estimation accuracy. Second stage encompasses paired comparisons matrix of analytical hierarchy process to determine amount of any resources consumed in each step of software production by experts’ opinions. Third stage concludes cost and time tables of production scheduling by using Work break structure (WBS) and network models of project control. In whole of all stages of this paper, triangular fuzzy numbers are used to express uncertainty existed in succession and repetition of each production step, time of beginning, ending, the duration of each task and costs of them. Retrieved results examined by 30 practical projects conclude accuracy of 93 percent for time estimation and 92 percent for cost one. Also suggested algorithm is more accurate than COCOMOІІ 2000 algorithm as 50 percent based on examined problems.

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