International Journal of Industrial Engineering & Production Research

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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.

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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.

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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.

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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.

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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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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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The present paper aims to propose a fuzzy multi-objective model to allocate order to supplier in uncertainty conditions and for multi-period, multi-source, and multi-product problems at two levels with wastages considerations.  The cost including the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects, partial coverage of suppliers in respect of distance and finally, suppliers weights which make the products orders more realistic are considered as the measures to evaluate the suppliers in the proposed model. Supplier's weights in the fifth objective function are obtained using fuzzy TOPSIS technique. Coverage and wastes parameters in this model are considered as random triangular fuzzy number. Multi-objective imperial competitive optimization (MOICA) algorithm has been used to solve the model,. To demonstrate applicability of MOICA, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms and results are analyzed using quantitative criteria and performing parametric. 

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Due to the multiplicity of standards and complex rules; maintenance, repair and servicing of machinery could be done only by the fully qualified and proficient experts. Since the knowledge of such experts is not available all times, using expert systems can help to improve the maintenance process. To address this need and the uncertainty of the maintenance process indicators, this research proposed a Fuzzy Expert Systems (FES) for decision making on the type of service. Since all indicators identified in the literature aren’t important adequately, more influential indicators in the service type selection are chosen using inferential statistical analysis firstly. Then, the fuzzy rules of the knowledge based were designed by these selected indicators. Finally, Inference engine has been designed based on Mamdani model to detect the service type of equipment. This research selected Shemsh Sazan Zanjan Company as a case study to implement the proposed expert system. According to our experiments, the proposed system increases the reliability by suggesting effective ideas that lead to decrease production line breakdowns. The main contribution of this paper is providing a new approach for designing maintenance dynamic FES based on Maintenance Indicators for service type selection that can decrease production line breakdowns.

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The health insurance system can play an effective role to control health expenditures. The purpose of this study is to provide a model for estimating the physician visit tariffs. To achieve this goal, a hybrid model was used. fuzzy logic is the most appropriate tool for controlling systems and deriving rules for the relationship between inputs and outputs. So, the output of the data mining techniques enter the fuzzy logic as an input variable. The data were collected from the Health Insurance Organization of Iran in two sections including the physicians' costs and physicians' deductions. Owing to the techniques used in this model, NN had the least error, as compared to other data mining techniques (0.0034 and 0.0013, respectively). After defining the variables, membership functions and fuzzy logic rules, the accuracy of the whole control model was confirmed by random data. This research has dealt with the domains of health insurance , their connections and defining effective variables better and more extensively than the other studies in the field.