Computer-aided maintenance management systems selection based on a fuzzy AHP approach

Computerized maintenance management systems (CMMS) are common in today’s industries. CMMS can bring a large number of benefits, which include increased productivity, reduced costs, and effective utilization of the assets in any manufacturing and service producer. The list of CMMS that are available in the market has grown very rapidly during the last years. When purchasing a system, one that suits the specific needs and objectives of the company’s maintenance operations should be preferred. Several selection methods were proposed in literature. Up to now, no article has considered ambiguity and uncertainty factors when selecting effective CMMS. In addition, CMMS selection decisions involve the simultaneous consideration of multiple criteria, including tangible and intangible factors; prioritizing these factors can be a great challenge and a complex task. Therefore, no attempt has been made to incorporate fuzziness into multicriteria decision-making in the area of CMMS selection. This work proposes a fuzzy-based methodology for comparative evaluation of a number of CMMS alternatives. The proposal is based on the well-known multicriteria decision method called Analytical Hierarchy Process (AHP) with triangular numbers. An example is given to illustrate the proposed methodology. Finally, a software prototype for implementing this method was implemented. To illustrate and validate the proposed approach and the software prototype developed some details are presented and discussed.     

Product classification in the Internet business—a fuzzy approach

In this paper, a methodology has been introduced as a decision support tool to the consumers in the Internet business. This decision support tool takes into account the multiple attributes of the product, analyses them with respect to the consumer's desire, and finally classifies these products into different hierarchical levels as per the consumer's level of preference. The product attributes, which are in general conflicting, imprecise, and non-commensurable in nature, are well handled here by using the concepts of fuzzy logic. Concepts of linguistic quantifier are used to quantify the qualitatively defined items and also to classify the products into different preference levels as required by the customer. Classification of the products into preference levels in any business, particularly, in the business through the Internet, gives a boost to the customer and helps him in a final product choice. The procedure described here can be used by virtual buying agents for generating a hierarchical classification based on buyer's preference. At the end, a numerical example is illustrated to highlight the procedure.     

Autonomous Mapping of E-Business Demands and Supplies via Invisible Internet Agents

In business Internet-based systems demands and supplies can be hidden in different forms and locations. We develop a simple type of agents called knowledgeable objects for carrying and mapping heterogeneous and distributed business data in a self-processing way. Internet agents coordinate these invisible agent objects to implement a competitive mapping via agent bidding. Interval-valued numbers and fuzzy ranking are utilized for representing and clustering dynamic business data. The concept of agent awareness and invisibility is used for regulating agent interaction scope to decrease mapping time and to accord with system capacity. Our analysis shows that this combination of agent-interval-based techniques not only meets the distributed, heterogeneous, and dynamic tendencies of E-business systems but also makes the mapping process more autonomous and efficient.     

Development of a fuzzy decision model for manufacturability evaluation

A manufacturability evaluation decision model is formulated and analyzed based on fuzzy logic and multiple attribute decision-making under the concurrent engineering environment. The study emphasizes on the treatment of the linguistic and vagueness at the early product development stage. The study also considers the function integration of the total life cycle of a product. Hence, the integrated decision model covers the multi-level, multi-goal requirements of the products. Multiple criteria such as the goal space, the decision space, the function space, the development (i.e., product & process design) space, and the activity space, are then applied under different analysis of decision-making methods. For instances, the fuzzy multiple attribute decision-making (FMADM) combined with activity-based costing (ABC) can be used in the activity decision space. The fuzzy logic decision model can be applied in the goal decision space. The results of this study point out the importance of early decision making capability. An example of a high-pressure vessel is provided to demonstrate the proposed model for evaluating the manufacturability.     

Protecting data: a fuzzy approach

Privacy issues represent a longstanding problem nowadays. Measures such as k-anonymity, l-diversity and t-closeness are among the most used ways to protect released data. This work proposes to extend these three measures when the data are protected using fuzzy sets instead of intervals or representative elements. The proposed approach is then tested using Energy Information Authority data set and different fuzzy partition methods. Results shows an improvement in protecting data when data are encoded using fuzzy sets.     

An approach for multiple attribute group decision making problems with interval-valued intuitionistic trapezoidal fuzzy numbers

This article proposes an approach to resolve multiple attribute group decision making (MAGDM) problems with interval-valued intuitionistic trapezoidal fuzzy numbers (IVITFNs). We first introduce the cut set of IVITFNs and investigate the attitudinal score and accuracy expected functions for IVITFNs. Their novelty is that they allow the comparison of IVITFNs by taking into accounting of the experts’ risk attitude. Based on these expected functions, a ranking method for IVITFNs is proposed and a ranking sensitivity analysis method with respect to the risk attitude is developed. To aggregate the information with IVITFNs, we study the desirable properties of the interval-valued intuitionistic trapezoidal fuzzy weighted geometric (IVITFWG) operator, the interval-valued intuitionistic trapezoidal fuzzy ordered weighted geometric (IVITFOWG) operator, and the interval-valued intuitionistic trapezoidal fuzzy hybrid geometric (IVITFHG) operator. It is worth noting that the aggregated value by using these operators is also an interval-valued intuitionistic trapezoidal fuzzy value. Then, based on these expected functions and aggregating operators, an approach is proposed to solve MAGDM problems in which the attribute values take the form of interval-valued intuitionistic fuzzy numbers and the expert weights take the form of real numbers. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.     

Linear interpolation in a fuzzy information space

An approach to solving a linear interpolation problem in a fuzzy information space is proposed. Two different schemes of interpolation are outlined: a heuristic one, based on the geometrical interpretation of operations, and an optimization one, based on the expansion principle. The results obtained allow performing fuzzy linear prediction. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 55–68, March–April 2006.     

A fuzzy analytic hierarchy process approach in modular product design

Product development stages. The analytic hierarchy process (AHP), which breaks down a complex problem into simple hierarchical decision-making processes, can be incorporated with fuzzy logic to suggest the relative strength of the factors in the corresponding criteria, thereby enabling the construction of a fuzzy judgement matrix to facilitate decision-making. This paper proposes a fuzzy AHP approach in modular product design complemented with a case example to validate its feasibility in a real company. Test findings indicate that the approach is helpful for providing key decision support information in terms of product module selection during product development stages. The significance of the contribution of this paper is the suggestion of a novel approach in modular product design, embracing a combination of computational intelligence and traditional techniques, thereby providing more alternatives and ideas for those researchers who are interested in this field of study.     

The selection of technology forecasting method using a multi-criteria interval-valued intuitionistic fuzzy group decision making approach

Technological forecasting is a tool for organizations to develop their technology strategies. The quality of forecasting is extremely important for the accuracy of the results and in turn company future. Therefore a proper selection methodology of forecasting technique that considers the characteristics of technology and resources needed such as cost, time is essential. On the other hand, although many forecasting techniques are available, there is a high uncertainty in choosing the most appropriate technique among a set of available techniques. In this paper interval valued intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method is proposed for the solution of technological forecasting technique selection problem. The proposed method includes seven selection criteria and twelve forecasting technique alternatives. The methodology is applied for 3D TV technology. The results revealed that Fisher Pry method is found as the most appropriate method for forecasting since it has the highest closeness coefficient.     

Mediative fuzzy logic: a new approach for contradictory knowledge management

In this paper we are proposing a novel fuzzy method that can handle imperfect knowledge in a broader way than Intuitionistic (in the sense of Atanassov) fuzzy logic does (IFL). This fuzzy method can manage non- contradictory, doubtful, and contradictory information provided by experts, providing a mediated solution, so we called it Mediative Fuzzy Logic (MFL). We are comparing results of MFL, with IFL and traditional Fuzzy logic (FL).     

Examining the structure of the Internet Addiction Test in adolescents: A bifactor approach

Young’s (1998a) Internet Addiction Test (IAT) is one of the most commonly used measures of problematic Internet use. Although the instrument is being increasingly applied in research with adolescents, its measurement structure has not been systematically evaluated with pre-adult respondents. Because the results of previous factor-analytic studies with adults have been highly inconsistent, the present study applied both the traditional (simple-structure) and novel (bifactor) modeling approaches to derive the most optimal measurement structure of the IAT for adolescents. The results of exploratory and confirmatory analyses in a large Canadian sample of high-school students (N = 1948) converged on a bifactor model with a dominant global IA factor and two distinct sub-dimensions, each associated with a unique gender and problem behaviour profile. The discussion focuses on the implications of this bifactor structure for scale scoring and substantive theory on the nature and sources of individual differences in Internet addiction.     

Interest rate prediction: a neuro-hybrid approach with data preprocessing

The following research implements a differential evolution-based fuzzy-type clustering method with a fuzzy inference neural network after input preprocessing with regression analysis in order to predict future interest rates, particularly 3-month T-bill rates. The empirical results of the proposed model is compared against nonparametric models, such as locally weighted regression and least squares support vector machines, along with two linear benchmark models, the autoregressive model and the random walk model. The root mean square error is reported for comparison.     

Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

Fuzzy c-means （FCM） algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index （CVI） is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3].     

Group decision making in medical system: An intuitionistic fuzzy soft set approach

In medical system, there may be many critical diseases, where experts do not have sufficient knowledge to handle those problems. For these cases, experts may provide their opinion only about certain aspects of the disease and remain silent for those unknown features. Feeling the need of prioritizing different experts based on their given information, this article uses a novel concept for assigning confident weights to different experts which are mainly based on their provided information. Experts provide their opinions about various symptoms using intuitionistic fuzzy soft matrix (IFSM). In this article, we propose an algorithmic approach based on intuitionistic fuzzy soft set (IFSS) which explores a particular disease reflecting the agreement of all experts. This approach is guided by the group decision making (GDM) model and uses cardinals of IFSS as novel concept. We have used choice matrix (CM) as an important parameter which is based on choice parameters of individual expert. This article has also validated the proposed approach using distance measurements and consents of the majority of experts. The effectiveness of the proposed approach is demonstrated using a suitable case study.     

Application of MADM in a fuzzy environment for selecting the best barrier for offshore wells

A fuzzy Multi Attribute Decision Making (FMADM) method, which is suitable for treating group decision making problems in a fuzzy environment, is proposed for ranking offshore well barriers from a cost-benefit view point. It is obvious that much knowledge in the real world is fuzzy rather than precise. MADM decision data is usually fuzzy, crisp, or a combination of the two. A useful model is proposed here in order to handle both fuzzy and crisp data. Imprecision and ambiguity in the calculation of a performance rating are incorporated into MADM whereby fuzzy set theory provides a mathematical framework for modeling them. Human opinions often conflict in group decision-making. The purpose of fuzzy MADM is to aggregate the conflicting opinions. In general, one expert’s opinion for a given attribute may be different from others’. Therefore, it is necessary to develop an appropriate method of aggregating multiple experts’ opinions, taking into account a degree of importance of each expert in the aggregation procedure. The weights of all attributes and experts are estimated using a Fuzzy Analytical Hierarchy Process (FAHP). Finally, the best well barrier or risk control option (RCO) with respect to cost and benefit is selected using a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method.     

Quality management in product recovery using the Internet of Things: An optimization approach

Internet of Things, by reducing or almost eliminating uncertainty regarding existence, types, conditions and remaining lives of components in an end-of-life product (EOLP), can mitigate planning of remanufacturing operations. Remaining useful life can be taken into account as a good measure of quality. Therefore, immediate determination of remaining useful life allows optimal recovery decisions to guarantee a minimum quality level on recovered products while satisfying various system criteria.     

Computers and the Internet as a job assisted tool: based on the three-tier use model approach

Personal attitudes are a major factor to affect individual information technology usage. The purpose of this study is to explore the role of individual attitudes toward computers and the Internet usage for assisting job performance. This research applies the 3-TUM approach to understand individual attitudes toward computers and the Internet usage. After statistical analysis, the results provide a support that the 3-TUM is appropriate model for investigating faculty and staff perceptions toward computers and Internet. In addition, the results also support that using computers and the Internet may assist individual job performance. Furthermore; this study offer evidence that when individuals have more self-efficacy and feel computers and the Internet are more useful, then they have more behavioral intention to use and learn computers and the Internet for assisting their job performance.     

A fuzzy cognitive map model to calculate a user's desirability based on personality in e-learning environments

The recent research in artificial intelligence shows an increasing interest in the modeling of human behavior factors such as personality, mood, and emotion for developing human-friendly systems. That is why there is an interest in developing models and algorithms to determine a human's emotions while interacting with a system to improve the quality of the interaction. In this paper, we propose a computational model to calculate a user's desirability based on personality in e-learning environments. The desirability is one of the most important variables in determining a user's emotions. The model receives several e-learning environmental events and predicts the desirability of the events based on the user's personality and his/her goals. The proposed model has been evaluated in a simulated and real e-learning environment. The results show that the model formulates the relationship between personality and emotions with high accuracy.