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• An approach to prioritizing the customers is proposed. • The approach includes a two–stage fuzzy inference system. • Four criteria and arrival time is used for prioritization. • The results show fair prioritization with lower waiting times. This paper presents a fuzzy expert system for prioritization of heterogeneous customers with different needs. In this way, it is possible to improve the average waiting cost of a queuing system and enhance customer satisfaction as the basic component of organizations. Each customer has his own specific characteristics and demands and expects the system to understand his value and provide the best service. In this study, a two-stage Mamdani fuzzy inference system (FIS) was applied as a decision-making approach to prioritize customers based on Service Duration, Service Value, Customer Loyalty, Maximum Toleration, and Waiting Time. For this purpose, an expert system was developed that can be applied in various fields perfectly. To evaluate and analyze the performance of Fuzzy Prioritization System (FPS), the proposed model is compared with the First-In-First-Out (FIFO) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods as common approaches for prioritizing customers. The results of numerical experiments revealed the efficiency of the proposed system compared to these two methods. [ABSTRACT FROM AUTHOR]

This paper introduces an Expert System acting as an intelligent agent to interrogate past aircraft occurrence data to support the aircraft occurrence investigators. This Expert System can potentially identify similar occurrences that provide indicators of contributing factors that may otherwise have been missed. This intelligence comes from the Expert System's ability to learn from past occurrence data using the combined Fuzzy Logic System (FLS) inference technique and AcciMaps to emulate the aircraft occurrence investigators' evidence analytical reason model. The learned expert knowledge is presented graphically and interactively to the user (investigator) using the AcciMaps. The paper includes an overview of the Australian Transport Safety Bureau (ATSB) investigation evidence analysis workflow, popular mainstream inference techniques used in Expert Systems, evaluation of the proposed Fuzzy Logic System (FLS) inference technique and AcciMap, including an example study. The FLS inference technique has met the Expert System requirements to support aviation occurrence investigations. [ABSTRACT FROM AUTHOR]

• Bibliometric study on fuzzy systems research. • A combined methodology designed to maximize accurate results. • Novel visualizations of results, based on big data processing software. • Expert systems with Applications as a top fuzzy systems research jorunal. Fuzzy systems research (FSR) is a continuously growing academic field which developments and applications extend to a wide-ranging variety of scientific areas. Academic journals play a decisive role in the scientific communication process and the dissemination of novel ideas. The current developments in information technologies bring both challenges and opportunities for scientific journals to analyze, debate, and share scientific findings. Applying bibliometric and network visualization techniques, this paper seeks to analyze the evolution of the FSR field based on journals from 1965 to 2019. The aim is to visualize the current landscape of key highly oriented FSR journals, focusing on productivity, influence, and synergy. The paper uses data retrieved from the Web of Science scientific database and uses a combined methodology specifically designed to maximize the retrieval of significant documents. Results show that the journals with the highest productivity analyzed by period are Mathematical Analysis and Applications (1965–1979), Fuzzy Sets and Systems [FSS](1980–2009), and the Journal of Intelligent Fuzzy Systems (2010–2019). The most cited journals of the last 20 years are FSS, IEEE Transactions on Fuzzy Systems, and IEEE Transactions on Systems Man Cybernetics Systems, while the most relevant for impact factor is Information Fusion. Moreover, a bibliographic coupling analysis by decades is proposed, showing not only the natural impact and relation that the interaction between journals generate but the impact that the rapid development in communications has in the diversification and dissemination of influence, citations, and productivity of scientific journals. [ABSTRACT FROM AUTHOR]

This paper presents the properties, identification issues and utilisation of a new concept of probabilistic fuzzy system for the innovative project risk assessment. This system constitutes the extension of Mamdani probabilistic fuzzy system. For this purpose, a group of risk factors, which influence risk variables, has been chosen. Linguistic risk variables are inputs to the innovation risk assessment system. The structure of fuzzy sets for linguistic values takes into account knowledge of a number of experts. Knowledge is presented as fuzzy IF–THEN rules together with probability measures of fuzzy events occurrence in the antecedent and conclusion of rules. The paper presents a new method of identification of the analysed system. The method uses parametric family of triangular t-norms, which facilitates inference parameters optimisation, enables flexible adjustment of a system to empirical data and makes the system more precise. The modified FP-Growth algorithm to create probabilistic fuzzy rule base is used. Using assumption of the minimal support of rules enables decreasing of knowledge base complexity while preserving the level of identification quality, comparable to the system with full marginal and conditional probability distributions. The results of the system inference have been compared with regression model and Mamdani fuzzy inference system. Finally, the numerical experiments show more precision of system inference than the compared method. The example of analytical use of created probabilistic fuzzy knowledge base in the context of technical innovation risk assessment is also presented. The constructed expert system has an identification character and it can be develop as a tool to help the assessment of applications for funding the implementation of innovative projects by the institutions established for this purpose. [ABSTRACT FROM AUTHOR]

Abstract: It is well-known that one important issue emerging strongly in agriculture is related with the automation of tasks, where camera-based sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they comes from barley and maize fields including weeds, so that some type of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. The images come from outdoor environments, which are affected for a high variability of illumination conditions because of sunny or cloudy days or both with high rate of changes. Several indices have been proposed in the literature for greenness identification, but under adverse environmental conditions most of them fail or do not work properly. This is true even for camera devices with auto-image white balance. This paper proposes a new automatic and robust Expert System for greenness identification. It consists of two main modules: (1) decision making, based on image histogram analysis and (2) greenness identification, where two different strategies are proposed, the first based on classical greenness identification methods and the second inspired on the Fuzzy Clustering approach. The Expert System design as a whole makes a contribution, but the Fuzzy Clustering strategy makes the main finding of this paper. The system is tested for different images captured with several camera devices. [Copyright &y& Elsevier]

Abstract: In the present paper, a novel fuzzy Multiple Attribute Decision Making (MADM) model is proposed for modeling and solving truck selection problem of a land transportation company. As truck selection is a very crucial problem for the land transportation companies, a systematic and scientific approach is necessary for its solution. A systematic methodology is proposed in the present study by integrating “fuzzy DEMATEL” and “fuzzy hierarchical TOPSIS”. The proposed method makes use of fuzzy DEMATEL method for evaluating the weights of the criteria and hierarchical fuzzy TOPSIS method for assessing the alternatives according to criteria. The steps of the method are described first then a case study is presented in the paper. [Copyright &y& Elsevier]

Abstract: Computer programming is a high-level thinking activity. In the educational area, using learning styles to understand how students learn is a significant issue. The electronic Portfolio (ePortfolio) is a popular educational management and assessment tool. Unfortunately, few researchers investigate programming learning style diagnosis. This paper addresses this gap in research: this study constructs an ePortfolio-based programming learning style diagnosis to detect students’ styles. The fusion of multiple fuzzy-based diagnosis knowledge is the main contribution of this work. This paper built a heuristic optimization method to integrate multiple diagnosis knowledge bases. Performance evaluations and empirical studies were implemented to verify the proposed algorithm and fusion solution. Experimental results showed that the proposed heuristic optimization firms the validity and stability of a diagnostic system, and the ePortfolio-based programming learning style diagnosis is highly accepted by students. Furthermore, teachers agreed that the knowledge fusion mechanism and diagnosis system were usable. [Copyright &y& Elsevier]

Abstract: This paper is a contribution to theoretical background of data mining, more precisely to fuzzy association analysis. We consider three the most commonly used confirmation measures and we study relations among found and known associations given by them. Good understanding of such relationships is necessary for creating more efficient algorithms or for subsequent work with found associations as well as for cooperation with the consumer of the data mining process. Even if our motivation to this work arose from mining of linguistic associations, found properties that coincide with semantics of mined associations are valid in general. Additionally, some examples showing how to use obtained properties are also contained in this paper. [Copyright &y& Elsevier]

Abstract: In a recent paper in this journal by Liu and Chuang [Liu, S. -T., & Chuang, M. (2009). Fuzzy efficiency measures in fuzzy DEA/AR with application to university libraries. Expert Systems with Applications, 36(2), 1105–1113]. Proposition 1 is proved for finding lower and upper bounds of the α-cut of . However, we find that the above proof omit the verification of constraints of assurance regions and the models (11) and (12) omit the constraints of DMUr, which makes it incredible. In this paper, we correct the models and proof of Proposition 1 in . We also propose a generalized fuzzy data envelopment model with assurance regions, whose lower and upper bounds at given levels could be obtained similarly. [Copyright &y& Elsevier]

Abstract: This paper emphasizes on supporting international entry decisions for construction firms in a three-phase analysis process. Phase I identified significant factors as two categories, namely country and project factor levels. Phase II stuck on identifying a risk of a country market for construction firms with fuzzy preference relations (FPR) and country factors. Of which, FPR helped elicit factor relative weights. Phase III devoted to figuring out which project or projects had the best prospects of success and profitability for assisting decisions of decision makers. Within phase III, project factors were used for project prospects, FPR for project success probability, and cumulative prospect theory (CPT) for decision maker prospects. This paper integrates FPR and CPT to select country markets and identify project prospects for decision makers of construction firms. [Copyright &y& Elsevier]

Abstract: Neuro-fuzzy classifiers are characterized by incorporation of the expert knowledge into their construction. The most popular neuro-fuzzy systems are Mamdani-type systems. The main groups of neuro-fuzzy systems are also Takagi–Sugeno and logical-type systems. The latter were very rarely studied in the literature, however it was shown that logical-type reasoning transpired to be better suited for classification tasks whereas Mamdani-type reasoning for approximation problems (). It is well known that an ensemble of several classifiers improves classification accuracy. Many ensembling methods are meta-learning techniques, thus they can be used to design ensembles of various member classifiers. In the paper an ensemble of logical-type neuro-fuzzy systems, based on S-implications, is proposed. The design of such an ensemble, and fuzzy ensemble in general, is a challenging task and encounters technical difficulties. The major problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. Several experiments illustrate the idea presented in the paper. [Copyright &y& Elsevier]

Abstract: Ranking of fuzzy numbers play an important role in decision making, optimization, forecasting etc. Fuzzy numbers must be ranked before an action is taken by a decision maker. Cheng (Cheng, C. H. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 95, 307–317) pointed out that the proof of the statement “Ranking of generalized fuzzy numbers does not depend upon the height of fuzzy numbers” stated by Liou and Wang (Liou, T. S., & Wang, M. J. (1992). Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems, 50, 247–255) is incorrect. In this paper, by giving an alternative proof it is proved that the above statement is correct. Also with the help of several counter examples it is proved that ranking method proposed by Chen and Chen (Chen, S. M., & Chen, J. H. (2009). Fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. Expert Systems with Applications, 36, 6833–6842) is incorrect. The main aim of this paper is to modify the Liou and Wang approach for the ranking of L–R type generalized fuzzy numbers. The main advantage of the proposed approach is that the proposed approach provide the correct ordering of generalized and normal fuzzy numbers and also the proposed approach is very simple and easy to apply in the real life problems. It is shown that proposed ranking function satisfy all the reasonable properties of fuzzy quantities proposed by Wang and Kerre (Wang, X., & Kerre, E. E. (2001). Reasonable properties for the ordering of fuzzy quantities (I). Fuzzy Sets and Systems, 118, 375–385). [Copyright &y& Elsevier]

Abstract: Fuzzy c-means clustering with spatial constraints is considered as suitable algorithm for data clustering or data analyzing. But FCM has still lacks enough robustness to employ with noise data, because of its Euclidean distance measure objective function for finding the relationship between the objects. It can only be effective in clustering ‘spherical’ clusters, and it may not give reasonable clustering results for “non-compactly filled” spherical data such as “annular-shaped” data. This paper realized the drawbacks of the general fuzzy c-mean algorithm and it tries to introduce an extended Gaussian version of fuzzy C-means by replacing the Euclidean distance in the original object function of FCM. Firstly, this paper proposes initial kernel version of fuzzy c-means to aim at simplifying its computation and then extended it to extended Gaussian kernel version of fuzzy c-means. It derives an effective method to construct the membership matrix for objects, and it derives a robust method for updating centers from extended Gaussian version of fuzzy C-means. Furthermore, this paper proposes a new prototypes learning method and it obtains initial cluster centers using new mathematical initialization centers for the new effective objective function of fuzzy c-means, so that this paper tries to minimize the iteration of algorithms to obtain more accurate result. Initial experiment will be done with an artificially generated data to show how effectively the new proposed Gaussian version of fuzzy C-means works in obtaining clusters, and then the proposed methods can be implemented to cluster the Wisconsin breast cancer database into two clusters for the classes benign and malignant. To show the effective performance of proposed fuzzy c-means with new initialization of centers of clusters, this work compares the results with results of recent fuzzy c-means algorithm; in addition, it uses Silhouette method to validate the obtained clusters from breast cancer datasets. [Copyright &y& Elsevier]

Abstract: Multiple criteria decision-making (MCDM) research has developed rapidly and has become a main area of research for dealing with complex decision problems. The purpose of the paper is to explore the performance evaluation model. This paper develops an evaluation model based on the fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution, fuzzy TOPSIS, to help the industrial practitioners for the performance evaluation in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The proposed method enables decision analysts to better understand the complete evaluation process and provide a more accurate, effective, and systematic decision support tool. [ABSTRACT FROM AUTHOR]

Abstract: Understanding the way the ecological and social components interact in a settlement (a village, town, city) is the key to manage effectively the coupled human–natural system and start programs for its sustainability. This paper presents the utility of a fuzzy inference system to codify the interactions among the sustainability components for effective decision making. The method uses local knowledge elicited through a participatory methodology to understand the linkages among the ecological, social, physical and financial components of sustainability in a village. Considering the complexity involved in the interactions among these components, efforts are underway to devise theories, methodologies and approaches to further our understanding. This paper proposes a fuzzy rule based approach to model the interactions of sustainability components in an agro-ecosystem. The approach is explained with the example of a typical agrarian village in southern India. The proposed approach relies on cognitive mapping to elicit understanding of interactions based on local ecological knowledge. The interactions captured in the form of cognitive maps are used to derive the rules, which govern the sustainability states of the components. Fuzzy inference system is used to simulate various scenarios and to study their impact on the sustainability of livelihoods. Cognitive mapping and FIS promise to be an effective combination in building participatory rule based sustainability expert systems and merits further attention. [Copyright &y& Elsevier]

Abstract: Material Handling (MH) is one of the key issues for every production site and has a great impact on manufacturing costs. The core concern in the design of a MH system is selecting the most suitable equipment for every MH operation and optimising them totally in order to attain an optimum solution. This paper presents a hybrid method for the selection and assignment of the most appropriate Material Handling Equipment (MHE). In the first phase, the system selects the most appropriate MHE types for every MH operation in a given application using a Fuzzy Knowledge-Based Expert System consisting of two sets of rules: Crisp Rules and Fuzzy Rules. In the second phase, a Genetic Algorithm (GA) searches throughout the feasible solution space, constituting of all possible combinations of the feasible equipment specified in the previous phase, in order to discover optimum solutions. The validity of the methodology developed in this paper is proved through the use of a real problem. Finally a comparison of the method with the other available publicised methods reveals the effectiveness of this hybrid approach. [Copyright &y& Elsevier]

Abstract: In this paper, an expert speech recognition system is presented. This paper especially deals with the combination of feature extraction and classification for real speech signals. A Wavelet packet adaptive network based fuzzy inference system (WPANFIS) model is developed in this study. WPANFIS consists of two layers: wavelet packet and adaptive network based fuzzy inference system. The wavelet packet layer is used for adaptive feature extraction in the time–frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The performance of the developed system is evaluated by using noisy speech signals. Test results showing the effectiveness of the proposed speech recognition system are presented in the paper. The rate of correct classification is about 92% for the sample speech signals. [Copyright &y& Elsevier]

It is well known the fact that the design of a fuzzy control system is based on the human expert experience and control engineer knowledge regarding the controlled plant behavior. As a direct consequence, a fuzzy control system can be considered as belonging to the class of intelligent expert systems. The tuning procedure of a fuzzy controller represents a quite difficult and meticulous task, being based on prior data regarding good knowledge of the controlled plant. The complexity of the tuning procedure increases with the number of the fuzzy linguistic variables and, consequently, of the fuzzy inference rules and thus, the tuning process becomes more difficult. The paper presents a new design strategy for such expert fuzzy system, which improves their performance without increasing the number of fuzzy linguistic variables. The novelty consists in extending the classic structure of the fuzzy inference core with an intelligent module, which tunes one of the control singletons, providing a significant simplification of the design and implementation procedure. The proposed strategy implements a logical, not physical, supplementation of the linguistic terms associated to the controller output. Therefore, a fuzzy rules set with a reduced number of linguistic terms is used to implement the expert control system. This logical supplementation is based on an intelligent algorithm which performs a shifting of only one of the control singletons (the singleton associated to the SMALL_ linguistic variable), its value becoming variable, a fact that allows an accurate control and a better performance for the expert control system. The logic of this intelligent algorithm is to initially provide a high controller output, followed by a slowdown of the control signal near to the operating set point. The main advantage of the proposed expert control strategy is its simplicity: a reduced number of linguistic terms, combined with an intelligent tuning of a single parameter, can provide results as accurate as other more complex available solutions involving tuning of several parameters (well described by the technical literature). Also, a simplification of the preliminary off-line tuning procedure is performed by using a reduced set of fuzzy rules. The generality of the proposed expert control strategy allows its use for any other controlled process. [ABSTRACT FROM AUTHOR]

Decision on alternative-fuel vehicles is one of the most important problems for fleet operations. In this paper we propose a hierarchical hesitant fuzzy linguistic model that captures hesitant linguistic evaluations of multiple experts on multiple criteria for alternative-fuel vehicles. We apply the proposed model on the alternative-fuel vehicle selection problem of a home health care service provider in the USA. The results show that an electric vehicle is the best fit for the application in today’s conditions. We also show robustness of the decision through a sensitivity analysis as well as analyze three scenarios representing possible changes in conditions. [ABSTRACT FROM AUTHOR]

WiMAX is a futuristic technology which provides simultaneous support for web, video, and voice applications. WiMAX networks are best suitable to real time traffic however the quantity of non real time and best effort traffic cannot be neglected. Distribution of resources in such heterogeneous applications is therefore a challenging task. There are many schedulers available for WiMAX but adaptive and adequate schedulers are still in growing stage of development. This paper introduces a novel method using which a system is developed based on concepts of fuzzy logic to schedule traffic in WiMAX networks. The proposed fuzzy expert system simplifies fair allocation of resources to real as well as non real time traffic. The implementation is based on changing the weights of the queues serving real and non- real time traffic adaptively. New weights will be calculated for each bandwidth request made to base station and these weights will in turn decide amount of bandwidth allocated to different traffic classes. The weights are calculated based on three parameters that are amount of real time and non real time traffic in queues, change in throughput requirement for non real time flows and latency requirement of real time input data. Results obtained by virtue of simulations justify the significance of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this paper is to introduce a decision support system that ranks patents based on multiple expert evaluations. The presented approach starts with the creation of three value scenarios for each considered patent by each expert. These are used for the construction of individual fuzzy pay-off distribution functions for the patent value; a consensual fuzzy pay-off distribution is then determined starting from the individual distributions. Possibilistic moments are calculated from the consensus pay-off distribution for each patent and used in ranking them with TOPSIS. It is further showed how the analytic hierarchy process (AHP) can be used to include additional decision variables into the patent selection, thus allowing for a two-tier decision making process. The system is illustrated with a numerical example and the usability of the system and the combination of methods it includes for patent portfolio selection in the real world context is discussed. [Copyright &y& Elsevier]

Abstract: Currently, an important demand for security exists in many fields. In the area of traffic safety, children are vulnerable elements because they may create a dangerous situation easily. Therefore, in this paper, we propose an intelligent surveillance system to detect, in real time, the danger due to the existence of unprotected children in traffic zones. We analyze the behavior of objects from video content analysis. The developed system is based on fuzzy rules for describing semantically the studied risk. The fuzzy logic provides a gradual danger detection. Besides, the model can be adjusted through membership functions to fuzzy concepts. The system is characterized by being highly scalable and flexible. Moreover, it is portable to any environment. We also highlight in our proposal the high-level tracking module developed, which is based on the classification of objects and 3D positioning. This tracking method is robust to errors that 2D tracking can present. The results obtained in the experimental stage show a high system performance. [Copyright &y& Elsevier]

Abstract: The present paper aims to demonstrate the interest of fuzzy inference systems in system modeling when human interaction is important. It discusses the originality of FIS and their capability to integrate expertise and rule learning from data into a single framework, analyzing their place relatively to concurrent approaches. An open source software implementation is presented, with a focus on the useful features for modeling. Two real world case studies are presented to illustrate the approach and the software utility. [Copyright &y& Elsevier]

Abstract: The aim of the present paper is to optimize and extend the recent method in . We simplify the calculus taking into account the expected value of a fuzzy number can be expressed by using its α-cut sets. A collateral benefit is that the discussed method becomes applicable in any structure of data as triangular fuzzy numbers. Then, to cover a wider range of data – performances of alternatives versus criteria and importance weights of criteria – we extend the optimized method to trapezoidal fuzzy numbers and to the cases of importance weights of criteria/ratings of alternatives expressed by real numbers and ratings of alternatives/importance weights of criteria expressed by fuzzy numbers. Each theoretical development is illustrated by examples. The importance of the method to the selection of the best touristic destinations is discussed too. [Copyright &y& Elsevier]

Abstract: The study aims to extend the Uncapacitated Fuzzy Single Item Lot Sizing Problem (known as F-USILSP) model and extend it for inventory planning. The F-USILSP model is a good choice when there is no statistical data collection, but where there is verbal or qualitative information from experts with experience. Previously, the mixed integer linear programming (MILP) relied on the crisp assumption which hinders the use of the F-USILSP. In this paper, a Possibility Approach is adapted to convert the F-USILSP to a mathematically solvable equivalent crisp USILSP (EC-USILSP). The EC-USILSP model is tested with a case. The organization under study is a petrochemical company power plant with trapezoidal fuzzy demand and triangular fuzzy unit price. The overall results show that the EC-USILSP is more practical and exhibits more flexibility when there is a need to add more realistic situations. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to develop a regime switching design of the fuzzy analytic hierarchy process (FAHP) and to improve its functionality under the choice-varying priority (CVP) problem. In the conventional AHP decision process, priority matrices are identical and their values are invariant for a specific objective. However, in many Multi-Criteria Decision Making (MCDM) problems, the relative importance of criteria may differ according to the choices. A regime switching process is proposed for improving the CVP problem. Under the fuzzy-AHP (FAHP) framework, choice-varying priorities are presented in a cubic matrix form. Another novel contribution is suggested in the prioritization of the level of expert consistency. During the decision-making practice, experts may have different attitudes and their individual matrix consistencies might be superior or inferior in their overall practices. Individual consistency is one of the objective indicators of the quality of judgment. An expert consistency prioritization approach is proposed to deal with the classification of response stability. For the financial risk assessment part of the study, the loss probability of the intended projects is calculated by the fuzzy Monte-Carlo simulation framework. [Copyright &y& Elsevier]

Abstract: This paper is a statistical analysis of hybrid expert system approaches and their applications but more specifically connectionist and neuro-fuzzy system oriented articles are considered. The current survey of hybrid expert systems is based on the classification of articles from 1988 to 2010. Present analysis includes 91 articles from related academic journals, conference proceedings and literature reviews. Our results show an increase in the number of recent publications which is an indication of gaining popularity on the part of hybrid expert systems. This increase in the articles is mainly in neuro-fuzzy and rough neural expert systems’ areas. We also observe that many new industrial applications are developed using hybrid expert systems recently. [Copyright &y& Elsevier]

Abstract: Members of university commissions decide on an especial sort of students’ requests, based on various criteria. Since these decisions may seriously affect the students’ future life, it is necessary to pay precise attention to the decision-making process. However, many causes like lack of mental readiness, impact of irrelevant factors, change in views and limitation in concentration power of the commission members plus some other undistinguishable facts can affect the decision process leading to many ruinous problems. The paper aims simulation of a decision-making process which is used by management or consulting teams in order to solve this usual problem. As an especial case study, it focuses on the decisions made by members of university commissions on an especial sort of students’ requests, who have faced problems with their education. After analyzing the decision process, we have designed a hybrid expert system that can regenerate the process. In this system, the criteria shape a fuzzy rule base with linguistic variables and the membership functions are adjusted by experts through interviews and/or questionnaire. Indeed, it is a composition of a fuzzy MCDM and a fuzzy inference system (FIS). Moreover, to differentiate between the criteria, we have attributed weightings to the criteria, which are computed using a fuzzy AHP method. Results show that the system output error in comparison to the historical data is less than 5%, and it can resemble the council’s behavior. This supporting system can be applied to generate consistent outputs that contribute in convergence and unification of the decisions, and hence preventing false aftermaths. [Copyright &y& Elsevier]

Abstract: Supplier selection is usually involved a multi-criteria evaluation to assist decision-making. This study proposed a simple method for vendor selection. The proposed procedure utilizes the structure of criteria in analytic hierarchy process (AHP) model and employs consistent fuzzy preference relations (CFPR) to construct the decision matrices. The advantage of CFPR is computational simplicity and efficiency. The computation can be done by using an Excel spreadsheet or writing a simple computer program. Also, the CFPR method guarantees the consistency of decision matrices. In this paper, a step-by-step empirical example demonstrates the usage of this proposed method. [Copyright &y& Elsevier]

Abstract: It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan. [Copyright &y& Elsevier]

Abstract: Distribution channel management not only consists of choosing distribution channels. In fact, probably the most difficult phase of the distribution management starts after this choice. Determining an appropriate organization strategy for distribution channel management is like a problem of concern to marketing practitioners and academics as well in this phase. In this study, the organization strategy of distribution channel management is developed using fuzzy analytic hierarchy process (FAHP) and hierarchical fuzzy TOPSIS (HFTOPSIS) for an edible-vegetable oils manufacturer firm operating in Turkey. The company distributes its products all over the country. Due to the complex structure of the distribution network, the company wants to decide the organization strategy to manage the distribution channels. In this paper, the methods of FAHP and HFTOPSIS for evaluating and selecting among the five organization strategy models for distribution channel management of vegetable oil manufacturer have been presented. The proposed models include determinants of distribution channel management for edible-vegetable oil industry; (i) customer profile, (ii) distributor reliability, (iii) the position of competitors in market, and (iv) managerial and financial perspective. Using FAHP and HFTOPSIS, hybrid based strategy (KBS), which has the greatest desirability index value after the evaluation among the five alternatives is found as the best choice. Thus, the case of the vegetable oil manufacturer company provides the researchers and practitioners to understand in a better way the importance of developing organization strategy in channel management from a practical point of view. [Copyright &y& Elsevier]

Abstract: The proper generation of fuzzy membership function is of fundamental importance in fuzzy applications. The effectiveness of the membership functions in pattern classifications can be objectively measured in terms of interpretability and classification accuracy in the conformity of the decision boundaries to the inherent probabilistic decision boundaries of the training data. This paper presents the Supervised Pseudo Self-Evolving Cerebellar (SPSEC) algorithm that is bio-inspired from the two-stage development process of the human nervous system whereby the basic architecture are first laid out without any activity-dependent processes and then refined in activity-dependent ways. SPSEC first constructs a cerebellar-like structure in which neurons with high trophic factors evolves to form membership functions that relate intimately to the probability distributions of the data and concomitantly reconcile with defined semantic properties of linguistic variables. The experimental result of using SPSEC to generate fuzzy membership functions is reported and compared with a selection of algorithms using a publicly available UCI Sonar dataset to illustrate its effectiveness. [Copyright &y& Elsevier]

Abstract: In this paper, some multiple attribute group decision making (MAGDM) problems in which both the attribute weights and the expert weights are usually correlative, attribute values take the form of intuitionistic fuzzy values or interval-valued intuitionistic fuzzy values, are investigated. Firstly, some operational law, score function and accuracy function of intuitionistic fuzzy values or interval-valued intuitionistic fuzzy values are introduced. Then two new aggregation operators: induced intuitionistic fuzzy correlated averaging (I-IFCA) operator and induced intuitionistic fuzzy correlated geometric (I-IFCG) operator are developed and some desirable properties of the I-IFCA and I-IFCG operators are studied, such as commutativity, idempotency and monotonicity. An I-IFCA and IFCA (intuitionistic fuzzy correlated averaging) operators-based approach is developed to solve the MAGDM problems in which both the attribute weights and the expert weights usually correlative, attribute values take the form of intuitionistic fuzzy values. Then, we extend the developed models and procedures to the interval-valued intuitionistic fuzzy environment. Finally, some illustrative examples are given to verify the developed approach and to demonstrate its practicality and effectiveness. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to develop a generic version of the conventional fuzzy-analytic hierarchy process (FAHP) method and investigate the shipping asset management (SAM) problem in the dry bulk shipping market. The recent literature has various applications of the FAHP, but these studies lack consistency control, use identical decision support rather than weighted expert choices, and lack measurable criteria. The proposed model, generic fuzzy-AHP (here after GF-AHP), provides a standard control of consistency on the decision matrix for the expert group. GF-AHP also improves the capabilities of the FAHP by executing direct numerical inputs without expert consultation. In practical business, some of the criteria can be easily calculated and expert consultation is a redundant process. Therefore, GF-AHP presents how to transform such numerical inputs to a priority scale. Finally, expertise differences on the decision group are reflected in the GF-AHP process by an expert weighting algorithm. [Copyright &y& Elsevier]

Abstract: Despite its usefulness, design knowledge is not often captured or documented, and is therefore lost or damaged after a product design is completed. As a way to address this issue, two major formalisms can be used for modeling, representing, and reasoning about causal design knowledge: fuzzy cognitive map (FCM) and Bayesian belief network (BBN). Although FCM has been used extensively in knowledge engineering, few methodologies exist for systematically constructing it. In this paper, we present a methodology and application—FCM Constructor—to systematically acquire design knowledge from domain experts, and to construct a corresponding BBN. To show the system’s usability, we use three realistic product design cases to compare BBNs that are directly generated by domain experts, with BBNs that are generated using the FCM Constructor. We find that the BBN constructed through the FCM Constructor is similar, based on reasoning results, to the BBN constructed directly by specifying conditional probability tables of BBNs. [Copyright &y& Elsevier]

Abstract: This paper investigates the dynamic intuitionistic fuzzy multi-attribute group decision making (DIF-MAGDM) problems, in which all the attribute values provided by multiple decision makers (DMs) at different periods take the form of intuitionistic fuzzy numbers (IFNs), and develops an interactive method to solve the DIF-MAGDM problems. The developed method first aggregates the individual intuitionistic fuzzy decision matrices at different periods into an individual collective intuitionistic fuzzy decision matrix for each decision maker by using the dynamic intuitionistic fuzzy weighted averaging (DIFWA) operator, and then employs intuitionistic fuzzy TOPSIS method to calculate the individual relative closeness coefficient of each alternative for each decision maker and obtain the individual ranking of alternatives. After doing so, the method utilizes the hybrid weighted averaging (HWA) operator to aggregate all the individual relative closeness coefficients into the collective relative closeness coefficient of each alternative and obtain the aggregate ranking of alternatives, by which the optimal alternative can be selected. In addition, the spearman correlation coefficient for both the aggregate ranking and individual ranking of alternatives is calculated to measure the consensus level of the group preferences. Finally, a numerical example is used to illustrate the developed method. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel contribution in Web caching area, especially in Web cache replacement, so-called intelligent client-side Web caching scheme (ICWCS). This approach is developed by splitting the client-side cache into two caches: short-term cache that receives the Web objects from the Internet directly, and long-term cache that receives the Web objects from the short-term cache. The objects in short-term cache are removed by least recently used (LRU) algorithm as short-term cache is full. More significantly, when the long-term cache saturates, the neuro-fuzzy system is employed efficiently in managing contents of the long-term cache. The proposed solution is validated by implementing trace-driven simulation and the results are compared with least recently used (LRU) and least frequently used (LFU) algorithms; the most common policies of evaluating Web caching performance. The simulation results have revealed that the proposed approach improves the performance of Web caching in terms of hit ratio (HR), up to 14.8% and 17.9% over LRU and LFU. In terms of byte hit ratio (BHR), the Web caching performance is improved up to 2.57% and 26.25%, and for latency saving ratio (LSR), the performance is better with 8.3% and 18.9% over LRU and LFU, respectively. [Copyright &y& Elsevier]

Abstract: Process performance can be analyzed by using process capability indices (PCIs), which are summary statistics to depict the process location and dispersion successfully. Traditional PCIs are generally used for a process which has a symmetric tolerance when the target value (T) locates on the midpoint of the specification interval (m). When this is not the case (T ≠ m), there are serious disadvantages in the casual use and interpretation of traditional PCIs. To overcome these problems, PCIs with asymmetric tolerances have been developed and applied successfully. Although PCIs are very usable statistics, they have some limitations which prevent a deep and flexible analysis because of the crisp definitions for specification limits (SLs), mean, and variance. In this paper, the fuzzy set theory is used to add more information and flexibility to PCIs with asymmetric tolerances. For this aim, fuzzy process mean, and fuzzy variance, , which are obtained by using the fuzzy extension principle, are used together with fuzzy specification limits (SLs) and target value (T) to produce fuzzy PCIs with asymmetric tolerances. The fuzzy formulations of the indices , which are the most used PCIs with asymmetric tolerances, are developed. Then a real case application from an automotive company is given. The results show that fuzzy estimations of PCIs with asymmetric tolerances include more information and flexibility to evaluate the process performance when it is compared with the crisp case. [Copyright &y& Elsevier]

Abstract: Classification systems have been widely utilized in medical domain to explore patient’s data and extract a predictive model. This model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. The aim of this paper is to use an Ant Colony-based classification system to extract a set of fuzzy rules for diagnosis of diabetes disease, named FCS-ANTMINER. We will review some recent methods and describe a new and efficient approach that leads us to considerable results for diabetes disease classification problem. FCS-ANTMINER has new characteristics that make it different from the existing methods that have utilized the Ant Colony Optimization (ACO) for classification tasks. The obtained classification accuracy is 84.24% which reveals that FCS-ANTMINER outperforms several famous and recent methods in classification accuracy for diabetes disease diagnosis. [Copyright &y& Elsevier]

Abstract: The growing emphasis on complexity concerns for ontologies has attracted significant interest from both the researcher’s and the practitioner’s communities in modularization techniques as a way to decrease the complexity of managing huge ontologies. On the other hand, it has been widely pointed out that classical ontologies are not appropriate to deal with imprecise and vague knowledge, which is inherent to several real world domains. In order to handle these types of knowledge, some fuzzy extensions of classical ontologies are presented, yielding fuzzy ontologies. In this paper, we integrate modular ontologies with fuzzy ontologies, i.e., the notion of modular fuzzy ontologies is presented. Furthermore, we present an infrastructure for the representation of and reasoning with modular fuzzy ontologies based on distributed fuzzy description logics. [Copyright &y& Elsevier]

Abstract: A new adaptive fuzzy approach to function approximation is proposed in the paper. A Takagi–Sugeno (T–S) type fuzzy system is used as the function approximator in the study. The proposed approach uses a hybrid learning method to train the T–S fuzzy system to achieve high accuracy in function approximation. The hybrid learning method combines both the particle swarm optimization (PSO) and the recursive least squares estimator (RLSE) to update the parameters of the fuzzy approximator. The PSO is used to update the premise part of the fuzzy system while the consequent part is updated by the RLSE. The PSO–RLSE learning method is very efficient in learning convergence. The proposed approach is compared to other methods. Three benchmark functions are used for the performance comparison. The proposed approach shows superior performance to compared approaches, in terms of approximation accuracy and learning convergence. [Copyright &y& Elsevier]

Abstract: This paper proposed a sustainability balanced scorecard (SBSC) framework to measure the sustainable performance of the semiconductor industry. Based on the principle of four perspectives of balanced scorecard (BSC), of which two were changed from the financial and customer perspectives to sustainability and stakeholder perspectives to deal the insufficiently addressed issue of corporate social responsibility. Twenty-five measures of sustainable performance were recognized through expert questionnaires and through the fuzzy Delphi method (FDM). Further, the relative weights of the selected measures were determined using the analytic network process (ANP). Results indicated that the five most important measures of sustainable performance were profitability (0.2528), green innovation and investment (0.1563), green image (0.1546), customer satisfaction (0.0799), and sustainability awareness of the top management (0.0516). Among these measures, the top three all belonged to the sustainability perspective, which comprised 56% of the overall weighting. The proposed SBSC framework can help Taiwanese semiconductor companies enhance their competitiveness in terms of sustainable operations is anticipated. [Copyright &y& Elsevier]

Abstract: The hotel industry is one of the leading stakeholders in the tourism sector. In order to reduce the traveler’s cost of seeking accommodations, enforce the return ratio efficiency of guest rooms and enhance total operating performance, evaluating and selecting a suitable hotel location has become one of the most critical issues for the hotel industry. In this scenario, recommender services are increasingly emerging which employ intelligent agents and artificial intelligence to “cut through” unlimited information and obtain personalized solutions. Taking this assumption into account, this paper presents Sem-Fit, a semantic hotel recommendation expert system, based on the consumer’s experience about recommendation provided by the system. Sem-Fit uses the consumer’s experience point of view in order to apply fuzzy logic techniques to relating customer and hotel characteristics, represented by means of domain ontologies and affect grids. After receiving a recommendation, the customer provides a valuation about the recommendation generated by the system. Based on these valuations, the rules of the system are updated in order to adjust the new recommendations to past user experiences. To test the validity of Sem-Fit, the validation accomplished includes the interaction of the customer with the system and then the results are compared with the expert recommendation for each customer profile. Moreover, the values of precision and recall and F1 have been calculated, based on three points of view, to measure the degree of relevance of the recommendations of the fuzzy system, showing that the system recommendations are on the same level as an expert in the domain. [Copyright &y& Elsevier]

Abstract: Electro-hydraulic actuators (EHAs) have a wide range of applications where force or position control with high accuracy is exceedingly necessary. Among them, press machines applied hybrid EHAs are more and more used in the heavy industry. This paper presents an online smart tuning fuzzy PID (OSTFPID) approach based on a robust extended Kalman filter (REKF) for the development of high force control precision in the press machines. Here, the main control unit employs the fuzzy PID structure of which membership function (MF) optimization is considered as a system identification problem. A smart selection procedure (SSP) is implemented to pick out only fuzzy input and output MFs activated at each running step, and then the REKF algorithm is used to tune the active MFs automatically during the operation process to minimize the control error. Consequently, the active MFs are trained about their shapes and positions to adapt to the working conditions. As the result, the control performance is significantly improved, while the optimizing time and number of the controller calculations are remarkably reduced. In order to verify the ability of the proposed controller applied to the press machines using EHAs, a test press bench system called electro-hydraulic test machine (EHTM) is also suggested and setup to use in this study. Real-time experiments on the EHTM are carried out to evaluate the control method in a large variation of working environments. Considerable improvement in the performance generated by the designed controller is compared with the traditional PID and fuzzy PID controllers. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new method to forecast TAIEX based on a high-order type 2 fuzzy time series. Extra observations are used to improve forecasting performance. Extra observations are modeled as type 2 fuzzy sets and fourth-order fuzzy time series. Our proposed model outperforms previous studies. [ABSTRACT FROM AUTHOR]

Abstract: In Capability Maturity Model Integrated (CMMI), Lead Appraiser (LA) evaluates the processes of one company according to qualitative sources such as instrument, interview and document through direct artifacts, indirect artifacts and affirmation. Due to the subjective measurement and non-quantitative expression of LAs, this paper proposes a fuzzy quantitative integrated metric model (FQIMM) that combines Quantitative Software Metrics Set (QSMS), linguistic variables, interval of confidence and a new fuzzy number ranking method. It can help software companies to evaluate on quantitative approach and then know the status by their self more quickly and effectively. Moreover, a CMMI appraisal support system (CMMI-ASS) is developed to help self-assessment software companies to accomplish the appraisal process, which integrates a fuzzy number ranking method to rank the final result. [ABSTRACT FROM AUTHOR]

Abstract: Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, etc.) can occur. The two most important issues of FMEA are the acquirement of FMEA team members’ diversity opinions and the determination of risk priorities of the failure modes that have been identified. First, the FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information because of its cross-functional and multidisciplinary nature. These different types of information are very hard to incorporate into the FMEA by the traditional model and fuzzy logic approach. Second, the traditional FMEA determines the risk priorities of failure modes using the risk priority numbers (RPNs) by multiplying the scores of the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode. The method has been criticized to have several shortcomings. In this paper, we present an FMEA using the fuzzy evidential reasoning (FER) approach and grey theory to solve the two problems and improve the effectiveness of the traditional FMEA. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a new method for evaluating students’ learning achievement by automatically generating the weights of the attributes “accuracy rate”, “time rate”, “difficulty”, “complexity”, “answer-cost” and “importance” of fuzzy rules, respectively, with the fuzzy reasoning capability. The proposed method can be more sensitive to reflect students’ learning achievement. It can provide us with more fairer and more reasonable results for students’ learning achievement evaluation than . [ABSTRACT FROM AUTHOR]

Abstract: The main objective of the Ubiquitous computing is to include technology on the user life without modifying their daily routine. A lot of kinds of applications base their operations on using sensors situated on a Tagged World, which is a smart area that serves to recognize user’s behaviour using information about their daily activity. It manages information collected by sensors to identify the user behaviour and to provide some services according to inferred behaviours. In this paper, we present a specific method to extract behaviour patterns on time from the collected sensor signals. The method is based on frequent itemsets that represent the common actions of the user. After, we obtain sequence patterns from these extracted itemsets. Our method should pay attention to the user activities because these have non-random imprecision by definition. Thus, we have designed a method to handle this imprecision, through establishing a temporal constraint that is called Fuzzy Temporal Window. [ABSTRACT FROM AUTHOR]

Abstract: E-alliance is the union of e-commerce and its success and efficiency is related to comprehensive quality of e-commerce. Thus, ranking e-commerce websites in e-alliance is of importance, which is a multi-criteria decision-making (MCDM) problem. This paper proposes an evaluation model based on analytic hierarchy process (AHP), fuzzy sets and technique for order performance by similarity to ideal solution (TOPSIS), to tackle the issue in fuzzy environment. The AHP is applied to analyze the structure of ranking problem and to determine weights of the criteria, fuzzy sets is utilized to present ambiguity and subjectivity with linguistic values parameterized by triangular fuzzy numbers, and TOPSIS method is used to obtain final ranking. Case analysis is conducted to illustrate the utilization of the model for the problem. It demonstrates the effectiveness and feasibility of the proposed model. [ABSTRACT FROM AUTHOR]