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Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

In the era of common digitalization and far reaching progress in the field of cybernetics, it is necessary to use the knowledge and experience in military cybernetics applications. In the field of machines, control fuzzy expert inference systems open new horizons and possibilities. Generally, the main affect of human efforts in the case of artificial intelligence is to create a machine with a set of behaviors and attitudes that would allow it to work independently, with ability to adjust to changing environmental conditions and an advisory role in the decision-making process. It should be noted that this technology used in some cases has already produced successful results. This paper aims to describe how the fuzzy expert inference membership function shapes influence analysis on selected air tasks efficiency evaluation results. Presented results prove that proper fuzzy membership functions shape selection has fundamental influence on aircraft system level of efficiency evaluation (its calculation accuracy). Using this technology in military aviation air tasks efficiency evaluation aspects is pioneer.

In the era of common digitalization and far reaching progress in the field of cybernetics, it is necessary to use the knowledge and experience in the domain of cybernetics in military applications. Artificial intelligence that so much influences on the imagination of scholars actually opens new horizons when it comes to control the machines. Relatively recently, it is introduced for military applications such departments of artificial intelligence as fuzzy logic, expert systems and fuzzy control theory.

In this paper, fuzzy expert inference membership function shapes influence analysis on selected air tasks efficiency evaluation results are described. Presented results prove that proper fuzzy membership functions shape selection has fundamental influence on aircraft system level of efficiency evaluation (its calculation accuracy).

The issue solved in the paper is based on application of theoretical results in practice. The paper can be estimated to bridge the gap between theory and practice in specific field.

Using this technology in military aviation air tasks efficiency evaluation aspects is pioneer.

This study aims to analyze the performance of four different fuzzy inference system-based forecasting tools using a real case company.

The forecasting tools were tested using 27 products of the nail polish line of a multinational beauty company and the performance of said tools was compared to those of the company’s previous forecasting methods that were basically qualitative (informal and intuition-based).

The performance of the methods analyzed was compared by using mean absolute percentage error. It was possible to determine the characteristics and conditions that make each model the best for each situation. The main takeaways were that low kurtosis, negatively skewed demand time-series and longer horizon forecasts that favor the fuzzy inference system-based models. Besides, the results suggest that the fuzzy forecasting tools should be preferred for longer horizon forecasts over informal qualitative methods.

Notwithstanding the proposed hybrid modeling approach based on fuzzy inference systems, our research offers a relevant contribution to theory and practice by shedding light on the segmentation and selection of forecasting models, both in terms of time-series characteristics and forecasting horizon. The proposed fuzzy inference systems showed to be particularly useful not only when time-series distributions present no clear central tendency (that is, they are platykurtic or dispersed around a large plateau around the median, which is the characteristic of negative kurtosis), but also when mode values are greater than median values, which in turn are greater than mean values. This large tail to the left (negative skewness) is typical of successful products whose sales are ramping up in early stages of their life cycle. For these, fuzzy inference systems may help managers screen out forecast bias and, therefore, lower forecast errors. This behavior also occurs when managers deal with forecasts of longer horizons. The results suggest that further research on fuzzy inference systems hybrid approaches for forecasting should emphasize short-term forecasting by trying to better capture the “tribal” managerial knowledge instead of focusing on less dispersed and slower moving products, where the purely qualitative forecasting methods used by managers tend to perform better in terms of their accuracy.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

The increasing complexity and dynamism of new technology implemented or to implement have imposed substantial uncertainties and subjectivities in the risk assessment process. This paper aims to present a risk assessment methodology for e-procurement implementation based on modified analytic network process (ANP) coupled with fuzzy inference systems.

ANP is modified in such a way that the experts can provide necessary data precise numerical value, a range of numerical values, a linguistic term or a fuzzy number. The proposed methodology incorporates knowledge and judgements obtained from experts to carry out identification of risk factors and to assess the risk magnitude of the identified risk factors based on factor index, risk likelihood and risk severity.

Risk magnitude of third party systems are found to be minor with a belief of 100 per cent, and for in-house systems, the risk is found to be between minor with a belief of 30 per cent and major of 70 per cent. The results indicate that by using the proposed methodology, the technological risk assessment of new technology can be done effectively and efficiently.

Using the results of this study, the practitioners can better know the pros and cons of implementing both in-house and third party e-procurement systems.

The modified ANP is used mainly to structure and prioritize the diverse risk factors. Finally, an illustrative example on technological risk assessment of both in-house and third party e-procurement systems is used to demonstrate the applicability of the proposed methodology in real life situations.

Double auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of this study is to develop novel bidding strategies for dynamic double auction markets, explain price formation through interactions of buyers and sellers in decentralized fashion and compare macro market outputs of different micro bidding strategies.

In this study, two novel bidding strategies based on fuzzy logic are presented. Also, four new bidding strategies based on price targeting are introduced for the aim of comparison. The proposed bidding strategies are based on agent-based computational economics approach. The authors performed multi-agent simulations of double auction market for each suggested bidding strategy. For the aim of comparison, the zero intelligence strategy is also used in the simulation study. Various market outputs are obtained from these simulations. These outputs are market efficiencies, price means, price standard deviations, profits of sellers and buyers, transaction quantities, profit dispersions and Smith’s alpha statistics. All outputs are also compared to each other using t-tests and kernel density plots.

The results show that fuzzy logic-based bidding strategies are superior to price targeting strategies and the zero intelligence strategy. The authors also find that only small number of inputs such as the best bid, the best ask, reference price and trader valuations are sufficient to take right action and to attain higher efficiency in a fuzzy logic-based bidding strategy.

This paper presents novel bidding strategies for dynamic double auction markets. New bidding strategies based on fuzzy logic inference systems are developed, and their superior performances are shown. These strategies can be easily used in market-based control and automated bidding systems.

The purpose of this paper is to ascertain overall system health and maintenance needs with degree of certainty using condition‐monitoring data with hierarchical fuzzy inference system.

In process plants, equipment condition is ascertained using condition‐monitoring data for each condition indicator. For large systems with multiple condition indicators, estimating the overall system health becomes cumbersome. The decision of selecting the equipment for an overhaul is mostly determined by generic guidelines, and seldom backed up by condition‐monitoring data. The proposed approach uses a hierarchical system health assessment using fuzzy inference on condition‐monitoring data collected over a period. Each subsystem health is ascertained with degree of certainty using degree of match operation performed on fuzzy sets of condition‐monitoring data and expert opinion. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge.

The proposed approach has been applied to a large electric motor (> 500kW), which is treated as four subsystems i.e. power transmission system, electromagnetic system, ventilation system and support system. Fuzzy set of condition‐monitoring data of each condition indicator on each subsystem is used to ascertain the degree of match with the expert opinion fuzzy set, thus inferring the need for periodical overhaul. Subjective expert opinion and quantitative condition‐monitoring data have been evaluated using hierarchical fuzzy inference system with a rule base. It is found that the certainty of each subsystem's health is not the same at the end of 600 days of monitoring and can be classified as “very good”, “good”, “marginal” and “sick”. Degree of certainty has helped in taking a managerial decision to avoid “over‐maintenance” and to ensure reliability. Large volumes of condition‐monitoring data not only helped in assessing motor overhaul health, but also guide the maintenance engineer to suitably review maintenance/monitoring strategy on similar systems to achieve desired reliability goals.

Condition‐monitoring data collected for long periods can be utilized to understand the degree of certainty of degradation pattern in the longer time frame with reference to domain knowledge to improve effectiveness of predictive maintenance towards reliability.

The paper gives an opportunity to evaluate quantitative condition‐monitoring data and subjective/qualitative domain expertise using fuzzy sets. The predictive maintenance cycle “Monitor‐analyse‐plan‐repair‐restore‐operate” is scientifically regulated with a degree of certainty. Approach is generic and can be applied to a variety of process equipment to ensure reliability through effective predictive maintenance.

In order to ensure the uninterrupted supply of items, the purchasing manager needs to evaluate suppliers' performance periodically. The evaluation process typically consists of identifying the attributes and factors relevant to the decision, and measuring the performance of a supplier by considering the relevant factors. Linguistic assessment of suppliers may be carried out based on several criteria. In this paper, an attempt has been made to evaluate the suppliers' performance by adopting an evolutionary fuzzy system. One of the key considerations in designing the proposed system is the generation of fuzzy rules. A genetic algorithm‐based methodology is developed to evolve the optimal set of fuzzy rule base, and a fuzzy inference system of the MATLAB fuzzy logic toolbox is used to assess the suppliers' performance. The proposed methodology, illustrated with the data collected in a process plant, provides acceptable results in determining the suppliers' performance score.

To permit the system safety and reliability analysts to evaluate the criticality or risk associated with item failure modes.

The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (S_f), severity (S) and detectability (S_d) of an item failure mode. Because of the subjective and qualitative nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic is proposed. In the proposed approach, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and are evaluated in fuzzy inference engine, which makes use of well‐defined rule base and fuzzy logic operations to determine the criticality/riskiness level of the failure. The fuzzy conclusion is then defuzzified to get risk priority number. The higher the value of RPN, the greater will be the risk and lower the value of RPN, and the lesser will be the risk. The fuzzy linguistic assessment model was developed using toolbox platform of MATLAB 6.5 R.13.

The applicability of the proposed approach is investigated with the help of an illustrative case study from the paper industry. Fuzzy risk assessment is carried out for prioritizing failure causes of the hydraulic system, a primary element of the feeding system. The results provide an alternate ranking to that obtained by the traditional method. It is concluded from the study that the fuzzy logic‐based approach not only resolves the limitations associated with traditional methodology for RPN evaluation but also permits the experts to combine probability of occurrence (S_f), severity (S) and detectability (S_d) of failure modes in a more flexible and realistic manner by using their judgement, experience and expertise.

The paper integrates the use of fuzzy logic and expert database with FMEA and may prove helpful to system safety and reliability analysts while conducting failure mode and effect analysis to prioritize failures for taking corrective or remedial actions.