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Article
Publication date: 1 May 1995

Linda Stotts and Brian H. Kleiner

Sets out to provide an understanding of the theory of fuzzy logicby supplying background details concerning its evolution in mathematicsand computer science. Once a basic…

155

Abstract

Sets out to provide an understanding of the theory of fuzzy logic by supplying background details concerning its evolution in mathematics and computer science. Once a basic understanding of the theory is obtained, then it is easier to understand the implications for computer applications. Fuzzy logic processors and compilers have facilitated the development of expert systems that typically use a lot of imprecise data. These expert systems have been used successfully as control units in industrial settings and as decision support systems in hospital settings. Fuzzy logic has been found to be a practical and viable form of artificial intelligence that mitigates the current drawbacks of other forms of artificial intelligence. But the really exciting development that is poised to emerge is the introduction of fuzzy logic appliances. These appliances employ an expert system on a chip that is able to mimic the range of flexibility of the human mind, while utilizing resources more efficiently.

Details

Industrial Management & Data Systems, vol. 95 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 1 September 1994

R Bannatyne

Examines the growth of a new technology called fuzzy logic and itssignificance for microcontroller‐based embedded control solutions.Outlines the reasons for the emergence…

485

Abstract

Examines the growth of a new technology called fuzzy logic and its significance for microcontroller‐based embedded control solutions. Outlines the reasons for the emergence of fuzzy logic and explains the mathematic principles behind fuzzy set theory. Using the example of an oven temperature control system, describes how fuzzy logic is applied to the practical solution of a control problem rather than a conventional solution. Concludes that fuzzy logic has been used primarily in embedded control application as a software‐based methodology in closed‐loop control systems whilst a dedicated fuzzy hardware processor would optimally be based on a parallel architecture, allowing the entire rule base to be evaluated in a parallel fashion.

Details

Sensor Review, vol. 14 no. 3
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 1 May 2003

Kostas Metaxiotis, John Psarras and Emanuel Samouilidis

Companies deal with many decision‐making processes whose impact on the global performance can be very strong. As a consequence, the role of the decision support systems…

2382

Abstract

Companies deal with many decision‐making processes whose impact on the global performance can be very strong. As a consequence, the role of the decision support systems (DSSs) within the organization is critical. Considering the imprecise or fuzzy nature of the data in real‐world problems, it becomes obvious that the ability to manage uncertainty turns out to be a crucial issue for a DSS. In this framework, this paper discusses the key role of fuzzy logic (FL) in the DSSs, presents new applications of FL in DSSs in various sectors and identifies new challenges and new directions for further research.

Details

Information Management & Computer Security, vol. 11 no. 2
Type: Research Article
ISSN: 0968-5227

Keywords

Article
Publication date: 1 April 1996

Edward T. Lee

Manufacturing is a key to continuous economic growth. Fuzzy expert systems, fuzzy logics, fuzzy languages, fuzzy neural networks, and intelligent control are proposed as…

430

Abstract

Manufacturing is a key to continuous economic growth. Fuzzy expert systems, fuzzy logics, fuzzy languages, fuzzy neural networks, and intelligent control are proposed as additional tools in manufacturing. Fuzzy logic is a new way to program computers and appliances to mimic the imprecise way humans make decisions. Fuzzy logic has been applied to cameras, subways, computers and air conditioners. Through the use of fuzzy logic, fuzzy expert systems can be built which add a new dimension in the technologies for intelligent factories.

Details

Kybernetes, vol. 25 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 10 March 2022

Vishal Ashok Wankhede and S. Vinodh

The purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.

Abstract

Purpose

The purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.

Design/methodology/approach

I4.0 implies fourth industrial revolution that necessitates vital challenges to be dealt with. In this viewpoint, this article presents the evaluation of I4.0 Readiness Index. The evaluation includes two levels with appropriate criteria and factors. Fuzzy logic approach is used for assessment. Furthermore, the results obtained from fuzzy logic have been benchmarked with multi-grade fuzzy approach.

Findings

The proposed assessment model has successfully utilized fuzzy logic approach for assessment of I4.0 readiness index of automotive component manufacturing organization. Based on fuzzy logic approach, readiness index of I4.0 has been found to be (4.74, 6.26, 7.80) which is further benchmarked using multi-grade fuzzy approach. Industry 4.0 readiness index obtained from multi-grade fuzzy approach is 6.258 and thus, validated. Furthermore, 20 weaker areas have been identified and improvement suggestions are provided.

Research limitations/implications

The assessment module include two levels (Six Criteria and 50 Factors). The assessment model could be expanded based on advancements in industrial developments. Therefore, future researchers could utilize findings of the readiness model to further develop multi-level assessment module for Industry 4.0 readiness in organization. The developed readiness model helped researchers in understanding the methodology to assess I4.0 readiness of organization.

Practical implications

The model has been tested with reference to automotive component manufacturing organization and hence the inferences derived have practical relevance. Furthermore, the benchmarking strategy adopted in the present study is simple to understand that makes the model unique and could be applied to other organizations. The results obtained from the study reveal that fuzzy logic-based readiness model is efficient to assess I4.0 readiness of industry.

Originality/value

The development of model for I4.0 readiness assessment and further analysis is the original contribution of the authors. The developed fuzzy logic based I4.0 readiness model indicated the readiness level of an organization using I4RI. Also, the model provided weaker areas based on FPII values which is essential to improve the readiness of organization that already began with the adoption of I4.0 concepts. Further modification in the readiness model would help in enhancing I4.0 readiness of organization. Moreover, the benchmarking strategy adopted in the study i.e. MGF would help to validate the computed I4.0 readiness.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Book part
Publication date: 5 October 2018

Aminah Robinson Fayek and Rodolfo Lourenzutti

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of…

Abstract

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

Details

Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

Keywords

Open Access
Article
Publication date: 25 March 2021

Per Hilletofth, Movin Sequeira and Wendy Tate

This paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Abstract

Purpose

This paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Design/methodology/approach

Two fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.

Findings

The findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.

Research limitations/implications

The developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.

Practical implications

The support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.

Originality/value

There is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Details

Industrial Management & Data Systems, vol. 121 no. 5
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 26 June 2009

Eddie Chi Man Hui, Otto Muk Fai Lau and Tony Kak Keung Lo

The purpose of this paper is to explore the application of fuzzy logic in real estate investment in Hong Kong. There have been sufficient debates on the literature…

1240

Abstract

Purpose

The purpose of this paper is to explore the application of fuzzy logic in real estate investment in Hong Kong. There have been sufficient debates on the literature, providing the theoretical background on real estate investment decisions but there has been a lack of empirical support in this regard. This paper attempts to fill the gap between theorem and application.

Design/methodology/approach

The fuzzy logic system is adopted to evaluate the situation of a real estate market with imprecise and vague information. An indicator‐portfolio, rather than a specific indicator/index usually employed by practitioners, is explored to assist investors in risk management. The result derived from this framework is then compared to the property price index. This approach provides a framework in understanding the market without statistical and mathematical models. It tries to stimulate the complex human cognitive process involving decision making.

Findings

The housing‐indicator portfolio composition produces an outcome value which is able to reflect the complexities of both the real estate market and investors' expectations. An increase of this value implies that the investment condition is becoming more positive.

Research limitations/implications

The paper reveals that fuzzy logic can provide some insights in an intuitive manner and is capable of obtaining information not found in market data. It is particularly useful to investors without experience in mathematical modeling.

Practical implications

This paper establishes a basic framework of fuzzy logic for real estate investment on which a base is formed as a reference for practitioners and investors. However, they should make references to the specific housing‐indicator portfolio composition in their own regions.

Originality/value

This paper has used a fuzzy logic system to assist practitioners as well as investors on decision making in real estate investment with imperfect market information. With the aid of the system, practitioners and investors are able to enhance their investment decision‐making quality by reducing the risk incurred by such uncertainties.

Details

Property Management, vol. 27 no. 3
Type: Research Article
ISSN: 0263-7472

Keywords

Article
Publication date: 1 October 1995

Edward T. Lee and Te‐Shun Chou

The set of fuzzy threshold functions is defined to be a fuzzy set over the set of functions. All threshold functions have full memberships in this fuzzy set. Defines and…

Abstract

The set of fuzzy threshold functions is defined to be a fuzzy set over the set of functions. All threshold functions have full memberships in this fuzzy set. Defines and investigates a distance measure between a non‐linearly separable function and the set of all threshold functions. Defines an explicit expression for the membership function of a fuzzy threshold function through the use of this distance measure and finds three upper bounds for this measure. Presents a general method to compute the distance, an algorithm to generate the representation automatically, and a procedure to determine the proper weights and thresholds automatically. Presents the relationships among threshold gate networks, artificial neural networks and fuzzy neural networks. The results may have useful applications in logic design, pattern recognition, fuzzy logic, multi‐objective fuzzy optimization and related areas.

Details

Kybernetes, vol. 24 no. 7
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 5 September 2018

Baki Unal and Çagdas Hakan Aladag

Double auctions are widely used market mechanisms on the world. Communication technologies such as internet increased importance of this market institution. The purpose of…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Kybernetes, vol. 48 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

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