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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.

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.

This paper aims to investigate the distributed coordinated fuzzy tracking problems for multiple mechanical systems with nonlinear model uncertainties under a directed communication topology.

The dynamic leader case is considered while only a subset of the follower mechanical systems can obtain the leader information. First, this paper approximates the system uncertainties with finite fuzzy rules and proposes a distributed adaptive tracking control scheme. Then, this paper makes a detailed classification of the system uncertainties and uses different fuzzy systems to approximate different kinds of uncertainties. Further, an improved distributed tracking strategy is proposed. Closed-loop systems are investigated using graph theory and Lyapunov theory. Numerical simulations are performed to verify the effectiveness of the proposed methods.

Based on fuzzy control and adaptive control theories, the desired distributed coordinated tracking control strategies for multiple uncertain mechanical systems are developed.

Compared with most existing literature, the proposed distributed tracking algorithms use fuzzy control and adaptive control techniques to cope with system nonlinear uncertainties of multiple mechanical systems. Moreover, the improved control strategy not only reduces fuzzy rules but also has higher control accuracy.

This paper seeks to propose a systematic method to design multi fuzzy FACTS based stabilizers in a multi‐machine power system.

Conventional FACTS based stabilizers are decentralized controllers that adopt local measurements and operate in closed loop. To improve overall system dynamic performance, a coordinating application of FACTS based stabilizer is essential. Although, numerous researches have indicated the effectiveness and superiority of fuzzy logic controllers in comparison with the conventional linear controllers in power system application but researchers have not adequately investigated coordination of multi fuzzy controllers in multi‐machine power systems to provide optimal performance. Genetic algorithm is used to determine optimum values of controllers' parameters.

The search space of the optimisation procedure is decreased to a smaller one, design and computation time can be reduced significantly and the design process becomes more systematic.

A systematic method is introduced to coordinate multi fuzzy FACTS based stabilizers in multi‐machine power systems.

The purpose of this paper is to investigate the performance of a turbine structure of the oil and gas Egyptian company in terms of reliability, mean time to failure (MTTF), mean time to repair (MTTR) and mean time between failures (MTBF) under fuzzy environment and working criteria. This paper examines the impact of the failure of various components on the complete turbine structure of the oil and gas system.

To overcome the problem of uncertain behavior of available data for various components, the right triangular generalized fuzzy number (RTrGFN) is proposed to be taken into the account to express the uncertainty which attains some tolerance in data. Furthermore, reliability indices are calculated with the help of the Lambda Tau method and the arithmetic operations on right generalized triangular fuzzy numbers (RTrGFN).

This paper explores the reliability of a repairable 3 out of 4 structure of turbines and along with the other parameters namely MTTF, MTTR and MTBF; under a fuzzy environment. Failure rates and repair times are expected to be exponential. The ranking of components of the structure is being found to decide the priority for maintenance.

This paper investigates the performance of the system with different spread/tolerance like 15%, 25% and 50% of crisp data. It helps to predict realistic results in the range value. To enhance the system's performance, the most important item of the system requires greater attention. For this, the authors find the sensitive part by ranking. For ranking, an extended approach has been developed to find the sensitive unit of the system by using the right triangular generalized fuzzy number. This paper explores the most and least sensitive component of the system, which helps the maintenance department to plan the maintenance action.

A plethora of studies focused on the cause and solutions for the bullwhip effect, and consequently many have successfully experimented to dampen the effect. However, the feasibility of such studies and the actual contribution for supply chain performance are yet up for debate. This paper aims to fill this gap by providing a holistic system-based perspective and proposes a fuzzy logic decision-making implementation for a single-product, three-echelon and multi-period supply chain system to mitigate such effect.

This study uses system dynamics (SD) as the central modeling method for which Vensim® is used as a tool for hybrid simulation. Further, the authors used MATLAB for undertaking fuzzy logic modeling and constructing a fuzzy inference system that is later on incorporated into SD model for interaction with the main supply chain structure.

This research illustrated the usefulness of fuzzy estimations based on experts’ linguistically and logically defined parameters instead of relying merely on the traditional demand forecasting based on time series. Despite the increased complexity of the calculations and structure of the fuzzy model, the bullwhip effect has been considerably decreased resulting in an improved supply chain performance.

This dynamic modeling approach is not only useful in supply chain management but also the model developed for this study can be integrated into a corporate financial planning model. Further, this model enables optimization for an automated system in a company, where decision-makers can adjust the fuzzy variables according to various situations and inventory policies.

This study presents a systemic approach to deal with uncertainty and vagueness in dynamic models, which might be a major cause in generating the bullwhip effect. For this purpose, the combination between fuzzy set theory and system dynamics is a significant step forward.

The purpose of this paper is to look at the problem of fault tolerant control (FTC) for discrete time nonlinear system described by Interval Type-2 Takagi–Sugeno (IT2 TS) fuzzy model subjected to stochastic noise and actuator faults.

An IT2 fuzzy augmented state observer is first developed to estimate simultaneously the system states and the actuator faults since this estimation is required for the design of the FTC control law. Furthermore, based on the information of the states and the faults estimate, an IT2 fuzzy state feedback controller is conceived to compensate for the faults effect and to ensure a good tracking performance between the healthy system and the faulty one. Sufficient conditions for the existence of the IT2 fuzzy controller and the IT2 fuzzy observer are given in terms of linear matrix inequalities which can be solved using a two-step computing procedure.

The paper opted for simulation results which are applied to the three-tank system. These results are presented to illustrate the effectiveness of the proposed FTC strategy.

In this paper, the problem of active FTC design for noisy and faulty nonlinear system represented by IT2 TS fuzzy model is treated. The developed IT2 fuzzy fault tolerant controller is designed such that it can guarantee the stability of the closed-loop system. Moreover, the proposed controller allows to accommodate for faults, presents a satisfactory state tracking performance and outperforms the traditional type-1 fuzzy fault tolerant controller.

Accounting, particularly in the area of cost variance analysis, contains a great deal of ambiguity due to imprecise or ill‐defined control terms. Cost accountants must continually incorporate good sense and professional judgment in the accounting process to overcome that ambiguity. Because of the construction of accounting expert systems, no ambiguity is present in the facts or rules, thereby excluding human reasoning and analysis of feedback within those systems. The use of fuzzy sets to build fuzzy control systems provides a method to incorporate ambiguity into expert systems, allowing expert systems to more closely emulate the complex human decision making process.