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1 – 10 of over 9000Per 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.
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Rajkumar Ohdar and Pradip Kumar Ray
In order to ensure the uninterrupted supply of items, the purchasing manager needs to evaluate suppliers' performance periodically. The evaluation process typically consists of…
Abstract
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.
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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.
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S. P. Sarmah and U. C. Moharana
The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities…
Abstract
Purpose
The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation.
Design/methodology/approach
Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models.
Findings
Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs.
Practical implications
The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and C considering multiple criteria and relationships among those criteria. The framework is flexible enough to add additional criteria and to modify fuzzy-rule-base at any point of time by the decision makers. This model can be easily integrated to any customized Enterprise Resource Planning applications.
Originality/value
The value of this paper is in applying Fuzzy-rule-based approach for Multi-criteria Inventory Classification of spare parts. This rule-based approach considering multiple criteria is not very common in classification of spare parts inventories. Total cost comparison is made to compare the performance of proposed model with the traditional classifications and the result shows that proposed fuzzy-rule-based classification approach performs better than the traditional ABC and gives almost the same cost as aggregate scoring model. Hence, this method is valid and adds a new value to spare parts classification for better management decisions.
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Yuriy Panteliyovych Kondratenko, Leonid Pavlovych Klymenko and Eyad Yasin Mustafa Al Zu'bi
The purpose of this paper is to propose a general method to simplify the structure of fuzzy controllers' rule base using integrated methodology for reducing the number of fuzzy…
Abstract
Purpose
The purpose of this paper is to propose a general method to simplify the structure of fuzzy controllers' rule base using integrated methodology for reducing the number of fuzzy rules based on modelling and simulation.
Design/methodology/approach
The paper considers the problem of developing effective methods and algorithms for optimization of fuzzy rules bases of Sugeno‐type fuzzy controllers that can be applied to control of dynamic objects, including objects with non‐stationary parameters. The proposed approach based on calculating the impact of each of the rule on the formation of control signals for different types of input signals provides optimization of a linguistic rules database by using exclusion mechanism for rules with negligible influence. The effectiveness of the proposed approach is investigated using a fuzzy PID controller for control of a non‐stationary object of second order.
Findings
In this paper, the authors argued that different aggregation models can be used for structural optimization of fuzzy controllers and not all the rules are actually required in the fuzzy controllers' rule base. Eliminating some of the rules does not necessarily lead to a significant change in the fuzzy controller's output. The proposed integrated approach based on combination of different kinds of reference input signals for fuzzy controllers modelling and simulation is able to provide guidelines to the users which rules are required and which can be eliminated. The results obtained from the case studies demonstrate that the proposed integrated approach is able to reduce the number of rules required and, at the same time, to have the desired values of quality control indices.
Research limitations/implications
In order to demonstrate the feasibility of the proposed approach, only control object of second order with PID fuzzy controller of Sugeno‐type is chosen. Future studies can advance this research by applying the proposed approach in different types of fuzzy systems.
Practical implications
The proposed integrated approach is able to simplify the structural optimization methodology and make it possible to be implemented in real processes of the fuzzy controllers' design.
Originality/value
The value of the current paper is on the proposal of an integrated approach for rule reduction to enhance the practical use of modelling and simulation in a design of fuzzy controllers.
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Mohammad Rahim and Seyed Móhammad‐Bagher Malaek
The purpose of this paper is to present a novel approach in terrain following (TF) flight using fuzzy logic. The fuzzy controller as presented in this work decides where and how…
Abstract
Purpose
The purpose of this paper is to present a novel approach in terrain following (TF) flight using fuzzy logic. The fuzzy controller as presented in this work decides where and how the aircraft needs to change its altitude. The fast decision‐making nature of this method promises real‐time applications even for tough terrains in terms of shape and peculiarities. The method could always assist to design trajectories in an off‐line manner.
Design/methodology/approach
To achieve the aforementioned goal, the method effectively incorporates the dynamics of the aircraft. Basically, the mathematical method employs special relationships among existing slope of the terrain and its derivative together with aircraft flying speed and height above the ground to construct suitable fuzzy rules. The fuzzification method is based on Sugeno and three rule‐sets are used for fuzzy structure. These rules are implemented using Fuzzy Logic Toolbox in MATLAB.
Findings
Different case studies conducted for flights in XZ‐plane show the effectiveness of the method as compared to other existing methods available to the authors. The results illustrate a good tracking based on the fuzzy approach while using both 18 and 27 rules with respect to the optimal approach. Furthermore, it is shown that decreasing number of rules from 27 to 18 rules causes only minor changes in the solution.
Practical implications
The current work offers a new approach in low‐level flights where maintaining a suitable height above the ground is essential. This is especially important for civil aircraft approaching an airport with low or non‐visibility and during aborted landing manoeuvres. The domain of the current work is however confined to only planning of TF manoeuvres. Nevertheless, the work could be expanded into TF/terrain avoidance and three‐dimensional manoeuvres which are not in the scope of the current work.
Originality/value
The current work addresses the problems associated with low‐level flight; such as TF using artificial intelligence and fuzzy logic. The provided intelligence helps the aircraft conduct TF manoeuvres by understanding the general patterns of the existing terrain. The method is fast enough to be applied for real‐time applications.
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Mahmoud Oukati Sadegh and K.L. Lo
This paper seeks to propose a systematic method to design multi fuzzy FACTS based stabilizers in a multi‐machine power system.
Abstract
Purpose
This paper seeks to propose a systematic method to design multi fuzzy FACTS based stabilizers in a multi‐machine power system.
Design/methodology/approach
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.
Findings
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.
Originality/value
A systematic method is introduced to coordinate multi fuzzy FACTS based stabilizers in multi‐machine power systems.
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Andreiwid Sheffer Corrêa, Alexandre de Assis Mota, Lia Toledo Moreira Mota and Pedro Luiz Pizzigatti Corrêa
The purpose of this study is to present a system called NEBULOSUS, which is a fuzzy rule-based expert system for assessing the maturity level of an agency regarding technical…
Abstract
Purpose
The purpose of this study is to present a system called NEBULOSUS, which is a fuzzy rule-based expert system for assessing the maturity level of an agency regarding technical interoperability.
Design/methodology/approach
The study introduces the use of artificial intelligence and fuzzy logic to deal with the imprecision and uncertainty present in the assessment process. To validate the system proposed and demonstrate its operation, the study takes into account the Brazilian technical interoperability maturity model, based on the Brazilian Government Interoperability Framework (GIF).
Findings
With the system proposed and its methodology, it could be possible to increase the assessment process to management level and to provide decision-making support without worrying about technical details that make it complex and time-consuming. Moreover, NEBULOSUS is a standalone system that offers an easy-to-use, open and flexible structuring database that can be adapted by governments throughout the world. It will serve as a tool and contribute to governments’ expectations for continuous improvement of their technologies.
Originality/value
This study contributes toward filling a gap in general interoperability architectures, which is a means to provide an objective method to evaluate GIF adherence by governments. The proposed system allows governments to configure their technical models and GIF to assess information and communication technology resources.
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Henry Lau, C.K.M. Lee, Dilupa Nakandala and Paul Shum
The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their…
Abstract
Purpose
The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment.
Design/methodology/approach
This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes.
Findings
The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome.
Research limitations/implications
The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results.
Originality/value
Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.
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Mohammad Raoufi, Nima Gerami Seresht, Nasir Bedewi Siraj and Aminah Robinson Fayek
Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex…
Abstract
Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.
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