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11 – 20 of over 8000Baki 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 this…
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.
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We introduce a four‐valued logic that includes, in addition to true and false, the values unknown and non‐existent. We introduce the idea of presupposition in fuzzy logic and then…
Abstract
We introduce a four‐valued logic that includes, in addition to true and false, the values unknown and non‐existent. We introduce the idea of presupposition in fuzzy logic and then use this to relate this four valued logic to the binary logic.
Kumar S. Ray and Arpan Chakraborty
The importance of fuzzy logic (FL) in approximate reasoning, and that of default logic (DL) in reasoning with incomplete information, is well established. Also, the need for a…
Abstract
Purpose
The importance of fuzzy logic (FL) in approximate reasoning, and that of default logic (DL) in reasoning with incomplete information, is well established. Also, the need for a commonsense reasoning framework that handles both these aspects has been widely anticipated. The purpose of this paper is to show that fuzzyfied default logic (FDL) is an attempt at creating such a framework.
Design/methodology/approach
The basic syntax, semantics, unique characteristics and examples of its complex reasoning abilities have been presented in this paper.
Findings
Interestingly, FDL turns out to be a generalization of traditional DL, with even better support for non‐monotonic reasoning.
Originality/value
The paper presents a generalized tool for commonsense reasoning which can be used for inference under incomplete information.
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S.A. Oke and O.E. Charles‐Owaba
The simultaneous scheduling of resource‐constrained maintenance and operations is addressed in this work. The purpose of the paper is to capture the uncertainty in the development…
Abstract
Purpose
The simultaneous scheduling of resource‐constrained maintenance and operations is addressed in this work. The purpose of the paper is to capture the uncertainty in the development of a model that schedules both preventive maintenance and operational activities. Fuzzy logic is employed to transform the human expertise into IF‐THEN rules.
Design/methodology/approach
The approach has the advantage of revealing semantic uncertainty with the associated non‐specifying measures. The methodology applied tracks the error values in terms of results in linguistic variable.
Findings
The results obtained indicate the feasibility of tracking the uncertain measures in the model discussed. Thus, the study may be applicable to both production system and transportation organizations that are engaged in both maintenance and operational activities.
Practical implications
The research has serious implication in terms of the ability to monitor the imprecision that were introduced in the previous models. This obviously provides a more reliable framework for researchers and practitioners interested in maintenance scheduling activities.
Originality/value
The paper is new in that it demonstrates the application of fuzzy logic in a form that was never documented.
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Devin DePalmer, Steven Schuldt and Justin Delorit
Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with…
Abstract
Purpose
Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.
Design/methodology/approach
A Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.
Findings
Results of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.
Originality/value
This paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.
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Although fuzzy logic is being extensively used in electronics and mathematical sciences, it has found little or no application in the social sciences, especially criminology. As a…
Abstract
Although fuzzy logic is being extensively used in electronics and mathematical sciences, it has found little or no application in the social sciences, especially criminology. As a mathematical system, fuzzy logic generalizes the Boolean logic and can be a very useful tool for the social sciences where concepts and terms involve shades of meanings. Outlines the essential mathematics behind this approach and develops a technique that could be useful in building offender profiles from fuzzy descriptions provided by witnesses. Also suggests several other possible areas of applications of this mathematical system.
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Pratesh Jayaswal, S.N. Verma and A.K. Wadhwani
The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The…
Abstract
Purpose
The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.
Design/methodology/approach
A detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.
Findings
The development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.
Practical implications
The work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.
Originality/value
The main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.
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P. Baguley, T. Page, V. Koliza and P. Maropoulos
Time to market is the essential aim of any new product introduction process. Performance measures are simple quantities that indicate the state of manufacturing organisations and…
Abstract
Purpose
Time to market is the essential aim of any new product introduction process. Performance measures are simple quantities that indicate the state of manufacturing organisations and are used as the basis of decision‐making at this crucial early stage of the process. Fuzzy set theory is a method for using qualitative data and subjective opinion. Fuzzy sets have been used extensively in manufacturing for applications including control, decision‐making, and estimation. Type‐2 fuzzy sets are a novel extension of type‐1 fuzzy sets. Aims to examine this subject.
Design/methodology/approach
This research explores the increased use of type‐2 fuzzy sets in manufacturing. In particular, type‐2 fuzzy sets are used to model “the words that mean different things to different people”.
Findings
A model that can leverage design process knowledge and predict time to market from performance measures is a potentially valuable tool for decision making and continuous improvement. A number of data sources, such as process maps, from previous research into time to market in a high technology products company, are used to structure and build a type‐2 fuzzy logic model for the prediction of time to market.
Originality/value
This paper presents a demonstration of how the type‐2 fuzzy logic model works and provides directions for further research into the design process for time to market.
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The purpose of this paper is to report a case study in which artificial neural network (ANN) has been used for performing fuzzy logic based leanness assessment.
Abstract
Purpose
The purpose of this paper is to report a case study in which artificial neural network (ANN) has been used for performing fuzzy logic based leanness assessment.
Design/methodology/approach
Leanness is the measure of lean manufacturing practice. Fuzzy logic has been used for the calculation of leanness. To improve the effectiveness of computation, ANN tool has been used in this study. The network has been modeled, trained and simulated using the MATLAB software.
Findings
The disadvantages associated with the scoring method has been overcome by the deployment of fuzzy logic. The problem associated with manual computation has been overcome by the application of ANN. The simulated model has been validated by measuring the leanness level of the case organization.
Research limitations/implications
The case study has been carried out in a single electronic switches manufacturing organization. In the fuzzy logic approach, triangular fuzzy numbers are being used in the present study.
Practical implications
The paper reports a case study conducted in an Indian transformers manufacturing organisation. Hence, the results derived from the study are validated in a real time manufacturing environment.
Originality/value
The idea of applying ANN for fuzzy logic based leanness assessment is the original contribution of the authors.
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