Application of a fuzzy multi-objective defuzzification method to solve a transportation problem
TL;DR: A novel approach to defuzzify fuzzy numbers using a modification of center of gravity COG method with multi-objective linear programming (LP) model to find the appropriate crisp value that satisfies all relationships and minimise the distance among this value and all values in the interval of fuzzy numbers.
About: This article is published in Materials Today: Proceedings.The article was published on 2021-02-18. It has received 34 citations till now. The article focuses on the topics: Fuzzy number & Defuzzification.
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TL;DR: The aim of the researcher was to determine the effectiveness of artificial intelligence techniques against cyber security risks particularly in case of Iraq and the quantitative method of research design along with primary data was opted.
144 citations
TL;DR: There is a need for cybersecurity because it defines the body of processes, technologies, and practices in terms of how they are designed to help protect various programs, devices, networks, and data from the malicious persons who perform unauthorized access and damage these resources.
139 citations
TL;DR: This paper aims to briefly address the psychological biometric authentication techniques and a brief summary to the advantages, disadvantages of each method.
17 citations
TL;DR: In this article, the authors acknowledge the Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).
Abstract: Funding Information: Funding: The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).
16 citations
TL;DR: In this paper , the authors systematically reviewed and summarized various multi-objective methods applied to the problems with more than one objective in uncertain environments where uncertainty is expressed using fuzzy sets.
Abstract: • Various multi-objective optimization (MOO) methods are systematically reviewed and summarized. • 439 articles are reviewed on fuzzy MOO published from 1978 to 2019. • The basic features of MOO methods are briefly presented along with current trends. • Recommendations for future research directions are provided. Multi-objective programming is commonly used in the literature when conflicted objectives arise in solving optimization problems. Over the past decades, classical optimization methods have been developed as useful tools to discover optimal solutions for multi-objective problems (MOPs). In recent years, under uncertainty, multi-objective Optimization (MOO) has received much attention due to its practical applications in real-world problems. However, many studies have been conducted on this matter. Some of which ignored the effects of uncertainty on optimization problems. This paper systematically reviews and summarizes various multi-objective methods applied to the problems with more than one objective in uncertain environments where uncertainty is expressed using fuzzy sets. In this paper, 439 articles on fuzzy multi-objective programming published from 1978 to 2021 are reviewed using corresponding texts, charts, and tables. Finally, the basic features of MOO are briefly presented, along with a prologue of MOO techniques and current trends. Recommendations for further research are also is provided.
11 citations
References
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Book•
01 Jan 1970TL;DR: A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.
Abstract: By decision-making in a fuzzy environment is meant a decision process in which the goals and/or the constraints, but not necessarily the system under control, are fuzzy in nature. This means that the goals and/or the constraints constitute classes of alternatives whose boundaries are not sharply defined.
An example of a fuzzy constraint is: “The cost of A should not be substantially higher than α,” where α is a specified constant. Similarly, an example of a fuzzy goal is: “x should be in the vicinity of x0,” where x0 is a constant. The italicized words are the sources of fuzziness in these examples.
Fuzzy goals and fuzzy constraints can be defined precisely as fuzzy sets in the space of alternatives. A fuzzy decision, then, may be viewed as an intersection of the given goals and constraints. A maximizing decision is defined as a point in the space of alternatives at which the membership function of a fuzzy decision attains its maximum value.
The use of these concepts is illustrated by examples involving multistage decision processes in which the system under control is either deterministic or stochastic. By using dynamic programming, the determination of a maximizing decision is reduced to the solution of a system of functional equations. A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.
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TL;DR: It is shown that solutions obtained by fuzzy linear programming are always efficient solutions and the consequences of using different ways of combining individual objective functions in order to determine an “optimal” compromise solution are shown.
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TL;DR: This paper picks up key points in applying fuzzy control and shows very recent results in industrial applications and points out some interesting and important problems to be solved.
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TL;DR: This paper presents a model for a Fuzzy Rule-Based System that automates the very labor-intensive and therefore time-heavy process of decision-making in the context of classical sets.
Abstract: Classical Sets and Fuzzy Sets.- Classical and Fuzzy Relations.- Membership Functions.- Defuzzification.- Fuzzy Rule-Based System.- Fuzzy Decision Making.- Applications of Fuzzy Logic.- Fuzzy Logic Projects with Matlab.
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