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Fuzzy Logic-Based Approaches for Estimating Efforts Invested in Component Selection
Published in Kirti Seth, Ashish Seth, Aprna Tripathi, Component-Based Systems, 2020
Kirti Seth, Ashish Seth, Aprna Tripathi
Among the various existing quantities, fuzzy number is one among them. The value for this is imprecise. This value will be found to be similar to a normal single value number. The fuzzy number is defined as a membership function. The domain of the function is more specified. The domain contains real numbers in the range between the [0, 1] interval is referred to as a positive number. A specified value will be allocated for every numerical value in the domain. The highest possible value in this membership function is 1, similarly the lowest possible value is 0. The various forms for plotting fuzzy numbers are given as follows: Parabolic shaped fuzzy numbers.Trapezoidal fuzzy numbers.Triangular fuzzy numbers.
Fuzzy logic systems
Published in A. W. Jayawardena, Environmental and Hydrological Systems Modelling, 2013
A fuzzy number is a special case of fuzzy sets. It has an increasing part, a decreasing part, and sometimes a flat part. The simplest fuzzy number is a triangle, which has zero values outside the universe of discourse and piece-wise linear parts in between. The piece-wise parts need not necessarily be symmetrical. A triangular fuzzy number can be specified by three values: the most likely value with a membership function value of unity, the lowest possible value with a membership function value of zero, and the highest possible value with a membership function value of zero. The membership function will have values of zero outside the latter two values. The interval between the lowest and highest possible values is called the support of the fuzzy number.
Entropy-Based Fuzzy Reliability–Redundancy Allocation Model
Published in Harish Garg, Mangey Ram, Reliability Management and Engineering, 2020
Fuzzy Number: A fuzzy number is a quantity which is imprecise, as opposed to the case with a single valued number. A fuzzy number measurement not alluded to one single value is an associated set of conceivable qualities, where every conceivable quality has a weight somewhere in the range of 0 and 1. This weight is called the membership function, which has the following form: μB˜(x):R→[0,1].
Fuzzy Gaussian mixture optimisation of the newsvendor problem: mixing fuzzy perception and randomness of customer demand
Published in International Journal of Production Research, 2023
Farzad Fathizadeh, Jean Savinien, Yacine Rekik
The concept of a fuzzy number is useful for modelling a quantity when there is a range of possibilities for the quantity, and one wishes to give values (or weights) ranging from 0 to 1 to the possibilities. More precisely, a fuzzy number is a function of the form where is a continuous non-decreasing function, and is a continuous non-increasing function such that and . That is, a fuzzy number is not a certain number, and allows one to think of to be the value of the possibility that the uncertain quantity will be x. We will denote the set of fuzzy numbers by .
A novel risk evaluation for vehicle failure modes using a hybrid method under fuzzy environment
Published in International Journal of Crashworthiness, 2022
Wencai Zhou, Zhaowen Qiu, Fenghui Wang, Lang Wei, Reza Langari
In this paper, we propose a hybrid approach focusing on classifying and ranking vehicle failure modes based on fuzzy AHP and VIKOR under fuzzy environment. The method can easily be adapted to evaluating risk of failure in other fields. In this hybrid method, the weights of three new risk criteria are obtained by utilising fuzzy AHP and the rank of vehicle failure modes is presented by employing fuzzy VIKOR. Imprecision in multi-criteria decision making is modelled using fuzzy set theory to define criteria and the importance of criteria. The fuzzy numbers are used to handle imprecise numerical quantities. The VIKOR method is based on the aggregating fuzzy merit Q that represents distance of a vehicle failure mode to the ideal alternative. An empirical study is conducted in Hyundai vehicle to verify the feasibility of purposed method. As a result, the failure mode F2 is the most critical failure mode for this model, followed by the alternatives F1, F3, F4 F5. The selected ranking almost agrees with those obtained by cost-based FMEA and MULTIOORA with same risk criteria. Therefore, it has been validated that the proposed fuzzy hybrid method is an effective tool in evaluating the risk of vehicle failure modes with interdependent factors and compromise alternatives.
Possibilistic Pareto-dominance approach to support technical bid selection under imprecision and uncertainty in engineer-to-order bidding process
Published in International Journal of Production Research, 2021
Abdourahim Sylla, Thierry Coudert, Elise Vareilles, Laurent Geneste, Michel Aldanondo
With the fuzzy set theory, the imprecise and uncertain values of the decision criteria are modelled with fuzzy numbers (Durbach and Stewart 2012; Longaray et al. 2019). The possibility theory is known to be a very good framework to simultaneously deal with imprecision (vagueness) and uncertainty due to a lack of accurate and complete information (knowledge) (French 1995; Solaiman and Éloi Bossé 2019; Hose, Mäck, and Hanss 2019; Denœux, Dubois, and Prade 2020). Moreover, it also permits to easily and effectively take into account expert's points of view (thus to take into account the confidence of the bidders in each technical bid solution) (Dubois and Prade 2012). Therefore, in this article, we consider the possibility theory framework to cope with uncertainty, imprecision and confidence related to the values of the decision criteria.