research-article

General Type-2 fuzzy decision making and its application to travel time selection

Authors: Amit K. Shukla, Pranab K. MuhuriAuthors Info & Claims

Journal of Intelligent & Fuzzy Systems, Volume 36, Issue 6

Pages 5227 - 5244

https://doi.org/10.3233/JIFS-18071

Published: 01 January 2019 Publication History

Abstract

The Decision making has been a major research topic in the computing literature for so long due to its vast significance in many real-world applications. Traditional fuzzy decision making (FDM) approaches have limitations due to the inability of the type-1 fuzzy sets (T1 FSs) in modeling higher order uncertainties. Since, the membership function (MF) of an interval type-2 fuzzy set (IT2 FS) is also fuzzy, as superior to T1 FSs, researchers considered IT2 FSs to model higher level of uncertainties in FDM and proposed a number of IT2 FDM methods. However, unlike IT2 FSs, general type-2 fuzzy sets (GT2 FSs) do not consider equal secondary membership values for all its primary membership functions. Hence, GT2 FSs offer more suitability in modelling uncertainties that exist in real-world scenarios. Thus, this paper proposes a more efficient decision making method called the “GT2 Fuzzy Decision Making (GT2 FDM)”, which considers GT2 FSs to model the fuzzy goals and fuzzy constraints in a problem. The working of the proposed approach is demonstrated using an example of room temperature selection. Then we have applied it to the problem of convenient travel time selection using a real-time traffic data set. It is observed that the proposed GT2 FDM approach offers more flexibility to the decision makers in choosing an optimal solution from a much wider solution space and hence is found to be more efficient than the IT2 FDM and classical FDM approaches.

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General Type-2 fuzzy decision making and its application to travel time selection
1. Applied computing
2. Information systems

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cover image Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology Volume 36, Issue 6

2019

1589 pages

ISSN:1064-1246

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© 2019 – IOS Press and the authors. All rights reserved.

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Netherlands

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Published: 01 January 2019

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Cited By

Baskov O(2023)Pareto Set Reduction Based on Information about Type Two Fuzzy Preference Relation. Algorithm DescriptionScientific and Technical Information Processing10.3103/S014768822306002350:6(520-526)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.3103/S0147688223060023
Chakraborty DVarshney AMuhuri PLohani Q(2022)Modified Probabilistic Intuitionistic Fuzzy c-Means Clustering Algorithm: MPIFCM2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE55066.2022.9882890(1-7)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/FUZZ-IEEE55066.2022.9882890
Chakraborty DVarshney AMuhuri PLohani Q(2022)P-IT2IFCM: Probabilistic Interval Type-2 Intuitionistic Fuzzy c-Means Clustering Algorithm2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE55066.2022.9882807(1-7)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/FUZZ-IEEE55066.2022.9882807
Runkler T(2022)Pareto Interval Type–2 Fuzzy Decision Making for Labeled Objects2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE55066.2022.9882586(1-6)Online publication date: 18-Jul-2022
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Noghin VBaskov O(2021)On Multicriteria Choice Based on Type-2 Fuzzy Preference Relation: an Axiomatic Approach2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ45933.2021.9494489(1-5)Online publication date: 11-Jul-2021
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Seth TMuhuri P(2020)Perceptual Computing with Comparative Linguistic Expressions2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ48607.2020.9177664(1-7)Online publication date: 19-Jul-2020
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