Next Article in Journal
On Eccentricity-Based Topological Indices and Polynomials of Phosphorus-Containing Dendrimers
Next Article in Special Issue
Fixed Point Theorem for Neutrosophic Triplet Partial Metric Space
Previous Article in Journal
An Efficient Object Detection Algorithm Based on Compressed Networks
Previous Article in Special Issue
A Hybrid Neutrosophic Group ANP-TOPSIS Framework for Supplier Selection Problems
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(7), 236; https://doi.org/10.3390/sym10070236

An Extended Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with Maximizing Deviation Method Based on Integrated Weight Measure for Single-Valued Neutrosophic Sets

1
Department of Actuarial Science and Applied Statistics, Faculty of Business & Information Science, UCSI University, Jalan Menara Gading, 56000 Kuala Lumpur, Malaysia
2
A-Level Academy, UCSI College KL Campus, Lot 12734, Jalan Choo Lip Kung, Taman Taynton View, 56000 Kuala Lumpur, Malaysia
3
Department of Mathematics, University of New Mexico, 705 Gurley Avenue, Gallup, NM 87301, USA
4
Laboratory of Information Processing, Faculty of Science Ben M’Sik, University Hassan II, B.P 7955, Sidi Othman, Casablanca 20000, Morocco
*
Author to whom correspondence should be addressed.
Received: 7 June 2018 / Revised: 19 June 2018 / Accepted: 20 June 2018 / Published: 22 June 2018
Full-Text   |   PDF [318 KB, uploaded 26 June 2018]

Abstract

A single-valued neutrosophic set (SVNS) is a special case of a neutrosophic set which is characterized by a truth, indeterminacy, and falsity membership function, each of which lies in the standard interval of [0, 1]. This paper presents a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with maximizing deviation method based on the single-valued neutrosophic set (SVNS) model. An integrated weight measure approach that takes into consideration both the objective and subjective weights of the attributes is used. The maximizing deviation method is used to compute the objective weight of the attributes, and the non-linear weighted comprehensive method is used to determine the combined weights for each attributes. The use of the maximizing deviation method allows our proposed method to handle situations in which information pertaining to the weight coefficients of the attributes are completely unknown or only partially known. The proposed method is then applied to a multi-attribute decision-making (MADM) problem. Lastly, a comprehensive comparative studies is presented, in which the performance of our proposed algorithm is compared and contrasted with other recent approaches involving SVNSs in literature. View Full-Text
Keywords: 2ingle-valued neutrosophic set; Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS); integrated weight; maximizing deviation; multi-attribute decision-making (MADM) 2ingle-valued neutrosophic set; Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS); integrated weight; maximizing deviation; multi-attribute decision-making (MADM)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Selvachandran, G.; Quek, S.G.; Smarandache, F.; Broumi, S. An Extended Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with Maximizing Deviation Method Based on Integrated Weight Measure for Single-Valued Neutrosophic Sets. Symmetry 2018, 10, 236.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top