A fuzzy MADM method for uncertain attributes using ranking distribution

Mohammadhossein Amini; Shing Chang; Behnam Malmir

A fuzzy MADM method for uncertain attributes using ranking distribution

Mohammadhossein Amini
, Shing Chang
, Behnam Malmir

Research output: Contribution to conference › Paper › peer-review

Abstract

Decision making methods offer a systematic approach to reach a unique final solution by considering alternatives under a set of criteria or attributes. In the context of multiple attribute decision making many attributes maybe uncertain. This research considers a new way to model uncertainty in decision making environment. The proposed method is based on TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). Uncertain attributes are represented by a range of values modeled by a triangular fuzzy membership function. Simulations are run to generate a rank distribution of alternatives. The final solution overcomes the concern of uncertainty inherited in the original TOPSIS method.

Original language	English
Pages	759-764
Number of pages	6
State	Published - 2020
Event	2016 Industrial and Systems Engineering Research Conference, ISERC 2016 - Anaheim, United States Duration: May 21 2016 → May 24 2016

Conference

Conference	2016 Industrial and Systems Engineering Research Conference, ISERC 2016
Country/Territory	United States
City	Anaheim
Period	05/21/16 → 05/24/16

Keywords

Fuzzy MADM
Simulation
TOPSIS
Uncertainty

Cite this

@conference{87148d1da9294ee3b6b3b04c1b963fa2,

title = "A fuzzy MADM method for uncertain attributes using ranking distribution",

abstract = "Decision making methods offer a systematic approach to reach a unique final solution by considering alternatives under a set of criteria or attributes. In the context of multiple attribute decision making many attributes maybe uncertain. This research considers a new way to model uncertainty in decision making environment. The proposed method is based on TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). Uncertain attributes are represented by a range of values modeled by a triangular fuzzy membership function. Simulations are run to generate a rank distribution of alternatives. The final solution overcomes the concern of uncertainty inherited in the original TOPSIS method.",

keywords = "Fuzzy MADM, Simulation, TOPSIS, Uncertainty",

author = "Mohammadhossein Amini and Shing Chang and Behnam Malmir",

note = "Publisher Copyright: {\textcopyright} 2016 Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016. All rights reserved.; 2016 Industrial and Systems Engineering Research Conference, ISERC 2016 ; Conference date: 21-05-2016 Through 24-05-2016",

year = "2020",

language = "English",

pages = "759--764",

}

TY - CONF

T1 - A fuzzy MADM method for uncertain attributes using ranking distribution

AU - Amini, Mohammadhossein

AU - Chang, Shing

AU - Malmir, Behnam

PY - 2020

Y1 - 2020

N2 - Decision making methods offer a systematic approach to reach a unique final solution by considering alternatives under a set of criteria or attributes. In the context of multiple attribute decision making many attributes maybe uncertain. This research considers a new way to model uncertainty in decision making environment. The proposed method is based on TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). Uncertain attributes are represented by a range of values modeled by a triangular fuzzy membership function. Simulations are run to generate a rank distribution of alternatives. The final solution overcomes the concern of uncertainty inherited in the original TOPSIS method.

AB - Decision making methods offer a systematic approach to reach a unique final solution by considering alternatives under a set of criteria or attributes. In the context of multiple attribute decision making many attributes maybe uncertain. This research considers a new way to model uncertainty in decision making environment. The proposed method is based on TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). Uncertain attributes are represented by a range of values modeled by a triangular fuzzy membership function. Simulations are run to generate a rank distribution of alternatives. The final solution overcomes the concern of uncertainty inherited in the original TOPSIS method.

KW - Fuzzy MADM

KW - Simulation

KW - TOPSIS

KW - Uncertainty

UR - https://www.scopus.com/pages/publications/85084090931

M3 - Paper

AN - SCOPUS:85084090931

SP - 759

EP - 764

T2 - 2016 Industrial and Systems Engineering Research Conference, ISERC 2016

Y2 - 21 May 2016 through 24 May 2016

ER -

A fuzzy MADM method for uncertain attributes using ranking distribution

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this