International Journal of Computational Intelligence Systems

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Volume 12, Issue 1, November 2018, Pages 410 - 425

A Pythagorean Fuzzy TOPSIS Method Based on Novel Correlation Measures and Its Application to Multiple Criteria Decision Analysis of Inpatient Stroke Rehabilitation

Authors
Yu-Li Lin1, Lun-Hui Ho2, Shu-Ling Yeh3, Ting-Yu Chen4, *
1Department of Nursing, Linkou Chang Gung Memorial Hospital, Department of Nursing, Chang Gung University of Science and Technology, No.5, Fuxing St., Guishan District, Taoyuan City 333, Taiwan
2Department of Nursing, Linkou Chang Gung Memorial Hospital, Department of Nursing, Chang Gung University of Science and Technology, No.5, Fuxing St., Guishan District, Taoyuan City 333, Taiwan
3Department of Nursing, Linkou Chang Gung Memorial Hospital, Department of Nursing, Chang Gung University of Science and Technology, No.5, Fuxing St., Guishan District, Taoyuan City 333, Taiwan
4Graduate Institute of Business and Management, Chang Gung University, Department of Industrial and Business Management, Chang Gung University, Department of Nursing, Linkou Chang Gung Memorial Hospital, No. 259, Wenhua 1st Rd., Guishan District, Taoyuan City 33302, Taiwan
*

Corresponding author. Email: tychen@mail.cgu.edu.tw

Received 29 May 2018, Revised 15 October 2018, Accepted 24 October 2018, Available Online 1 November 2018.
DOI
10.2991/ijcis.2018.125905657How to use a DOI?
Keywords
Pythagorean fuzzy set; Multiple criteria decision analysis; TOPSIS; Correlation measure; Inpatient stroke rehabilitation
Abstract

The complex nature of the realistic decision-making process requires the use of Pythagorean fuzzy (PF) sets which have been shown to be a highly promising tool capable of solving highly vague and imprecise problems. Multiple criteria decision analysis (MCDA) methods within the PF environment are very attractive approaches for today’s intricate decision environments. With this study, an effective compromise model named as the PF technique for order preference by similarity to ideal solutions (TOPSIS) is proposed based on some novel PF correlation-based concepts to overcome the complexities and ambiguities involved in real-life decision situations. In contrast to the existing distance-based definitions, this paper develops new closeness indices based on an extended concept of PF correlations. This paper employs the proposed PF correlation coefficients to construct two types of closeness measures. A comprehensive concept of PF correlation-based closeness indices can then be established to balance the consequences yielded by the two closeness measures. Based on these useful concepts, an effective PF TOPSIS method is proposed to address MCDA problems involving PF information and determine the ultimate priority orders among competing alternatives. Feasibility and practicability of the developed approach are illustrated by a medical decision-making problem of inpatient stroke rehabilitation. Finally, the proposed methodology is compared with other current methods to further explain its effectiveness.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 1
Pages
410 - 425
Publication Date
2018/11/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.2018.125905657How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Yu-Li Lin
AU  - Lun-Hui Ho
AU  - Shu-Ling Yeh
AU  - Ting-Yu Chen
PY  - 2018
DA  - 2018/11/01
TI  - A Pythagorean Fuzzy TOPSIS Method Based on Novel Correlation Measures and Its Application to Multiple Criteria Decision Analysis of Inpatient Stroke Rehabilitation
JO  - International Journal of Computational Intelligence Systems
SP  - 410
EP  - 425
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2018.125905657
DO  - 10.2991/ijcis.2018.125905657
ID  - Lin2018
ER  -