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

• Main Authors: Yu-Li Lin, Lun-Hui Ho, Shu-Ling Yeh, Ting-Yu Chen
• Format: Article
• Language: English
• Published: Atlantis Press 2018-11-01
• Series: International Journal of Computational Intelligence Systems
• Subjects: Pythagorean fuzzy set; Multiple criteria decision analysis; TOPSIS; Correlation measure; Inpatient stroke rehabilitation
• Online Access: https://www.atlantis-press.com/article/125905657/view

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.