An improved Taguchi multi-criteria decision-making method based on the hesitant fuzzy correlation coefficient and its ap
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ORIGINAL RESEARCH
An improved Taguchi multi‑criteria decision‑making method based on the hesitant fuzzy correlation coefficient and its application in quality evaluation Miao Tang1,2 · Tie‑Dan Wang2 · Ding‑Hong Peng2 Received: 19 January 2020 / Accepted: 16 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This article is aimed at exploring an improved Taguchi SNR technique to settle the problem of multi-criteria decision-making (MCDM) in a hesitant fuzzy situation. Firstly, a novel correlation coefficient is defined under message from hesitant fuzzy sets and a weighted correlation coefficient proposed to deal with uncertainty. On that basis, several hesitant fuzzy correlation SNR measures are proposed in terms of the use of the weighted correlation coefficient. These measures cannot only avoid information distortion and omission, but are more sensitive when small datasets are used to reduce computational burden and reflect the correlation conflict in the process of information processing. Then, a hesitant fuzzy Taguchi method is proposed based on a novel weighted correlation coefficient which follows the principle of compromise such that the difference between the selected alternatives and the average solution is minimised to determine the optimal alternatives under the framework of the Taguchi method. The established method considers association conflict and provides a flexible and effective method for solving MCDM problems in a hesitant fuzzy environment. The implementation and the rationality process of the proposed method are demonstrated by an example pertinent to quality evaluation of tourism city development and compared outputs therefrom with four similar methods. Keywords Multi-criteria decision-making (MCDM) · Hesitant fuzzy set (HFS) · Correlation coefficient · Hesitant fuzzy Taguchi signal-to-noise ratio (SNR) measure · Tourism quality evaluation
1 Introduction Multi-criteria decision-making (MCDM) as an important branch of modern analytical decision theory has been widely utilised in management, economics and other areas (Mardani et al. 2015; Liu et al. 2015; Chen and Xu 2019). So far, the MCDM method has helped decision-makers to choose the best alternative or arrange the alternatives according to different criteria. Among them, MCDM based on a distance measure is the method most suited to human cognition and logic, commonly used MCDM methods as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), * Miao Tang [email protected] 1
Glorious Sun School of Business and Management, Donghua University, Shanghai, China
School of Management and Economics, Kunming University of Science and Technology, Kunming 650093, China
2
Best Worst Method (BWM), Vlsekriterijumska Optimizacija IKompromisno Resenje (VIKOR), and so on. On the MCDM method, researchers have proposed various distance measures to solve uncertain decision-making problems, such as ordered weighted distance (Zeng 2011; Liao et al. 2014), Hamming distance (Wei 2014; Liang et al
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