Selecting Target Market by Similar Measures in Interval Intuitionistic Fuzzy Set

Abstract

The selection of the target market plays vital role in promoting the marketing strategies of companies. We presented is a method for target market selection. We introduce some novel similarity measures between intuitionistic fuzzy sets and the novel similarity measures between interval-valued intuitionistic fuzzy sets. They are constructed by combining exponential and other functions. Finally, we introduce a multi-criteria decision making model to select target market by using the novel similarity measure of interval intuitionistic fuzzy sets.

TOPSIS model based on entropy and similarity measure for market segment selection and evaluation

“…In this section, we apply the proposed model for the evaluation and selection market segment in four segmentations A 1 , A 2 , A 3 , A 4 . For demonstration of the proposed model, the data were gained by Thao and Duong (2019) with eight benefit criteria for assessment market segments, including identify profitability (C 1 ), the growth of the market (C 2 ), size of market (C 3 ), likely customer satisfaction (C 4 ), sales volume (C 5 ), likelihood of sustainable differential advantage (C 6 ), development opportunities (C 7 ) and the differentiation of product (C 8 ). The model is implemented as follows:…”

“…Ghorabaee et al (2017) select the market segments using the Porter's five force of competition. Thao and Duong (2019) used the aspects of profitability, market size, attractiveness as criteria for market segments. Hence, the selection and evaluation of market segment can be viewed as a multiple criteria decision-making (MCDM) problem in which the vague and imprecise information of experts or decision makers should not be neglected.…”

Novel Similarity Measures, Entropy of Intuitionistic Fuzzy Sets and their Application in Software Quality Evaluation

“…The rankings of the other options still remain. Meanwhile, the proposed method along with the remaining methods (Song et al, 2015;Song et al, 2019;Thao and Duong, 2019; all give stable ranking results. IFSQM 2 (proposed) Ye, 2016Zhou, 2016Song et al, 2019Song et al, 2015Quynh et al, 2020Thao and Duong, 2019 Comparing results of software ranking P1 P2 P3 P4 P5 Ye, 2016 Zhou, 2016Song et al, 2019Song et al, 2015Quynh et al, 2020Thao and Duong, 2019 Comparing results of software raking with weight 2 P1 P2 P3 P4 P5…”

Novel similarity measures, entropy of intuitionistic fuzzy sets and their application in software quality evaluation

“…Three projects 1 P , 4 P and 5 P have their quality being Medium, simultaneously ranked 3 rd , 4 th and 5 th , respectively. To further demonstrate the feasibility of the proposed method, we compare the ranking results with some existing ranking methods in (Song et al, 2015;Ye,2016;Zhou, 2016;Thao and Duong, 2019;Song et al, 2019;Quynh et al, 2020;. The comparison results are shown in Figure 1.…”

“…Later, Atanassov and Gargov introduced interval-valued IFSs (IVIFSs), in which membership functions and non-membership functions are subintervals of a unit interval [0, 1] (Atanassov and Gargov, 1989). Similar to fuzzy sets, IFSs have wide applications in processing uncertain data for various purposes, such as decision-making, medical diagnoses, and agriculture (Szmidt and Kacprzyk, 1996;Xu, 2010;Papakostas et al, 2013;Shidpour et al, 2013;Bharati and Singh, 2014;Li and Zeng, 2015;Liu et al, 2016;Xuan Thao, 2018;Thao and Duong, 2019;Joshi, 2020;Garg and Kumar, 2020;Xue & Deng, 2020;. Along with distance and correlation measures, similarity measures of IFSs have been studied and widely used in many fields, such as decision-making, machine learning, and pattern recognition (Li and Cheng, 2002;Szmidt Kacprzyk, 2004;Xu, 2007;Ye, 2011;Hwang et al, 2012;Park et al, 2013;Rajarajeswari andUma, 2013 Shi andYe, 2013;Tian, 2013;Song et al, 2015;J.…”