Generalized Scaled Prioritized Intuitionistic Fuzzy Geometric Interaction Aggregation Operators and Their Applications to the Selection of Cold Chain Logistics Enterprises

Generalized Scaled Prioritized Intuitionistic Fuzzy Geometric Interaction Aggregation Operators and Their Applications to the Selection of Cold Chain Logistics Enterprises

Shanshan Meng (College of Management and Economics, Tianjin University, Tianjin, China) and Yingdong He (College of Management and Economics, Tianjin University, Tianjin, China)
Copyright: © 2018 |Pages: 21
DOI: 10.4018/IJFSA.2018010101
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Abstract

This article contends that a generalization of scaled prioritized intuitionistic fuzzy interaction averaging operators, and develops the generalized scaled prioritized intuitionistic fuzzy geometric interaction averaging (GSPIFGIA) operator and the generalized scaled prioritized intuitionistic fuzzy weighted geometric interaction averaging (GSPIFWGIA) operator. The properties of the GSPIFGIA and GSPIFWGIA operators are investigated. Then this article proposes the new decision-making methods to the selection of cold chain logistics enterprises under an intuitionistic fuzzy environment based on these generalized information aggregation operators, and the proposed method evaluates the scaled prioritized relationships between criteria by priority labels in known and unknown situations. Finally, a numerical example is illustrated to show the new approach.
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1. Introduction

Taking account of the membership, non-membership and hesitate functions, Atanassov (1986) developed intuitionistic fuzzy sets (IFSs), which is a generalization of fuzzy sets (FSs) (Zadeh, 1965) and have got a lot of attentions from literature (Atanassov, 1994; De et al., 2000; Xu and Yager, 2006; Xu, 2007; Li, 2008, 2010b, 2011a; Beliakov et al., 2011; Xu and Xia, 2011; Chen, 2014; Cheng and Chang, 2015; He et al., 2016b, 2016c; Chen et al. 2016). Li (2010a) proposed a multi-attribute decision making method based on generalized OWA operators with intuitionistic fuzzy sets. Wan and Li (2014) developed an intuitionistic fuzzy programming method for heterogeneous multi-attribute group decision making with intuitionistic fuzzy truth degrees.

Multiple attribute decision making (MADM) has been widely used in many fields (Yager and Filev, 1999; Dymova and Sevastjinov, 2010; Li, 2011b; Chen and Zhou, 2011; Li, 2014; Rodríguez et al., 2014; Li and Liu, 2015; Liao, 2015; He et al., 2016a; Robinson, 2016; Selvachandran and John, 2017), such as management, engineering, military and economy, which is an important part of modern decision science. Rodríguez et al. (2012, 2013) proposed a group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Wei (2012) developed multiple attribute decision making based on hesitant fuzzy prioritized operators. Yager (2008) proposed prioritized aggregation operators. Yu and Xu (2013) proposed prioritized intuitionistic fuzzy aggregation operators.

In contrast to crisp properties that are either true or false, fuzzy techniques and fuzzy logic are helpful to describe imprecise (Pedrycz and Chen, 2015), thus Pedrycz and Chen (2015) pointed out that granularity helps reconcile decision making with fuzzy theories. Lorkowski and Kreinovich (2015) got membership function with granules form decision makers. Naim and Hagras (2015) embedded intuitionistic fuzzy sets in type-2 fuzzy sets and evaluate interval type-2 fuzzy sets with intuitionistic fuzzy sets. They used interval type-2 fuzzy logic to solve multi-criteria decision-making problems. Mendel (2016) estimated the interval type-2 fuzzy set model for a word by three different methods. Granular Computing has got more attentions from literature (Antonelli et al., 2016; Skowron et al., 2016).

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