Multi-Attribute Group Decision Making Method for Preference Conflicting with Heterogeneous Information

Multi-Attribute Group Decision Making Method for Preference Conflicting with Heterogeneous Information

Kai-Rong Liang
Copyright: © 2018 |Pages: 14
DOI: 10.4018/IJFSA.2018100101
(Individual Articles)
No Current Special Offers


The aim of this article is to propose a multi-objective decision-making method for researching and solving multi-attribute heterogeneous group decision-making problems. This is in the case that the characters of the decision information and decision makers' preferences are heterogeneous, and the weight information is incomplete. In this method, the multi-objective decision-making model, which considers the alternatives decision relative closeness and the preference of heterogeneous degree of decision makers in the objective function, is put forward. In addition, this article uses the minimax method to derive the multi-objective decision-making model and obtain the attribute weights and decision makers weights, and then the optimal scheme is established. Finally, an illustrative example shows the effectiveness of the proposed method.
Article Preview

1. Introduction

Group decision making (GDM) is a joint action, alternatives of a decision-making group. Aiming at decision making group’s problem in common, different preference information of individual in this group was given. Then different individual preference is aggregated into group preference, according to some preference standard. In the end, the selections are ranked or alternated by group preference. Group decision theory becomes more and more important in various of research field as its extensive practice background in the different areas, for instance, society, economy, military, management and engineering science. Undoubtedly, to adopt proper method is the key of the present study, executing group decision. The information of group decision is given by decision makers (DMs), which may be represented in the form of multiple formats, such as real numbers (RNs), interval numbers (Ins), fuzzy numbers (Atanassov, 1986), intuitionistic fuzzy numbers (IFNs) (Atanassov and Gargov, 1989), interval-valued intuitionistic fuzzy numbers (IIFNs) (Zhang and Liu, 2010) and linguistic values (LVs) (Liu, Liu, and Zhang, 2014). Heterogeneous multi-attribute group decision making (HMAGDM) problems (Li et al., 2010; Espinilla et al., 2012; Zhang et al., 2015; Chen et al., 2015) are the group decision making (GDM) problems with multiple conflict attributes whose values given by decision makers (DMs) may be made up of qualitative and quantitative information. The HMAGDM problems have been successfully applied to the fields of information processing (Li and Wang, 2013), outsourcing (Wang and Dong, 2015), sustainable project selection (Herrera, et al., 1996), human resources performance evaluation (Dong and Zhang, 2014), supply chain coordination (Yue and Jia, 2015), and so forth. The key to tackling such problems is how to fuse various types of attribute values (Herrera, et al., 1996). So far, many useful and valuable methods have been developed to study the fusion process of heterogeneous information, which can be roughly classified into three main categories (Li et al., 2010; Yue and Jia, 2015): (1) the indirect approach (Chiclana et al., 2002; Herrera et al., 2005; Dong et al., 2009), in which the heterogeneous decision information given by DMs is converted into an uniformed information by transformation methods; (2) the optimization-based approach (Wan and Li, 2013, 2014; Li and Wan, 2013, 2014), in which the heterogeneous information is integrated by constructing different multiple objective optimization models; (3) direct approach. In the direct approach, the collective decision information is obtained by aggregating standardized individual decision information. Then the heterogeneous information is transformed into preference information by an acceptable consensus degree (Herrera et al., 1996; Dong & Zhang, 2014; Yue & Jia, 2015).

Complete Article List

Search this Journal:
Volume 13: 1 Issue (2024)
Volume 12: 1 Issue (2023)
Volume 11: 4 Issues (2022)
Volume 10: 4 Issues (2021)
Volume 9: 4 Issues (2020)
Volume 8: 4 Issues (2019)
Volume 7: 4 Issues (2018)
Volume 6: 4 Issues (2017)
Volume 5: 4 Issues (2016)
Volume 4: 4 Issues (2015)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing