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21. Linguistic hesitant fuzzy interactive multi-attribute group decision making for enterprise resource planning selection.

Author

Wan, Shu-Ping, Zeng, Chun-yan, Dong, Jiu-ying, and Hu, Si-shi

Subjects
GROUP decision making, ENTERPRISE resource planning, AGGREGATION operators, FUZZY sets
Abstract

Enterprise resource planning (ERP) system selection involves multiple evaluation attributes with interaction. It can be attributed to a type of interactive multi-attribute group decision making (MAGDM). Linguistic hesitant fuzzy sets (LHFSs) are powerful tools to represent the uncertainty, hesitancy, and inconsistency of decision makers' (DMs') preference. This article proposes two new methods for interactive MAGDM with LHFSs based on comprehensive cloud (CC) power geometric (PG) aggregation operators. First, the CC of LHFS is defined and a distance measure between two CCs is offered. Considering the interaction among the aggregated LHFSs, we develop some CC PG aggregation operators of LHFSs. An uncertainty degree of LHFS is defined. Then, an approach is developed to derive the weights of DMs. An approach is proposed to derive the comprehensive attribute weights. Thus two new methods are presented for interactive MAGDM with LHFSs. An ERP selection example is provided to validate the proposed methods. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Max-Min and Min-Max Gray Association Degree-Based Method for Multiattribute Decision Making

Author

Dong Jiu-Ying and Wan Shu-Ping

Subjects
multiattribute decision making, gray association analysis, technique for order preference by similarity to ideal solution (topsis), Science, Electronic computers. Computer science, QA75.5-76.95
Abstract

A new method of multiattribute decision making is proposed based on the max-min and min-max gray association degree. The gray association coefficients between the alternative, positive ideal solution and negative ideal solution are defined. Here, we construct a bi-objective programming model that maximizes the minimum gray association degree and minimizes the maximum gray association degree simultaneously. By using the linear weighted summation method, the bi-objective programming model is transformed into a linear programming model. Thus, we can obtain the weight vector of attributes by solving the linear programming model. The ranking order of alternatives is generated according to the relative closeness. A numerical example is examined to demonstrate the applicability and implementation process of the decision method proposed in this paper.

Published
2015
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