group decision
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2022 ◽  
Vol 191 ◽  
pp. 116266
Mehdi Divsalar ◽  
Marzieh Ahmadi ◽  
Elnaz Ebrahimi ◽  
Alessio Ishizaka

2022 ◽  
Vol 175 ◽  
pp. 121391
Rosa M. Rodríguez ◽  
Álvaro Labella ◽  
Pedro Nuñez-Cacho ◽  
Valentin Molina-Moreno ◽  
Luis Martínez

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 136
Zhiwei Gong ◽  
Jian Lin ◽  
Ling Weng

Most linguistic-based approaches to multi-attribute group decision making (MAGDM) use symmetric, uniformly distributed sets of additive linguistic terms to express the opinions of decision makers. However, in reality, there are also some problems that require the use of asymmetric, uneven, i.e., non-equilibrium, multiplicative linguistic term sets to express the evaluation. The purpose of this paper is to propose a new approach to MAGDM under multiplicative linguistic information. The aggregation of linguistic data is an important component in MAGDM. To solve this problem, we define a chi-square for measuring the difference between multiplicative linguistic term sets. Furthermore, the linguistic generalized weighted logarithm multiple averaging (LGWLMA) operator and linguistic generalized ordered weighted logarithm multiple averaging (LGOWLMA) operator are proposed based on chi-square deviation. On the basis of the proposed two operators, we develop a novel approach to GDM with multiplicative linguistic term sets. Finally, the evaluation of transport logistics enterprises is developed to illustrate the validity and practicality of the proposed approach.

2022 ◽  
Xuejiao Zhang ◽  
Yu Yang

Abstract Enterprises have been faced with the problem of how to optimize resource allocation in an uncertain environment by the expanding of manufacturing informatization. In the process of cloud manufacturing matching, group decision making organizations may provide uncertain preference information. However, preference information at various points have led to differing impacts of the final matching decision. it is necessary to study the dynamic two-sided matching. In this paper, the dynamic two-sided matching problem under the multi-form preference information was studied. Primarily, the problem of two-sided matching is described, then through group decision-making and uncertain preference information, an ordinal vector matrix is constructed. Afterwards, the comprehensive satisfaction matrix is calculated by using dynamic time-series weight and matching competition degree. Further, by introducing stable matching constraints, a multi-objective optimization model considering the satisfaction, fairness and stability of matching is constructed. Then the optimal matching result is obtained by solving the model. In addition, the presented method was verified through a case of cloud manufacturing. At the end, advantages of the presented model were demonstrated by comparison. Research results of this paper enrich the theoretical research of two-sided matching and provide an effective solution for cloud manufacturing matching in uncertain environments.

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