A new fuzzy multi-attribute group decision-making method based on TOPSIS and optimization models
This is an incremental improvement for decision-makers in fields requiring fuzzy logic, such as management or engineering.
The paper tackles the problem of multi-attribute group decision-making under uncertainty by proposing a new method that combines TOPSIS and optimization models to determine expert and attribute weights, resulting in a complete algorithm validated through a real case study.
In this paper, a new method based on TOPSIS and optimization models is proposed for multi-attribute group decision-making in the environment of interval-valued intuitionistic fuzzy sets.Firstly, by minimizing the sum of differences between individual evaluations and the overallconsistent evaluations of all experts, a new optimization model is established for determining expert weights. Secondly, based on TOPSIS method, the improved closeness index for evaluating each alternative is obtained. Finally, the attribute weight is determined by establishing an optimization model with the goal of maximizing the closeness of each alternative, and it is brought into the closeness index so that the alternatives can be ranked. Combining all these together, the complete fuzzy multi-attribute group decision-making algorithm is formulated, which can give full play to the advantages of subjective and objective weighting methods. In the end, the feasibility and effectiveness of the provided method are verified by a real case study.