An Improved Ordered Visibility Graph Aggregation Operator for MADM

Abstract

For multi-attribute decision-making (MADM), how to aggregate data and determine attribute weight is still an open issue. Ordered visibility graph aggregation (OVGA) operator can objectively and effectively determine the weight of each attribute value in the network and solve the problem of data fusion. OVGA not only considers the attribute values of nodes in the network, but also synthesizes the influence of the distance between nodes on the weight distribution. However, when there are multiple identical attribute values in the network, the weights assigned by this method are unreasonable. This paper proposes an improved OVGA operator method based on OVGA, which redefines the distance between visual nodes. When there are multiple identical attribute values in the network, the distance formula is redefined in the form of a piecewise function, so that equivalent nodes are given the same weight. The improved method proposed in this paper not only considers the correlation between the visible nodes, but also fully considers the rationality of the weight distribution of the equivalent node support after the fusion of the entire network data. Meanwhile, through several practical application examples which including an application in produced water management, Dongping reservoir tourism resources and the academic ranking of world universities to illustrate the effectiveness and practicability of this method for MADM in complex networks.

Keywords

• Visibility graph
• aggregation operator
• the ordered weighted average operator
• multi-attribute decision making