A novel method for aggregating interval multiplicative comparison matrices and its application in ranking alternatives

Article type: Research Article

Authors: Li, Jing | Zhang, Yulin^{; *}

Affiliations: School of Economics and Management, Southeast University, Nanjing, China

Correspondence: [*] Corresponding author. Yulin Zhang, School of Economics and Management, Southeast University, Nanjing, 211189 China. E-mail: zhangyl@seu.edu.cn.

Abstract: Aggregating individual interval multiplicative comparison matrices (IMCMs) and then ranking all the alternatives are very important in group decision making. However, the traditional methods may make the final result become overall average and does not always satisfy the Pareto optimal axiom. What’s more, the ranking of the alternatives may rely on parameter or the possibility degree. In order to make the aggregated interval multiplicative comparison matrix (IMCM) and the ranking more reliable, this paper develops a novel method to aggregate interval multiplicative comparison matrices (IMCMs) and then applies logarithmic least squares method (LLSM) to rank alternatives. Firstly, DMs’ interval multiplicative comparison matrices (IMCMs) are converted into two-dimensional coordinates. Next, the minimum Euclidean distance model is constructed and plant growth simulation algorithm (PGSA) is applied to find the aggregated points. Then the logarithmic least squares method (LLSM) is employed to derive the weight vectors and the ranking of alternatives can be obtained. Finally, numerical examples are provided to show the advantage and efficiency of the proposed method.

Keywords: Group decision making, interval multiplicative comparison matrices, the minimum Euclidean distance model, plant growth simulation algorithm

DOI: 10.3233/JIFS-18455

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 3, pp. 3675-3684, 2018