Probabilistic Interval Ordering Prioritized Averaging Operator and Its Application in Bank Investment Decision Making

• Published in: Axioms Vol. 12; no. 11; p. 1007
• Main Authors: Ruan, Chuanyang, Gong, Shicheng, Chen, Xiangjing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2023
• Subjects: Algorithms; Banks (Finance); Commercial banks; Decision making; Fuzzy sets; information aggregation; interval ordering; Methods; multi-attribute decision making; prioritized aggregation operator; probabilistic interval ordering sets; Project evaluation; Small & medium sized enterprises-SME; Statistical analysis; Supply chains
• ISSN: 2075-1680; 2075-1680
• DOI: 10.3390/axioms12111007

Probabilistic interval ordering, as a helpful tool for expressing positive and negative information, can effectively address multi-attribute decision-making (MADM) problems in reality. However, when dealing with a significant number of decision-makers and decision attributes, the priority relationships between different attributes and their relative importance are often neglected, resulting in deviations in decision outcomes. Therefore, this paper combines probability interval ordering, the prioritized aggregation (PA) operator, and the Gauss–Legendre algorithm to address the MADM problem with prioritized attributes. First, considering the significance of interval priority ordering and the distribution characteristics of attribute priority, the paper introduces probability interval ordering elements that incorporate attribute priority, and it proposes the probabilistic interval ordering prioritized averaging (PIOPA) operator. Then, the probabilistic interval ordering Gauss–Legendre prioritized averaging operator (PIOGPA) is defined based on the Gauss–Legendre algorithm, and various excellent properties of this operator are explored. This operator considers the priority relationships between attributes and their importance level, making it more capable of handling uncertainty. Finally, a new MADM method is constructed based on the PIOGPA operator using probability intervals and employs the arithmetic–geometric mean (AGM) algorithm to compute the weight of each attribute. The feasibility and soundness of the proposed method are confirmed through a numerical example and comparative analysis. The MADM method introduced in this paper assigns higher weights to higher-priority attributes to establish fixed attribute weights, and it reduces the impact of other attributes on decision-making results. It also utilizes the Gauss AGM algorithm to streamline the computational complexity and enhance the decision-making effectiveness.