Interval orders, semiorders and ordered groups

Pouzet, Maurice; Zaguia, Imed

doi:10.1016/j.jmp.2019.01.002

Cited by 2 publications

(1 citation statement)

References 32 publications

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Order By: Relevance

“…Zapata et al [43] extended the ordering of Allen's algebra to intervals in an arbitrary partially ordered set. Pouzet and Zaguia [44] described ordered groups such that the ordering is a semiorder, and they introduced threshold groups generalizing totally ordered groups. Ghosh et al [45] introduced and analyzed the concepts of fixed ordering structure and variable ordering structure on intervals.…”

Section: Introductionmentioning

confidence: 99%

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

Ruan,

Gong,

Chen

2023

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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.

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Section: Introductionmentioning

confidence: 99%