An optimization viewpoint on evaluation-based interval-valued multi-attribute three-way decision model

排名(信息检索) 计算机科学 决策矩阵 区间(图论) 成对比较 不相交集 集合(抽象数据类型) 数据挖掘 决策分析 秩(图论) 加权和模型 机器学习 人工智能 最优决策 运筹学 数学 决策树 统计 组合数学 程序设计语言
作者
Haibo Jiang,Bao Qing Hu
出处
期刊:Information Sciences [Elsevier]
卷期号:603: 60-90 被引量:1
标识
DOI:10.1016/j.ins.2022.04.055
摘要

Multi-attribute decision-making (MADM) aims to rank alternatives based on their attributes and evaluation information, which provides decision support for decision-makers. Most existing MADM methods can only obtain ranking results, decision-makers usually need to subjectively choose priority alternatives to make a decision based on the preset evaluation level and ranking results, which does not meet the requirements of complex decision situations and uncertain information processing. A method is urgently needed, which can objectively produce classification results and automatically provide priority objects for decisions. Three-way decisions (3WDs) offer an effective research technique to solve decision-making problems under uncertainty and risk by objectively dividing a universal set into three pairwise disjoint parts: acceptance, non-commitment and rejection, and implementing the corresponding strategy for each part. Considering that it is difficult to accurately determine the exact value of attributes in some cases, interval numbers can be a useful concept to flexibly describe uncertain information and satisfy the decision-maker’s cognition. Therefore, under an interval-valued MADM environment, this paper proposes an evaluation-based interval-valued multi-attribute three-way decision (IVMA3WD) model from an optimization viewpoint. The model achieves an effective fusion of 3WDs and MADM problems, and further expands evaluation-based 3WD models on the totally ordered set . Specifically speaking, in order to depict uncertain information in decision-making, interval numbers are used to describe the evaluation information included in the multi-attribute evaluation matrix . Then, considering a general case that there are no decision attributes in the multi-attribute evaluation matrix, we determine the evaluation function by use of the idea of the interval-valued TOPSIS method. In addition, unlike the fixed-valued loss functions in the majority of 3WD models, the relative loss function in this article is not provided straightly but computed by attribute evaluation values expressed by interval numbers. Furthermore, based on an optimization viewpoint, we construct two optimization models and attempt to find the optimal solution to obtain the threshold pair. A housing market investment decision problem is utilized as an illustrative example to find out the applicability of our built IVMA3WD model. In the end, we adopt a sensitivity analysis and a comparative analysis to demonstrate our proposed model’s characteristics and advantages.
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