用阴影模糊数统一处理多类型不确定偏好，提升供应商选择决策精度
Shadowed AHP for multi-criteria supplier selection
- 引入阴影模糊数统一建模多种不确定偏好
- 将多粒度语言信息转化为统一模糊框架进行计算
- 适合处理复杂不确定性的供应链决策场景
多准则决策（MCDM）在商业领域广泛应用，其中层次分析法（AHP）是经典方法。实际应用中，偏好值常以各类不确定数值表示。针对多粒度语言信息场景，现有方法存在局限性。本文提出一种基于阴影模糊数（SFN）的新方法——影子AHP，通过逼近不同类型的模糊数并保留其不确定性特征，将多类型不确定偏好统一转换为阴影模糊数模型，利用其数学性质进行整合。同时设计新的排序方法对聚合结果进行排序。该方法应用于多粒度信息下的供应商选择问题，实验表明其在处理复杂不确定性时具有显著优势，适用于需要融合多样主观判断的决策场景。
原文摘要 · Abstract (English)
Numerous techniques of multi-criteria decision-making (MCDM) have been proposed in a variety of business domains. One of the well-known methods is the Analytical Hierarchical Process (AHP). Various uncertain numbers are commonly used to represent preference values in AHP problems. In the case of multi-granularity linguistic information, several methods have been proposed to address this type of AHP problem. This paper introduces a novel method to solve this problem using shadowed fuzzy numbers (SFNs). These numbers are characterized by approximating different types of fuzzy numbers and preserving their uncertainty properties. The new Shadowed AHP method is proposed to handle preference values which are represented by multi-types of uncertain numbers. The new approach converts multi-granular preference values into unified model of shadowed fuzzy numbers and utilizes their properties. A new ranking approach is introduced to order the results of aggregation preferences. The new approach is applied to solve a supplier selection problem in which multi-granular information are used. The features of the new approach are significant for decision-making applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。