面向多准则供应商选择的Shadowed AHP方法
Shadowed AHP for multi-criteria supplier selection
AI总结：
针对多粒度语言信息下的AHP决策问题，提出基于阴影模糊数的Shadowed AHP方法，统一多类型不确定偏好值并设计新排序方法，可有效解决多准则供应商选择问题。
摘要：
多准则决策（MCDM）的众多技术已被应用于各类商业领域，其中层次分析法（AHP）是广受认可的方法之一。在AHP问题中，各类不确定数常被用来表示偏好值；针对多粒度语言信息场景，已有多种方法被提出以处理这类AHP问题。本文提出一种利用阴影模糊数（SFNs）解决该问题的新方法，这类数的特点是能够近似不同类型的模糊数并保留其不确定性特性。新提出的Shadowed AHP方法用于处理由多类型不确定数表示的偏好值，该方法将多粒度偏好值转换为阴影模糊数的统一模型并利用其特性，同时引入了一种新的排序方法对聚合偏好结果进行排序。该新方法被应用于解决一个采用多粒度信息的供应商选择问题，其特性对决策应用具有重要意义。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
