模糊多准则分析法在净现值排序中的应用

E. Haven
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引用次数: 13

摘要

净现值(NPV)方法通常被用作对项目进行排序的工具,它使用的是总排序方法。本文采用了De Baets和Van De Walle(1994,1995)开发的一种新方法,我们称之为“模糊多标准分析”(FMCA)。该方法的主要目的是将总顺序弱化为一个模糊的准顺序,在这个准顺序中,方案之间可能存在不可比性。尽管其他方法,如Roy的排名法也处理了不可比较性问题,但FMCA和排名法在方法上存在重要差异。我们首先考虑将FMCA应用于一般NPV排序问题。我们推导了一些可能有用的属性,然后描述了一个实际例子的应用,其中三个项目产生相同的NPV结果。不同的阿尔法切割意味着不同的脆准顺序。FMCA的使用表明,在这个示例案例中,尽管项目具有相同的npv,但仍然有可能进行排名。版权所有©1998 John Wiley & Sons, Ltd
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The fuzzy multicriteria analysis method: an application on NPV ranking
The Net Present Value (NPV) approach is often used as a tool to rank projects and it uses a total order approach. This paper applies a new method developed by De Baets and Van de Walle (1994, 1995) and which we will call ‘fuzzy multicriteria analysis’ (FMCA). The main objective of this new method is to weaken the total order into a fuzzy quasi-order in which incomparability between alternatives is possible. Although other methods such as Roy’s outranking method also deal with the issue of incomparability, there are important methodological differences between FMCA and outranking. We consider first an application of FMCA to a general NPV ranking problem. Some properties are derived which may be useful and we then describe an application to a practical example in which three projects yield the same NPV outcome. Different alpha-cuts imply different crisp quasi-orders. The use of FMCA shows that, in this sample case, though the projects have equal NPVs, a ranking may still be possible. Copyright © 1998 John Wiley & Sons, Ltd.
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