Quantifying uncertainty in multicriteria concept selection methods |
| |
Authors: | Michael J Scott |
| |
Affiliation: | (1) Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, 842 W. Taylor Street, Chicago, IL 60607, USA |
| |
Abstract: | Decision support for concept selection in engineering design ranges from simple pairwise comparison techniques to methods
that consider multiple criteria. The Analytic Hierarchy Process, or AHP, is an example of a multicriteria selection tool with
wide-spread industry application. It is recognized by responsible practitioners that AHP, like other decision support methods,
is best used not as an optimization tool, but as a means of clarifying individual or group attitudes; the numerical rankings
that are its output are not definitive. This paper offers a means to quantify how differently two alternatives must be ranked
by AHP to instill confidence that one is truly better than the other, a question that is in practice always answered using
intuition. The quantification of uncertainty in AHP relies on the extension of concepts from statistical hypothesis testing.
The procedure is not stochastic in the same way that physical measurement is, so probability distributions are created over
relevant parameters. The quantified uncertainty depends, as in all statistical analysis, upon the assumed distributions. The
uncertainty in AHP is quantified from two distinct points of view. The first makes the assumption that AHP is structurally
correct but subject to measurement “error” in the pairwise comparisons, while the second quantifies the uncertainties introduced
by AHP’s failure to consider different level of compensation in trade-offs among criteria. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|