A sensitivity analysis algorithm for the constant sum pair-wise comparison judgments in hierarchical decision models

Hongyi Chen, Jingrui Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Every hierarchical decision modeling process starts with quantifying the contributions of decision elements through pair-wise comparisons. As subjective values, the pair-wise comparison judgments are seldom provided at a 100% confidence level and are subject to variations. To increase the model's validity and ensure requisite decision making, it is important to know how sensitive the model result is to these inputs. In this paper, a sensitivity analysis algorithm is developed to test a hierarchical decision model's robustness to the pair-wise comparison judgment inputs acquire from the constant sum method. It defines the allowable region of perturbation(s) induced to a judgment matrix at any level of a decision hierarchy to keep the current ranking of decision alternatives unchanged. An example will be presented to demonstrate the application of this algorithm in technology selection.

Original languageEnglish (US)
Title of host publicationPICMET'11 - Portland International Center for Management of Engineering and Technology, Proceedings - Technology Management in the Energy-Smart World
StatePublished - 2011
EventPortland International Center for Management of Engineering and Technology - Technology Management in the Energy-Smart World, PICMET'11 - Portland, OR, United States
Duration: Jul 31 2011Aug 4 2011

Publication series

NamePICMET: Portland International Center for Management of Engineering and Technology, Proceedings

Other

OtherPortland International Center for Management of Engineering and Technology - Technology Management in the Energy-Smart World, PICMET'11
Country/TerritoryUnited States
CityPortland, OR
Period7/31/118/4/11

Fingerprint

Dive into the research topics of 'A sensitivity analysis algorithm for the constant sum pair-wise comparison judgments in hierarchical decision models'. Together they form a unique fingerprint.

Cite this