Skip to main content

A Pairwise Comparison Matrix Framework for Large-Scale Decision Making


Abstract A Pairwise Comparison Matrix (PCM) is used to compute for relative priorities of criteria or alternatives and are integral components of widely applied decision making tools: the Analytic Hierarchy Process (AHP) and its generalized form, the Analytic Network Process (ANP). However, a PCM suffers from several issues limiting its application to large-scale decision problems, specifically: (1) to the curse of dimensionality, that is, a large number of pairwise comparisons need to be elicited from a decision maker (DM), (2) inconsistent and (3) imprecise preferences maybe obtained due to the limited cognitive power of DMs. This dissertation proposes a PCM Framework for Large-Scale Decisions to address these limitations in three phases as follow... (more)
Created Date 2013
Contributor Jalao, Eugene Rex Lazaro (Author) / Shunk, Dan L (Advisor) / Wu, Teresa (Advisor) / Askin, Ronald G (Committee member) / Goul, Kenneth M (Committee member) / Arizona State University (Publisher)
Subject Industrial engineering / Computer science / Management / AHP / Binary Integer Programming / Decomposition / Inconsistency / Non Linear Programming / Pairwise Comparison Matrix
Type Doctoral Dissertation
Extent 130 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Industrial Engineering 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


  Full Text
1.3 MB application/pdf
Download Count: 3370

Description Dissertation/Thesis