Analytic Hierarchy Process and Expert Choice: Benefits and limitations
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Analytic Hierarchy Process and Expert Choice: Benefits and limitations Alessio Ishizaka* and Ashraf Labib Portsmouth Business School, University of Portsmouth, Richmond Building, Portland Street, Portsmouth PO1 3DE, UK. E-mails: [email protected]; [email protected] *Corresponding author.
Abstract
This article describes the original Analytic Hierarchy Process (AHP) as it is implemented in the software package Expert Choice. We demonstrate its application through a practical example. In particular, we discuss problem modelling, pairwise comparisons, judgement scales, derivation methods, consistency indices, synthesis of the weights and sensitivity analysis. Finally, the limitations of the original AHP along with the new proposed development are explained. OR Insight (2009) 22, 201–220. doi:10.1057/ori.2009.10
Keywords: AHP; decision making; review
Introduction The Analytic Hierarchy Process (AHP) is a multi-criteria decision making (MCDM) method that helps the decision-maker facing a complex problem with multiple conflicting and subjective criteria (for example location or investment selection, projects ranking and so forth). Several papers have compiled the AHP success stories in very different fields (Zahedi, 1986; Golden et al, 1989; Shim, 1989; Vargas, 1990; Saaty and Forman, 1992; Forman and Gass, 2001; Kumar and Vaidya, 2006; Omkarprasad and Sushil, 2006; Ho, 2008; Liberatore and Nydick, 2008). The oldest reference we have found dates from 1972 (Saaty, 1972). After this, a paper in the Journal of Mathematical Psychology (Saaty, 1977) precisely described the method. The vast majority of the applications still use AHP as described in this first publication and are unaware of & 2009 Operational Reasearch Society Ltd 0953-5543 OR Insight www.palgrave-journals.com/ori/
Vol. 22, 4, 201–220
Ishizaka and Labib
successive developments. This fact is probably owing to the leading software supporting AHP, namely, Expert Choice (http://www.expertchoice.com/), which still incorporates AHP as it was described in its first publication. In this article, we describe AHP through Expert Choice and provide a sketch of the major directions in methodological developments (as opposed to a discussion of applications) and the further research in this important field.
The Original AHP Method Like several other MCDM methods such as ELECTRE, MacBeth, SMART, PROMETHEE, UTA and so on (Belton and Stewart, 2002; Figueira et al, 2005), AHP is based on four steps: problem modelling, weights valuation, weights aggregation and sensitivity analysis. In the next sections, we will review these four steps used by AHP and its evolutions based on a simple problem: the selection of a car to buy.
Problem modelling As with all decision-making processes, the facilitator will sit a long time with the decision-maker(s) to structure the problem, which can be divided into three parts: goal (buy a car), criteria (initial cost, maintenance cost, prestige, quality and its sub-criteria) and alternatives (Fiat Uno, Nissan Maxima 4 Doors, Me
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