Skip to main content
Log in

Influence of aggregation and measurement scale on ranking a compromise alternative in AHP

  • Theoretical Paper
  • Published:
Journal of the Operational Research Society

Abstract

Analytic Hierarchy Process (AHP) is one of the most popular multi-attribute decision aid methods. However, within AHP, there are several competing preference measurement scales and aggregation techniques. In this paper, we compare these possibilities using a decision problem with an inherent trade-off between two criteria. A decision-maker has to choose among three alternatives: two extremes and one compromise. Six different measurement scales described previously in the literature and the new proposed logarithmic scale are considered for applying the additive and the multiplicative aggregation techniques. The results are compared with the standard consumer choice theory. We find that with the geometric and power scales a compromise is never selected when aggregation is additive and rarely when aggregation is multiplicative, while the logarithmic scale used with the multiplicative aggregation most often selects the compromise that is desirable by consumer choice theory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

Notes

  1. AHP only partially requires this hypothesis.

References

  • Ahn BS and Choi SH (2008). ERP system selection using a simulation-based AHP approach: A case of Korean home shopping company . J Opl Res Soc 59(3): 322–330.

    Article  Google Scholar 

  • Akarte MM, Surendra NV, Ravi B and Rangaraj N (2001). Web based casting supplier evaluation using analytical hierarchy process . J Opl Res Soc 52: 511–522.

    Article  Google Scholar 

  • Bañuelas R and Antony J (2007). Application of stochastic analytic hierarchy process within a domestic appliance manufacturer . J Opl Res Soc 58(1): 29–38.

    Article  Google Scholar 

  • Barzilai J (1997). Deriving weights from pairwise comparison matrices . J Opl Res Soc 48(12): 1226–1232.

    Article  Google Scholar 

  • Barzilai J and Golany B (1994). AHP rank reversal, normalization and aggregation rules . Inf Sys and Opl Res 32(2): 57–64.

    Google Scholar 

  • Belton V and Gear T (1983). On a short-coming of Saaty's method of analytic hierarchies . Omega 11: 228–230.

    Article  Google Scholar 

  • Dyer JS (1990). Remarks on the analytic hierarchy process . Mngt Sci 36(3): 249–258.

    Article  Google Scholar 

  • Forman EH and Gass SI (2001). The analytic hierarchy process—An exposition . Opns Res 49(4): 469–486.

    Article  Google Scholar 

  • Fukuyama H and Weber WL (2002). Evaluating public school district performance via DEA gain functions . J Opl Res Soc 53(9): 992–1003.

    Article  Google Scholar 

  • Golden BL, Wasil EA and Harker PT (1989). The Analytic Hierarchy Process: Applications and Studies . Springer-Verlag: Heidelberg.

    Book  Google Scholar 

  • Harker PT and Vargas LG (1987). The theory of ratio scale estimation: Saaty's analytic hierarchy process . Mngt Sci 33(11): 1383–1403.

    Article  Google Scholar 

  • Harker PT and Vargas LG (1990). Reply to ‘Remarks on the Analytic Hierarchy Process' . Mngt Sci 36(3): 269–273.

    Article  Google Scholar 

  • Ho W (2008). Integrated analytic hierarchy process and its applications—A literature review . Eur J Opl Res 186(1): 211–228.

    Article  Google Scholar 

  • Holder RD (1990). Some comment on the analytic hierarchy process . J Opl Res Soc 41(11): 1073–1076.

    Article  Google Scholar 

  • Holder RD (1991). Response to Holder's comments on the analytic hierarchy process: Response to the response . J Opl Res Soc 42(10): 914–918.

    Article  Google Scholar 

  • Ishizaka A (2004a). The advantages of clusters and pivots in AHP. Proceeding 15th Mini-Euro Conference MUDSM (CD-Rom) ISBN 972-9044-52-X.

  • Ishizaka A (2004b). Développement d'un système tutorial intelligent pour dériver des priorités dans l'AHP. PhD Thesis, University of Basle. Dissertation.de: Berlin..

  • Ishizaka A and Lusti M (2006). How to derive priorities in AHP: A comparative study . Cent Eur J Opns Res 14(4): 387–400.

    Article  Google Scholar 

  • Lee CW and Kwak NK (1999). Information resource planning for a health-care system using an AHP-based goal programming method . J Opl Res Soc 50(12): 1191–1198.

    Article  Google Scholar 

  • Leskinen P and Kangas J (2005). Rank reversals in multi-criteria decision analysis with statistical modelling of ratio-scale pairwise comparisons . J Opl Res Soc 56(12): 855–861.

    Article  Google Scholar 

  • Leung LC, Lam KC and Cao D (2006). Implementing the balanced scorecard using the analytic hierarchy process & the analytic network process . J Opl Res Soc 57(6): 682–691.

    Article  Google Scholar 

  • Li X, Beullens P, Jones D and Tamiz M (2009). An integrated queuing and multi-objective bed allocation model with application to a hospital in China . J Opl Res Soc 60: 330–338.

    Article  Google Scholar 

  • Liberatore MJ and Nydick RL (2008). The analytic hierarchy process in medical and health care decision making: A literature review . Eur J Opl Res 189(1): 194–207.

    Article  Google Scholar 

  • Lootsma FA (1989). Conflict resolution via pairwise comparison of concessions . Eur J Opl Res 40(1): 109–116.

    Article  Google Scholar 

  • Lootsma FA, Mensch TCA and Vos FA (1990). Multi-criteria analysis and budget reallocation in long-term research planning . Eur J Opl Res 47(3): 293–305.

    Article  Google Scholar 

  • Lootsma FA (1993). Scale sensitivity in the multiplicative AHP and SMART . J Multi-criteria Decis Anal 2(2): 87–110.

    Article  Google Scholar 

  • Ma D and Zheng X (1991). 9/9-9/1 Scale method of AHP. 2nd Proceeding Int. Symposium on AHP, Vol. 1, University of Pittsburgh; Pittsburgh, PA, pp 197–202.

  • Mingers J, Liu W and Weng W (2009). Using SSM to structure the identification of inputs and outputs in DEA . J Opl Res Soc 60: 168–179.

    Article  Google Scholar 

  • Pöyhönen MA, Hämäläinen RP and Salo AA (1997). An experiment on the numerical modelling of verbal ratio statements . J Multi-criteria Decis Anal 6(1): 1–10.

    Article  Google Scholar 

  • Saaty TL (1977). A scaling method for priorities in hierarchical structures . J Math Psychol 15(3): 234–281.

    Article  Google Scholar 

  • Saaty TL (1980). The Analytic Hierarchy Process . McGraw-Hill: New York.

    Google Scholar 

  • Saaty TL (1990). An exposition of the AHP in reply to the paper ‘Remarks on the Analytic Hierarchy Process' . Mngt Sci 36(3): 259–268.

    Article  Google Scholar 

  • Saaty TL (1991). Response to Holder's comments on the analytic hierarchy process . J Opl Res Soc 42(10): 909–929.

    Article  Google Scholar 

  • Saaty TL and Vargas LG (1984). Comparison of eigenvalue, logarithmic least squares and least squares methods in estimating ratios . Math Modelling 5(5): 309–324.

    Article  Google Scholar 

  • Salo AA and Hämäläinen RP (1997). On the measurement of preferences in the analytic hierarchy process . J Multi-criteria Decis Anal 6(6): 309–319.

    Article  Google Scholar 

  • Sha DY and Che ZH (2006). Supply chain network design: Partner selection and production/distribution planning using a systematic model . J Opl Res Soc 57(1): 52–62.

    Article  Google Scholar 

  • Stam A and Duarte Silva P (2003). On multiplicative priority rating methods for AHP . Eur J Opl Res 145(1): 92–108.

    Article  Google Scholar 

  • Tavana M (2006). A priority assessment multi-criteria decision model for human spaceflight mission planning at NASA . J Opl Res Soc 57(10): 1197–1215.

    Article  Google Scholar 

  • Triantaphyllou E (2001). Two new cases of rank reversals when the AHP and some of its additive variants are used that do not occur with the multiplicative AHP . J Multi-criteria Decis Anal 10(1): 11–25.

    Article  Google Scholar 

  • Triantaphyllou E and Baig K (2005). The impact of aggregating benefit and cost criteria in four MCDA methods . IEEE T Eng Mngt 52(2): 213–226.

    Article  Google Scholar 

  • Vaidya O and Kumar S (2006). Analytic hierarchy process: An overview of applications . Eur J Opl Res 169(1): 1–29.

    Article  Google Scholar 

  • Vargas LG (1990). An overview of the analytic hierarchy process and its applications . Eur J Opl Res 48(1): 2–8.

    Article  Google Scholar 

  • Vargas LG (1997). Comments on Barzilai and Lootsma: Why the multiplicative AHP is invalid: A practical counterexample . J Multi-criteria Decis Anal 6(4): 169–170.

    Article  Google Scholar 

  • Wheeler S (2006). An analysis of combined arms teaming for the Australian defence force . J Opl Res Soc 57(11): 1279–1288.

    Article  Google Scholar 

  • Winkler R (1990). Decision modeling and rational choice: AHP and utility theory . Mngt Sci 36(3): 247–248.

    Article  Google Scholar 

  • Yeo G-T, Song D-W, Dinwoodie J, Roe M (2009). Weighting the competitiveness factors for container ports under conflicting interests. J Opl Res Soc, advance online publication, doi: 10.1057/jors.2009.88.

  • Zahedi F (1986). The analytic hierarchy process: A survey of the method and its applications . Interface 16(4): 96–108.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ishizaka, A., Balkenborg, D. & Kaplan, T. Influence of aggregation and measurement scale on ranking a compromise alternative in AHP. J Oper Res Soc 62, 700–710 (2011). https://doi.org/10.1057/jors.2010.23

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2010.23

Keywords

Navigation