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How to derive priorities in AHP: a comparative study. (English) Zbl 1122.90367

Summary: A heated discussion has arisen over the “best” priorities derivation method. Using a Monte Carlo simulation this article compares and evaluates the solutions of four AHP ratio scaling methods: the right eigenvalue method, the left eigenvalue method, the geometric mean and the mean of normalized values. Matrices with different dimensions and degree of impurities are randomly constructed. We observe a high level of agreement between the different scaling techniques. The number of ranking contradictions increases with the dimension of the matrix and the inconsistencies. However, these contradictions affect only close priorities.

MSC:

90B50 Management decision making, including multiple objectives

References:

[1] Bana e Costa CA, Vansnick J-C (2001) A fundamental criticism to Saaty’s use of the eigenvalue procedure to derive priorities. Working Paper LSEOR 01.42. Retrieved December 2006 from http://www.lse.ac.uk/collections/operationalResearch/research/workingPapers.htm
[2] Barzilai J (1997) Deriving weights from pairwise comparison matrices. J Operat Res Soc (JORS) 48(12):1226–32 · Zbl 0895.90004
[3] Barzilai J (1998) On the decomposition of the value function. Operat Res Lett 22(4–5):159–170 · Zbl 0911.90004 · doi:10.1016/S0167-6377(98)00015-7
[4] Barzilai J (2001a) Notes on the analytic hierarchy process. In: Proceedings of the NSF Design and Manufacturing Research Conference, pp 1–6
[5] Barzilai J (2001b) Basic principles of measurement. In: Proceedings of the IEEE inter-national conference on syst man cybern pp 395–400
[6] Barzilai J (2002) Notes on measurement and decision theory. In: Proceedings of the NSF design and manufacturing research conference, pp 1–11
[7] Barzilai J, Golany B (1990) Deriving weights from pairwise comparison matrices: the additive case. Operat Rese Lett 9:407–10 · Zbl 0711.90007 · doi:10.1016/0167-6377(90)90062-A
[8] Barzilai J, Cook WD, Golany B (1987) Consistent weights for judgements matrices of the relative importance of alternatives. Operat Res Lett 6(1):131–134 · Zbl 0622.90004 · doi:10.1016/0167-6377(87)90026-5
[9] Berekoven L, Eckert W, Ellenrieder P (2001) Marktforschung. Methodische Grundlagen und praktische Anwendung, 9. Auflage, Gabler Verlag, Wiesbaden
[10] Blankmeyer E (1987) Approaches to consistency adjustments. J Optim Theory Appl 45:479–488 · Zbl 0597.90049 · doi:10.1007/BF00940197
[11] Brugha C (2000) Relative measurement and the power function. Eur J Operat Res 121(3):627–640 · Zbl 0958.91007 · doi:10.1016/S0377-2217(99)00057-0
[12] Budescu DV, Zwick R, Rapoport A (1986) A comparison of the eigenvalue method and the geometric mean procedure for ratio scaling. Appl Psychol Meas 10(1):69–78 · doi:10.1177/014662168601000106
[13] Chu ATW, Kalabra RE, Spingarn KA (1979) A comparison of two methods for determining the weights of belonging to fuzzy sets. J Optim Theory Appl 27:531–538 · Zbl 0377.94002 · doi:10.1007/BF00933438
[14] Cogger KO, Yu PL (1985) Eigenweight vectors and least distance approximation for revealed preference in pairwise weight ratios. J Optim Theory Appl 46:483–491 · Zbl 0552.90050 · doi:10.1007/BF00939153
[15] Cook WD, Kress M (1988) Deriving weights from pairwise comparison ratio matrices: an axiomatic approach. Eur J Operat Res 37:355–62 · Zbl 0652.90002 · doi:10.1016/0377-2217(88)90198-1
[16] Crawford G, Williams C (1985) A note on the analysis of subjective judgement matrices. J Math Psychol 29:387–405 · Zbl 0585.62183 · doi:10.1016/0022-2496(85)90002-1
[17] Golany B, Kress M (1993) A multicriteria evaluation of the methods for obtaining weights from ratio-scale matrices. Eur J Operat Res 69:210–202 · Zbl 0800.90007 · doi:10.1016/0377-2217(93)90165-J
[18] Golden BL, Wasil EA, Harker PT (1989) The analytic hierarchy process: applications and studies. Springer, Berlin Heidelberg New York
[19] Ishizaka A (2004) The advantages of clusters in AHP. In: The 15th Mini-Euro conference, MUDSM
[20] Ishizaka A, Lusti M (2003) An intelligent tutorial system for AHP. In: The šorić K, Hunjak T, Scitovski R (eds) Proceedings of the 9th international conference on operational research KOI 2002, pp 215–223 · Zbl 1052.90571
[21] Ishizaka A, Lusti M (2004) An expert module to improve the consistency of AHP matrices. Int Trans Operat Res 11(1):97–105 · Zbl 1057.90026 · doi:10.1111/j.1475-3995.2004.00443.x
[22] Jensen RE (1984) An alternative scaling method for priorities in hierarchical structures. J Math Psychol 28(3):317–332 · doi:10.1016/0022-2496(84)90003-8
[23] Johnson CR, Beine WB, Wang TY (1979) Right-left asymmetry in an eigenvector ranking procedure. J Math Psychol 18:61–64 · doi:10.1016/0022-2496(79)90005-1
[24] Harker PT, Vargas L (1987) The theory of ratio scale estimation: saaty’s analytic hierarchy process. Manage Sci 33(11):1383–1403 · doi:10.1287/mnsc.33.11.1383
[25] Lusti M (2002) Data warehousing und data mining, 2nd edn. Springer, Berlin Heidelberg New York
[26] Saaty ThL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281 · Zbl 0372.62084 · doi:10.1016/0022-2496(77)90033-5
[27] Saaty ThL (1980) The analytic hierarchy process. Mac Gray-Hill, New York
[28] Saaty ThL (2001a) Decision-making with the AHP: why is the Principal Eigenvector necessary? In: Proceedings of the 6th international symposium on the analytic hierarchy process (ISAHP 2001), pp 383–396
[29] Saaty ThL (2001b) The seven pillars of the analytic hierarchy process. In: Köksalan M et al. (eds) Multiple criteria decision making in the new millennium. In: Proceedings of the 15th conference, MCDM. Lect. Notes Econ. Math. Syst. vol 507, Springer, Berlin Heidelberg New York, pp 15–37
[30] Saaty ThL (2003) Decision-making with the AHP: why is the principal eigenvector necessary?. Eur J Operat Res 145:85–91 · Zbl 1012.90015 · doi:10.1016/S0377-2217(02)00227-8
[31] Saaty ThL, Vargas LG (1984a) Inconsistency and rank preservation. J Math Psychol 28:205–214 · Zbl 0557.62093 · doi:10.1016/0022-2496(84)90027-0
[32] Saaty Th.L., Vargas L.G. (1984b) Comparison of eigenvalue, logarithmic least squares and least squares methods in estimating ratios. Math Model 5:309–324 · Zbl 0584.62102 · doi:10.1016/0270-0255(84)90008-3
[33] Salo AA, Hämäläinen RP (1997), On the Measurement of Preferences in the Analytic Hierarchy Process, J Multi-Criteria Decis Anal 6:309–319
[34] Shim JP (1989) Bibliography research on the analytic hierarchy process (AHP). Socio-Econ Plan Sci 23:161–167 · doi:10.1016/0038-0121(89)90013-X
[35] Takeda E, Cooger KO, Yu PL (1987) Estimating criterion weights using eigenvectors: a comparative study. Eur J Operat Res 29:360–369 · Zbl 0618.90046 · doi:10.1016/0377-2217(87)90249-9
[36] Triantaphyllou E, Pardalos PM, Mann SH (1990) A minimization approach to membership evaluation in fuzzy sets and error analysis. J Optim Theory Appl 66(2):275–287 · Zbl 0683.90005 · doi:10.1007/BF00939539
[37] Vargas LG (1990) An overview of the analytic hierarchy process and its applications. Eur J Operat Res 48(1):2–8 · doi:10.1016/0377-2217(90)90056-H
[38] Zahedi F (1986) The analytic hierarchy process: a survey of the method and its applications. Interface 16:96–108
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