Comparison of Analytic Hierarchy Process and Dominance-Based Rough Set Approach as Multi-Criteria Decision Aid Methods for the Selection of Investment Projects
Bryan Boudreau-Trudel, Kazimierz Zaras
DOI: 10.4236/ajibm.2012.21002   PDF    HTML     5,455 Downloads   11,345 Views   Citations


This investigation compares two multi-criteria analysis methods, Analytic Hierarchy Process (AHP) and Dominance- based Rough Set Approach (DRSA), applied to the ranking of ten investment projects based on evaluation of the overall risk associated with each. AHP requires decision makers to evaluate the various elements of risk by paired comparison in terms of their impact on the element above them in the hierarchy. Each investment project is then rated in terms of each risk to produce a weighted summation used for ranking purposes. DRSA produces a ranking based on a set of decision rules that are derived from evaluation of a reduced number of reference projects well known to the decision makers. For this purpose, four reference projects were chosen from the ten. The results show that the two methods gave very similar final rankings of the ten projects. The advantage of DRSA is that the projects are evaluated using a reduced number of attributes without explicit knowledge of their impact in the hierarchy, thus eliminating a lengthy and tedious process for the decision makers.

Share and Cite:

B. Boudreau-Trudel and K. Zaras, "Comparison of Analytic Hierarchy Process and Dominance-Based Rough Set Approach as Multi-Criteria Decision Aid Methods for the Selection of Investment Projects," American Journal of Industrial and Business Management, Vol. 2 No. 1, 2012, pp. 7-12. doi: 10.4236/ajibm.2012.21002.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Observatory of the Abitibi-Témiscaminigue, “Number of Establishments and Jobs According to the Size and the MRC, Abitibi-Témiscamingue,” 2011.
[2] A. Shpak and D. Zaporojan, “Working Out R & D Programs via Multicriteria Analysis,” Computer Science Journal of Moldova, Vol. 4, No. 2(11), 1996, pp. 239- 259.
[3] M. A. Coffin and B. W. Taylor, “Multiple Criteria R & D Project Selection and Scheduling Using Fuzzy Logic,” Computer & Operations Research, Vol. 23, No. 3, 1996, pp. 207-221. doi:10.1016/0305-0548(96)81768-0
[4] G. Lockett and M. Stratford, “Ranking Research Projects, Experiments with Two Methods,” Omega, Vol. 15, No. 5, 1987, pp. 395-400. doi:10.1016/0305-0483(87)90040-5
[5] P. Regan and S. Holtzman, “R & D Decision Advisor: An Interactive Approach to Normative Decision System Model Construction,” European Journal of Operational Research, Vol. 84, No. 1, 1995, pp. 116-133. doi:10.1016/0377-2217(94)00321-3
[6] F. Ghasemzadeh, N. P. Archer and P. Iyogun, “A Zero- One Model for Project Portfolio Selection and Scheduling,” Journal of Operational Research Society, Vol. 50, No. 7, 1999, pp. 745-755.
[7] T. L. Saaty, “The Analytic Hierarchy Process,” McGraw- Hill, New York, 1980.
[8] A. H. I. Lee, H. H. Chen and H. Y. Kang, “Multi-Criteria Decision Making on Strategic Selection of Wind Farms,” Renewable Energy, Vol. 34, No. 1, 2009, pp. 120-126. doi:10.1016/j.renene.2008.04.013
[9] P. K. Dey, “Integrated Project Evaluation and Selection Using Multiple-Attribute Decision-Making Technique,” International Journal Production Economics, Vol. 103, No. 1, 2006, pp. 90-103. doi:10.1016/j.ijpe.2004.11.018
[10] M. Yurdakul and Y. Tansel, “AHP Approach in the Credit Evaluation of the Manufacturing Firms in Turkey,” International Journal of Production Economics, Vol. 88, No. 3, 2004, pp. 269-289. doi:10.1016/S0925-5273(03)00189-0
[11] T. L. Saaty, “Fundamentals of the Analytic Network Process-Multiple Networks with Benefits, Opportunities, Costs and Risks,” Journal of Systems Science and Systems Engineering, Vol. 13, No. 3, 2004, pp. 348-379. doi:10.1007/s11518-006-0171-1
[12] Z. Pawlak, “Rough Sets,” International Journal of Parallel Programming, Vol. 11, No. 5, 1982, pp. 341-356.
[13] T. L. Saaty, “Relative Measurement and Its Generalization in Decision Making—Why Pairwise Comparisons Are Central in Mathematics for the Measurement of Intangible Factor, the Analytic Hierarchy/Network Process,” Review of the Royal Spanish Academy of Sciences, Series A, Mathematics, Vol. 102, No. 2, 2008, pp. 251-318. doi:10.1007/BF03191825
[14] Z. Xu, “On Consistency of the Weighted Geometric Mean Complex Judgement Matrix in AHP,” European Journal of Operational Research, Vol. 126, No. 3, 2000, pp. 683- 687. doi:10.1016/S0377-2217(99)00082-X
[15] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publishing, Dordrecht, 1991.
[16] S. Greco, B. Matarazzo and R. S?owiński, “Rough Sets Theory for Multi-Criteria Decision Analysis,” European Journal of Operational Research, Vol. 129, No. 1, 2001, pp. 1-47. doi:10.1016/S0377-2217(00)00167-3
[17] K. Zaras, “Rough Approximation of a Preference Relation by a Multi-Attribute Dominance for Deterministic, Stochastic and Fuzzy Decision Problems,” European Journal of Operational Research, Vol. 159, No. 1, 2004, pp. 196-206. doi:10.1016/S0377-2217(03)00391-6
[18] J. Thibault, D. Taylor, C. Yanofsky, R. Lanouette, C. Fonteix and K. Zaras, “Multicriteria Optimization of a High Yield Pulping Process with Rough Sets,” Chemical Engineering Science, Vol. 58, No. 1, 2003, pp. 203-213. doi:10.1016/S0009-2509(02)00470-0
[19] H. Kane, K. Zaras and M. Nowak, “Using the Dominance-Based Rough Set Approach in Production Planning and Control,” Journal of Global Business Administration, Vol. 1, No. 1, 2009, pp. 23-37.
[20] J. P. Brans, P. Vincke and B. Mareshal, “How to Select and How to Rank Projects: The PROMETHEE Method,” European Journal of Operational Research, Vol. 24, No. 2, 1986, pp. 228-238. doi:10.1016/0377-2217(86)90044-5

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.