THEORIES ON COEFFICIENT OF VARIATION SCALES TRIANGLE AND NORMALIZATION OF DIFFERENT VARIABLES: A NEW MODEL IN DEVELOPMENT OF MULTIPLE CRITERIA DECISION ANALYSIS

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Published Jul 31, 2019
Saeed Alitaneh

Abstract

This paper is an attempt to solve various problems by the two factors of mean and standard deviation (SD) of variables, introducing coefficient of variation (CV) of data as the best option for prioritization, scaling, pairwise comparison and normalization of quantitative and qualitative variables. An algorithm was built based on a coefficient of variation scales triangle (CVST) consisting of natural numbers with coefficients of binomial expansion for each line, followed by new and independent grading and scaling. In view of the existing factors, the theory provides higher generalization and maximum reliability rates in comparison to other methods for multiple-criteria decision analysis (MCDA). On the other hand, in the normalization process of different variables (i.e. de-scalarization), a precise and infinite model was presented based on coefficient of variation scale triangle (multipurpose triangle), in such a way that decision makers could work with the software in a more convenient and precise manner. Therefore, the proposed theories may be considered as a new approach and definition in the valuation and progress of MCDA.

How to Cite

Alitaneh, S. (2019). THEORIES ON COEFFICIENT OF VARIATION SCALES TRIANGLE AND NORMALIZATION OF DIFFERENT VARIABLES: A NEW MODEL IN DEVELOPMENT OF MULTIPLE CRITERIA DECISION ANALYSIS. International Journal of the Analytic Hierarchy Process, 11(2), 283–295. https://doi.org/10.13033/ijahp.v11i2.565

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Keywords

theories; coefficient of variation scales triangle (CVST); multiple-criteria decision analysis (MCDA), theories, coefficient of variation scales triangle (CVST), multiple criteria decision analysis (MCDA)

References
Alitaneh, S., Naeeimipour, H., & Golsheykhi, M. (2015). A new idea in animal science: The first application of the Analytical Hierarchy Process (AHP) model in selection of the best dairy cow. Iranian Journal of Applied Animal Science, 5(3), 553–559.

Dodd, F., Donegan, H. (1995). Comparison of prioritization techniques using inter hierarchy mappings. Journal of the Operational Research Society, 46(4), 492–498. Doi: https://doi.org/10.1057/jors.1995.67

Everitt, B. (1998). The dictionary of statistics. Cambridge: Cambridge University Press.

Forman, E., Gass, S. (2001). The analytic hierarchy process, an exposition.
Operations Research, 49(4), 469–486. Doi: https://doi.org/10.1287/opre.49.4.469.11231

Golden, B., Wasil, E., & Harker, P. (1989).The Analytic Hierarchy Process: Applications and Studies. Heidelberg, Germany: Springer-Verlag.

Harker, P., Vargas, L. (1987). The theory of ratio scale estimation: Saaty’s analytic hierarchy process. Management Science, 33(11), 1383–1403. Doi: https://doi.org/10.1287/mnsc.33.11.1383

Harker, P., Vargas, L. (1990). Reply to remarks on the Analytic Hierarchy Process. Management Science, 36(3), 269–273. Doi: https://doi.org/10.1287/mnsc.36.3.269

Ho, W. (2008). Integrated analytic hierarchy process and its applications, a literature review. European Journal of Operational Research, 186(1), 211–228. Doi: https://doi.org/10.1016/j.ejor.2007.01.004

Ishizaka, A., Balkenborg, D., & Kaplan, T. (2006). Influence of aggregation and
preference scale on ranking a compromise alternative in AHP. Proceedings of the
Multidisciplinary Workshop on Advances in Preference Handling, Riva Del Garda, 51–57. Doi: https://doi.org/10.2139/ssrn.1527520

Kumar, S., Vaidya, O. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29.

Liberatore, M., Nydick, R. (2008).The analytic hierarchy process in medical and health care decision making: A literature review. European Journal of Operational
Research, 189(1), 194–207. Doi: https://doi.org/10.1016/j.ejor.2007.05.001

Lootsma, F. (1989). Conflict resolution via pairwise comparison of concessions. European Journal of Operational Research, 40(1), 109–116. Doi: https://doi.org/10.1016/0377-2217(89)90278-6

Ma, D., Zheng, X. (1991). 9/9–9/1 Scale method of AHP. 2nd International
Symposium on AHP, Pittsburgh, 197–202.

Omkarprasad, V., Sushil, K. (2006). Analytic hierarchy process: An overview of
applications. European Journal of Operational Research, 169(1), 1–29.

Saaty, T. (1972). An Eigenvalue allocation model for prioritization and planning. University of Pennsylvania: Energy Management and Policy Center. Working Paper.

Saaty, T.L. (1980). The Analytic Hierarchy Process. Pittsburgh: RWS Publications.

Saaty, T.L. (1999). Fundamentals of the Analytic Network Process. ISAHP Japan, 12–14.

Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234–281. Doi: https://doi.org/10.1016/0022-2496(77)90033-5

Saaty, T., Forman, E. (1992). The hierarchy: A dictionary of hierarchies. Pittsburgh, PA: RWS Publications.

Salo, A., Hamalainen, R. (1997). On the measurement of preference in the analytic hierarchy process. Journal of Multi-Criteria Decision Analysis, 6(6), 309–319. Doi: https://doi.org/10.1002/(sici)1099-1360(199711)6:6%3C309::aid-mcda163%3E3.3.co;2-u

Shim, J. (1989). Bibliography research on the analytic hierarchy process (AHP). Socio-Economic Planning Sciences, 23(3), 161–167. Doi: https://doi.org/10.1016/0038-0121(89)90013-x

Vargas, L. (1990). An overview of the analytic hierarchy process and its applications.
European Journal of Operational Research, 48(1), 2–8.

Zahedi, F. (1986).The analytic hierarchy process: A survey of the method and its
applications. Interface, 16(4), 96–108. Doi: https://doi.org/10.1287/inte.16.4.96
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