| | | |

## trenİstatistiksel Varyans Prosedürü Temelli Analitik Hiyerarşi Prosesi ile SıralamaRanking with Statistical Variance Procedure based Analytic Hierarchy Process

#### Halit Alper TAYALI [1] , Mehpare TİMOR [2]

##### 283 374

This study introduces an objective multicriteria ranking method based on the Analytic Hierarchy Process (AHP). Different multicriteria decision analysis methods generate different solutions for the same ranking problem because of their varying mathematical models. In AHP, decision makers construct positive comparison matrices from their preferences by using a scale of 1-9. However, even a simple ranking problem requires numerous comparison matrices while subjective judgments lead to inconsistent rankings. As a simplified version of the AHP, the Statistical Variance Procedure (SVP) based AHP (SVP-AHP) extracts the ranking of alternatives from a multicriteria dataset without referring to costly survey processes. SVP-AHP uses pairwise comparison matrices, the powerful tool of AHP, and it does not need to measure consistency. For an objective ranking of alternatives, SVP-AHP embeds vector normalization and SVP into the AHP. SVP determines criteria weights while pairwise comparison matrices for alternatives are constructed using the normalized observations. In SVP-AHP, it is sufficient to know only criteria and alternative values, unlike AHP, where the model requires decision makers’ judgments. Results of the AHP and SVP-AHP for the example in this study point out that SVP-AHP is an efficient ranking method because of its computational efficieny and objectivity.

• [1] Esen, H. Ö., 2008, “Applied Operational Research” (“Uygulamalı Yöneylem Araştırması”), (S. Tolun, Ed.), Çağlayan Kitabevi.
• [2] Roy, B., & Vanderpooten, D., 1997, “An overview on “The European school of MCDA: Emergence, basic features and current works”, European Journal of Operational Research, 99, 26–27.
• [3] Tayalı, H. A., 2016, “Statistical variance procedure based analytic hierarcy process: An application on multicriteria facility location selection”, Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/.
• [4] Ömürbek, N., & Mercan, Y., 2014, “Performance Evaluation of Sub-manufacturing Sectors Using TOPSIS and ELECTRE Methods”, Cankiri Karatekin University Journal of the Faculty of Economics and Administrative Sciences, 4(1), 237–266.
• [5] Xidonas, P., Mavrotas, G., & Psarras, J., 2009, “A multicriteria methodology for equity selection using financial analysis”, Computers and Operations Research, 36(12), 3187–3203.
• [6] Zopounidis, C., & Doumpos, M., 2002, “Multicriteria classification and sorting methods: A literature review”, European Journal of Operational Research, 138(2), 229–246.
• [7] Tsoukiàs, A., 2008, “From decision theory to decision aiding methodology”, European Journal of Operational Research, 187(1), 138–161.
• [8] Saaty, T. L., 1977, “A scaling method for priorities in hierarchical structures”, Journal of Mathematical Psychology, 15(3), 234–281.
• [9] Sipahi, S., & Timor, M., 2010, “The analytic hierarchy process and analytic network process: An overview of applications”, Management Decision, 48(5), 775–808.
• [10] Nelson, D., 2008, “The Penguin Dictionary of Mathematics”, Penguin UK.
• 11] Alonso, J. A., & Lamata, M. T., 2006, “Consistency in the analytic hierarchy process: a new approach”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14(4), 445–459.
• [12] Farkas, A., 2007, “The analysis of the principal eigenvector of pairwise comparison matrices”, Acta Polytechnica Hungarica, 4(2), 99–115.
• [13] Taha, H. A., 2007, “Operations Research: An Introduction”, Pearson Education International.
• [14] Saaty, T. L., 2003, “Decision-making with the AHP: Why is the principal eigenvector necessary”, European Journal of Operational Research, 145(1), 85–91.
• [15] Saaty, T. L., 2008, “Relative measurement and its generalization in decision making; why pairwise comparisons are central in mathematics for the measurement of intangible factors”, Revista de La Real Academia de Ciencias Exactas, Fisicas Y Naturales. Serie A. Matematicas, 102(2), 251–318.
• [16] Peláez, J. I., & Lamata, M. T., 2003, “A new measure of consistency for positive reciprocal matrices”, Computers and Mathematics with Applications, 46(12), 1839–1845.
• [17] Opricovic, S., & Tzeng, G. H., 2004, “Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS”, European Journal of Operational Research, 156(2), 445–455.
• [18] Özdağoğlu, A., 2013, “The effects of different normalization methods to decision making process in TOPSIS” (“Farklı normalizasyon yöntemlerinin TOPSIS’te karar verme sürecine etkisi”), Ege Academic Review, 13(2), 245–257.
• [19] Pavlicic, D. M., 2001, “Normalisation affects the results of MADM methods”, Yugoslav Journal of Operations Research, 11(2), 251–265.
• [20] Tervonen, T., Figueira, J., Lahdelma, R., & Dias, J., 2009, “A stochastic method for robustness analysis in sorting problems”, European Journal of Operational Research, 192(1), 236–242.
• [21] Zardari, N. H., Ahmed, K., Shirazi, S. M., & Yusop, Z. Bin., 2015, “Weighting Methods and their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management”, Springer International Publishing.
• [22] Rao, R., & Patel, B., 2010, “A subjective and objective integrated multiple attribute decision making method for material selection”, Materials & Design, 31(10), 4738–4747.
• [23] Charilas, D. E., Panagopoulos, A. D., & Ourania, M. I., 2014, “A Unified Network Selection Framework Using Principal Component Analysis and Multi Attribute Decision Making”, Wireless Personal Communications, 74(1), 147–165.
• [24] Timor, M., 2011, “Analytic Hierarchy Process” (“Analitik Hiyerarşi Prosesi”) Istanbul, Türkmen Kitapevi.
• [25] Sánchez-Lozano, J., & Teruel-Solano, J., 2013, “Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain”, Renewable and Sustainable Energy Reviews, 24, 544–556.
Subjects Engineering, Multidisciplinary Makaleler Orcid: 0000-0002-2098-6482Author: Halit Alper TAYALIInstitution: İSTANBUL ÜNİVERSİTESİ, İŞLETME FAKÜLTESİCountry: Turkey Author: Mehpare TİMORInstitution: İSTANBUL ÜNİVERSİTESİ, İŞLETME FAKÜLTESİCountry: Turkey
 Bibtex @research article { acin303064, journal = {Acta INFOLOGICA}, issn = {2602-3563}, address = {Istanbul University}, year = {2017}, volume = {1}, pages = {31 - 38}, doi = {}, title = {İstatistiksel Varyans Prosedürü Temelli Analitik Hiyerarşi Prosesi ile Sıralama}, key = {cite}, author = {TAYALI, Halit Alper and TİMOR, Mehpare} } APA TAYALI, H , TİMOR, M . (2017). İstatistiksel Varyans Prosedürü Temelli Analitik Hiyerarşi Prosesi ile Sıralama. Acta INFOLOGICA, 1 (1), 31-38. Retrieved from http://dergipark.org.tr/acin/issue/28069/303064 MLA TAYALI, H , TİMOR, M . "İstatistiksel Varyans Prosedürü Temelli Analitik Hiyerarşi Prosesi ile Sıralama". Acta INFOLOGICA 1 (2017): 31-38 Chicago TAYALI, H , TİMOR, M . "İstatistiksel Varyans Prosedürü Temelli Analitik Hiyerarşi Prosesi ile Sıralama". Acta INFOLOGICA 1 (2017): 31-38 RIS TY - JOUR T1 - İstatistiksel Varyans Prosedürü Temelli Analitik Hiyerarşi Prosesi ile Sıralama AU - Halit Alper TAYALI , Mehpare TİMOR Y1 - 2017 PY - 2017 N1 - DO - T2 - Acta INFOLOGICA JF - Journal JO - JOR SP - 31 EP - 38 VL - 1 IS - 1 SN - 2602-3563- M3 - UR - Y2 - 2019 ER - EndNote %0 Acta INFOLOGICA İstatistiksel Varyans Prosedürü Temelli Analitik Hiyerarşi Prosesi ile Sıralama %A Halit Alper TAYALI , Mehpare TİMOR %T İstatistiksel Varyans Prosedürü Temelli Analitik Hiyerarşi Prosesi ile Sıralama %D 2017 %J Acta INFOLOGICA %P 2602-3563- %V 1 %N 1 %R %U ISNAD TAYALI, Halit Alper , TİMOR, Mehpare . "İstatistiksel Varyans Prosedürü Temelli Analitik Hiyerarşi Prosesi ile Sıralama". Acta INFOLOGICA 1 / 1 (June 2017): 31-38.