An integrated fuzzy AHP/DEA approach for performance evaluation of territorial units in Turkey


Due to the differences between regions and sub-regions in the countries, some problems come out especially in economic and social life. The issue of differences of regions has been widely implemented to evaluate the economic performance of Turkey in many disciplines. The objective of this paper is to evaluate the efficiency of 26 sub-regions of NUTS-2 classification using integration Fuzzy Analytic Hierarchy Process (FAHP) with Data Envelopment Analysis (DEA). The integrated FAHP/DEA method comprises two stages. In the first stage, linguistic terms are used to determine the decision makers’ opinion and are converted to quantitative forms by using FAHP methods. Subsequently, in the second stage, DEA method is applied to obtain relative efficiency of sub-regions in Turkey. The integrated FAHP/DEA method is illustrated with a real case study.

Keyword : Fuzzy Analytic Hierarchy Process, Data Envelopment Analysis, NUTS-2 classification

How to Cite
Çalik, A., Yapici Pehlivan, N., & Kahraman, C. (2018). An integrated fuzzy AHP/DEA approach for performance evaluation of territorial units in Turkey. Technological and Economic Development of Economy, 24(4), 1280-1302.
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Jun 29, 2018
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Abid, F.; Bahloul, S. 2011. Selected MENA countries’ attractiveness to G7 investors, Economic Modelling 28: 2197–2207.

Akadiri, P. O.; Olomolaiye, P. O.; Chinyio, E. A. 2013. Multi-criteria evaluation model for the selection of sustainable materials for building projects, Automation in Construction 30: 113–125.

Azadeh, A.; Ghaderi, S. F.; Izadbakhsh, H. 2008. Integration of DEA and AHP with computer simulation for railway system improvement and optimization, Applied Mathematics and Computation 195: 775–785.

Azadeh, A.; Ghaderi, S. F.; Mirjalili, M.; Moghaddam, M. 2011. Integration of analytic hierarchy process and data envelopment analysis for assessment and optimization of personnel productivity in a large industrial bank, Expert Systems with Applications 38: 5212–5225.

Baležentis, A.; Baležentis, T. 2011. Assessing the efficiency of Lithuanian transport sector by applying the methods of multimoora and data envelopment analysis, Transport 26(3): 263–270.

Banker, R. D.; Charnes, A.; Cooper, W. W. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science 30: 1078–1092.

Baysal, M. E.; Kaya, İ.; Kahraman, C.; Sarucan, A.; Engin, O. 2015. A two phased fuzzy methodology for selection among municipal projects, Technological and Economic Development of Economy 21(3): 405–422.

Brauers, W. K. M.; Kildienė, S.; Zavadskas, E. K.; Kaklauskas, A. 2013. The construction sector in twenty European countries during the recession 2008–2009 – country ranking by MULTIMOORA, International Journal of Strategic Property Management 17: 58–78.

Buckley, J. J. 1985. Fuzzy hierarchical analysis, Fuzzy Sets and Systems 17: 233–247.

Büyüközkan, G.; Feyzioğlu, O.; Nebol, E. 2008. Selection of the strategic alliance partner in logistics value chain, International Journal of Production Economics 113: 148–158.

Calabrese, A.; Costa, R.; Menichini, T. 2013. Using Fuzzy AHP to manage Intellectual Capital assets: an application to the ICT service industry, Expert Systems with Applications 40(9): 3747–3755.

Çalık, A. 2012. Measurement of investment efficiency of regions in Turkey via fuzzy analytical hierarchy process/data envelopment analysis in Turkish: Bulanık Analitik Hiyerarşi Süreci/Veri Zarflama Analizi ile Türkiye’de Bölgelerin Yatirim Etkinliğinin Ölçülmesi. MSc, Selçuk University.

Camanho, A. S.; Dyson, R. G. 2005. Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments, European Journal of Operational Research 161: 432–446.

Celik, E.; Gul, M.; Aydin, N.; Gumus, A. T.; Guneri, A. F. 2015. A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets, Knowledge-Based Systems 85: 329–341.

Chang, D.-Y. 1996. Applications of the extent analysis method on fuzzy AHP, European Journal of Operational Research 95: 649–655.

Charnes, A.; Cooper, W. W.; Rhodes, E. 1978. Measuring the efficiency of decision making units, European Journal of Operational Research 2: 429–444.

Che, Z. H.; Wang, H. S.; Chuang, C.-L. 2010. A fuzzy AHP and DEA approach for making bank loan decisions for small and medium enterprises in Taiwan, Expert Systems with Applications 37: 7189–7199.

Chen, X.; Skully, M.; Brown, K. 2005. Banking efficiency in China: application of DEA to pre- and post-deregulation eras: 1993–2000, China Economic Review 16: 229–245.

CIA. 2010. The world factbook available [online], [cited 20 February 2013]. Available from Internet:

Cooper, W. W.; Seiford, L. M.; Tone, K. 2000. Data envelopment analysis: a comprehensive text with models, applications, references, and DEA-Solver software. Kluwer Academic.

Cooper, W. W.; Seiford, L. M.; Zhu, J. 2004. Handbook on data envelopment analysis. Springer.

Deveci, M.; Demirel, N. Ç.; John, R.; Özcan, E. 2015. Fuzzy multi-criteria decision making for carbon dioxide geological storage in Turkey, Journal of Natural Gas Science and Engineering 27(2): 692–705.

Do, Q. H.; Chen, J.-F. 2014. A hybrid fuzzy AHP-DEA approach for assessing university performance, WSEAS Transactions on Business & Economic 11: 386–397.

Durán, O. 2011. Computer-aided maintenance management systems selection based on a fuzzy AHP approach, Advances in Engineering Software 42: 821–829.

Düzakın, E.; Düzakın, H. 2007. Measuring the performance of manufacturing firms with super slacks based model of data envelopment analysis: an application of 500 major industrial enterprises in Turkey, European Journal of Operational Research 182: 1412–1432.

Ecer, F. 2014. A hybrid banking websites quality evaluation model using AHP and COPRAS-G: a Turkey case, Technological and Economic Development of Economy 20(4): 758–782.

Erol, Ö.; Kılkış, B. 2012. An energy source policy assessment using analytical hierarchy process, Energy Conversion and Management 63: 245–252.

Ertay, T.; Ruan, D.; Tuzkaya, U. R. 2006. Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems, Information Sciences 176: 237–262.

Gao, L.; Hailu, A. 2012. Ranking management strategies with complex outcomes: an AHP-fuzzy evaluation of recreational fishing using an integrated agent-based model of a coral reef ecosystem, Environmental Modelling & Software 31: 3–18.

Giokas, D. I.; Pentzaropoulos, G. C. 2008. Efficiency ranking of the OECD member states in the area of telecommunications: a composite AHP/DEA study, Telecommunications Policy 32: 672–685.

Govindan, K.; Khodaverdi, R.; Jafarian, A. 2013. A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach, Journal of Cleaner Production 47: 345–354.

Ho, W. 2008. Integrated analytic hierarchy process and its applications – a literature review, European Journal of Operational Research 186: 211–228.

Johnes, J. 2006. Measuring teaching efficiency in higher education: an application of data envelopment analysis to economics graduates from UK Universities 1993, European Journal of Operational Research 174: 443–456.

Kahraman, C. 2008. Fuzzy multi-criteria decision making: theory and applications with recent developments. Springer US.

Kahraman, C.; Ertay, T.; Büyüközkan, G. 2006. A fuzzy optimization model for QFD planning process using analytic network approach, European Journal of Operational Research 171: 390–411.

Kahraman, C.; Onar, S. C.; Oztaysi, B. 2015. Fuzzy multicriteria decision-making: a literature review, International Journal of Computational Intelligence Systems 8: 637–666.

Kahraman, C.; Suder, A.; Cebi, S. 2013. Fuzzy multi-criteria and multi-experts evaluation of government investments in higher education: the case of Turkey, Technological and Economic Development of Economy 19: 549–569.

Kaya, T.; Kahraman, C. 2011. Fuzzy multiple criteria forestry decision making based on an integrated VIKOR and AHP approach, Expert Systems with Applications 38: 7326–7333.

Köksal, C. D.; Aksu, A. A. 2007. Efficiency evaluation of A-group travel agencies with data envelopment analysis (DEA): a case study in the Antalya region, Turkey, Tourism Management 28: 830–834.

Korpela, J.; Lehmusvaara, A.; Nisonen, J. 2007. Warehouse operator selection by combining AHP and DEA methodologies, International Journal of Production Economics 108: 135–142.

Kou, M.; Chen, K.; Wang, S.; Shao, Y. 2016. Measuring efficiencies of multi-period and multi-division systems associated with DEA: an application to OECD countries’ national innovation systems, Expert Systems with Applications 46: 494–510.

Kumar, A.; Shankar, R.; Debnath, R. M. 2015. Analyzing customer preference and measuring relative efficiency in telecom sector: a hybrid fuzzy AHP/DEA study, Telematics and Informatics 32: 447–462.

Lee, S. K.; Mogi, G.; Hui, K. S. 2013. A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: in the case of energy technologies against high oil prices, Renewable and Sustainable Energy Reviews 21: 347–355.

Lee, S. K.; Mogi, G.; Lee, S. K.; Hui, K. S.; Kim, J. W. 2010. Econometric analysis of the R&D performance in the national hydrogen energy technology development for measuring relative efficiency: the fuzzy AHP/DEA integrated model approach, International Journal of Hydrogen Energy 35: 2236–2246.

Lee, S. K.; Mogi, G.; Li, Z.; Hui, K. S.; Lee, S. K.; Hui, K. N.; Park, S. Y.; Ha, Y. J.; Kim, J. W. 2011. Measuring the relative efficiency of hydrogen energy technologies for implementing the hydrogen economy: an integrated fuzzy AHP/DEA approach, International Journal of Hydrogen Energy 36: 12655–12663.

Lin, C.-T.; Lee, C.; Chen, W.-Y. 2009. Using fuzzy analytic hierarchy process to evaluate service performance of a travel intermediary, The Service Industries Journal 29: 281–296.

Lin, M.-I.; Lee, Y.-D.; Ho, T.-N. 2011. Applying integrated DEA/AHP to evaluate the economic performance of local governments in China, European Journal of Operational Research 209: 129–140.

Liou, J. J. H.; Tzeng, G.-H. 2012. Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”, Technological and Economic Development of Economy 18(4): 672–695.

Liu, C.-H.; Tzeng, G.-H.; Lee, M.-H. 2012. Improving tourism policy implementation – the use of hybrid MCDM models, Tourism Management 33: 413–426.

Mardani, A.; Jusoh, A.; Md Nor, K.; Khalifah, Z.; Zakwan, N.; Valipour, A. 2015. Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014, Economic Research-Ekonomska Istraživanja 28: 516–571.

Meng, F. Y.; Zhou, P.; Zhou, D. Q.; Bai, Y. 2014. Inefficiency and congestion assessment of mix energy consumption in 16 APEC countries by using DEA window analysis, Energy Procedia 61: 2518–2523.

Mikhailov, L. 2002. Fuzzy analytical approach to partnership selection in formation of virtual enterprises, Omega 30: 393–401.

Mikhailov, L. 2003. Deriving priorities from fuzzy pairwise comparison judgements, Fuzzy Sets and Systems 134: 365–385.

Najafi, A.; Karimpour, M. H.; Ghaderi, M. 2014. Application of fuzzy AHP method to IOCG prospectivity mapping: a case study in Taherabad prospecting area, eastern Iran, International Journal of Applied Earth Observation and Geoinformation 33: 142–154.

Nazarko, J.; Šaparauskas, J. 2014. Application of DEA method in efficiency evaluation of public higher education institutions, Technological and Economic Development of Economy 20(1): 25–44.

Pan, N.-F. 2008. Fuzzy AHP approach for selecting the suitable bridge construction method, Automation in Construction 17: 958–965.

Ramanathan, R. 2006a. Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process, Computers & Operations Research 33: 1289–1307.

Ramanathan, R. 2006b. Evaluating the comparative performance of countries of the Middle East and North Africa: a DEA application, Socio-Economic Planning Sciences 40: 156–167.

Saaty, T. L. 1980. The analytic hierarchy process: planning, priority setting, resource allocation. McGrawHill.

Saen, R. F.; Memariani, A.; Lotfi, F. H. 2005. Determining relative efficiency of slightly non-homogeneous decision making units by data envelopment analysis: a case study in IROST, Applied Mathematics and Computation 165: 313–328.

Sarica, K.; Or, I. 2007. Efficiency assessment of Turkish power plants using data envelopment analysis, Energy 32: 1484–1499.

Sevkli, M.; Lenny Koh, S. C.; Zaim, S.; Demirbag, M.; Tatoglu, E. 2007. An application of data envelopment analytic hierarchy process for supplier selection: a case study of BEKO in Turkey, International Journal of Production Research 45: 1973–2003.

Shafer, S. M.; Byrd, T. A. 2000. A framework for measuring the efficiency of organizational investments in information technology using data envelopment analysis, Omega 28: 125–141.

Shaw, K.; Shankar, R.; Yadav, S. S.; Thakur, L. S. 2012. Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain, Expert Systems with Applications 39: 8182–8192.

Sinuany-Stern, Z.; Mehrez, A.; Hadad, Y. 2000. An AHP/DEA methodology for ranking decision making units, International Transactions in Operational Research 7: 109–124.

Sözen, A.; Alp, İ.; Özdemir, A. 2010. Assessment of operational and environmental performance of the thermal power plants in Turkey by using data envelopment analysis, Energy Policy 38: 6194–6203.

Sun, J. H.; Hu, J.; Yan, J. M.; Liu, Z.; Shi, Y. R. 2012. Regional environmental performance evaluation: a case of western regions in China, Energy Procedia 16(Part A): 377–382.

Tansel Iç, Y.; Yurdakul, M.; Dengiz, B. 2013. Development of a decision support system for robot selection, Robotics and Computer-Integrated Manufacturing 29: 142–157.

Taylan, O.; Bafail, A. O.; Abdulaal, R. M. S.; Kabli, M. R. 2014. Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies, Applied Soft Computing 17: 105–116.

Thokala, P.; Duenas, A. 2012. Multiple criteria decision analysis for health technology assessment, Value in Health 15: 1172–1181.

Tseng, Y.-F.; Lee, T.-Z. 2009. Comparing appropriate decision support of human resource practices on organizational performance with DEA/AHP model, Expert Systems with Applications 36: 6548–6558.

Tzeng, G. H.; Huang, J. J. 2011. Multiple attribute decision making: methods and applications. Taylor & Francis.

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

Van Laarhoven, P. J. M.; Pedrycz, W. 1983. A fuzzy extension of Saaty’s priority theory, Fuzzy Sets and Systems 11: 229–241.

Vlontzos, G.; Niavis, S.; Manos, B. 2014. A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries, Renewable and Sustainable Energy Reviews 40: 91–96.

Wang, K.; Yu, S.; Zhang, W. 2013. China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation, Mathematical and Computer Modelling 58: 1117–1127.

Wang, Y.-M.; Liu, J.; Elhag, T. M. S. 2008. An integrated AHP–DEA methodology for bridge risk assessment, Computers & Industrial Engineering 54: 513–525.

WIKIPEDIA. 2014. Economy of Turkey [online], [cited 15 February 2014]. Available from Internet:

WIKIPEDIA. 2015. Nomenclature of territorial units for statistics [online], [cited 01 July 2014]. Available from Internet:

Yang, G.; Ahlgren, P.; Yang, L.; Rousseau, R.; Ding, J. 2016. Using multi-level frontiers in DEA models to grade countries/territories, Journal of Informetrics 10: 238–253.

Yang, T.; Kuo, C. 2003. A hierarchical AHP/DEA methodology for the facilities layout design problem, European Journal of Operational Research 147: 128–136.

Zavadskas, E. K.; Skibniewski, M. J.; Antucheviciene, J. 2014a. Performance analysis of Civil Engineering Journals based on the Web of Science® database, Archives of Civil and Mechanical Engineering 14: 519–527.

Zavadskas, E. K.; Turskis, Z. 2011. Multiple criteria decision making (MCDM) methods in economics: an overview, Technological and Economic Development of Economy 17: 397–427.

Zavadskas, E. K.; Turskis, Z.; Kildienė, S. 2014b. State of art surveys of overviews on MCDM/MADM methods, Technological and Economic Development of Economy 20(1): 165–179.