Multicriteria Decision Analysis for Development Ranking of Balkan Countries
Authors: C. Ardil
In this research, the Balkan peninsula countries' developmental integration into European Union represents the strategic economic development objectives of the countries in the region. In order to objectively analyze the level of economic development competition of Balkan Peninsula countries, the mathematical compromise programming technique of multicriteria evaluation is used in this ranking problem. The primary aim of this research is to explain the role and significance of the multicriteria method evaluation using a real example of compromise solutions. Using the mathematical compromise programming technique, twelve countries of the Balkan Peninsula are economically evaluated and mutually compared. The economic development evaluation of the countries is performed according to five evaluation criteria forming the basis for economic development evaluation. The multiattribute model is solved using the mathematical compromise programming technique for producing different Pareto solutions. The results obtained by the multicriteria evaluation gives the possibility of identification and evaluation of the most eminent economic development indicators for each country separately. Finally, in this way, the proposed method has proved to be a successful model for the evaluation of the Balkan peninsula countries' economic development competition.
Keywords: Balkan peninsula countries, standard deviation, multicriteria decision making, mathematical compromise programming, multicriteria decision making, multicriteria analysis, multicriteria decision analysis.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3461932Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 Schramm, F., and Morais, D.C.(2012). Decision support model for selecting and evaluating suppliers in the construction industry, Pesquisa Operacional (2012) 32(3): 643-662.
 Karande, P., Zavadskas,E. K., and Chakraborty,S. (2016). A study on the ranking performance of some MCDM methods for industrial robot selection problems, International Journal of Industrial Engineering Computations 7 (2016) 399–422.
 Salminen, P., Hokkanen, J., and Lahdelma, R. (1998). Comparing multi-criteria methods in the context of environmental problems. European Journal of Operations Research, 104(3), 485-496.
 Zanakis, S.H., Solomon, A., Wishart, N., and Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research, 107(3), 507-529.
 Raju, K.S., and Pillai, C.R.S. (1999). Multicriterion decision making in river basin planning and development. European Journal of Operational Research, 112(2), 249-257.
 Parkan, C., and Wu, M-L. (2000). Comparison of three modern multicriteria decision-making tools. International Journal of Systems Science, 31(4), 497-517.
 Caterino, N., Iervolino, I., Manfredi, G., and Cosenza, E. (2009). Comparative analysis of multi-criteria decision-making methods for seismic structural retrofitting. Computer-Aided Civil and Infrastructure Engineering, 24(4),432-445.
 Mela, K., Tiainen, T., and Heinisuo, M. (2012). Comparative study of multiple criteria decision-making methods for building design. Advanced Engineering Informatics, 26(4), 716-726.
 Chatterjee, P., and Chakraborty, S. (2012). Material selection using preferential ranking methods.Materials and Design, 35, 384-393.
 Anojkumar, L., Ilangkumaran, M., and Sasirekha, V. (2014). Comparative analysis of MCDM methods for pipe material selection in sugar industry. Expert Systems with Applications, 41(6), 2964-2980.
 Stanujkic, D., Ðordevic. B., Ðordevic. M. (2013). Comparative analysis of some prominent MCDM methods: a case of ranking Serbian banks. Serbian J Manag 8(2):213–241.
 Adali, E. A., and Isik, A. T. (2017). The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop
 selection problem, J Ind Eng Int (2017) 13:229–237.
 Kumar, R., Ray, A. (2015). Selection of material under conflicting situation using simple ratio optimization technique.In: Das et al. (eds) Proceedings of the fourth international conference on soft computing for problem solving, advances in intelligent systems and computing 335: 513–519.
 Brauers, W.K.M., Balezentis, A., Balezentis, T. (2011). MULTIMOORA for the EU member states updated with fuzzy number theory.Technol Econ Dev Eco 17(2):259–290.
 Stevens, S.P. (2015). Mathematical Decision Making: Predictive Models and Optimization. DVD, James Madison University, USA.
 Brauers, W.K.M., and Zavadskas, E.K. (2006).The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, vol. 35 (2006) No. 2, 446-469.
 Li, Zhi-Hui (2014). An Extension of the MULTIMOORA Method for Multiple Criteria Group Decision Making based upon Hesitant Fuzzy Sets. Journal of Applied Mathematics, Volume 2014, 1-16.
 Lai, Y.J., Liu, T.Y., and Hwang, C.L.(1994). TOPSIS for MODM. European Journal of Operational Research 76 (1994) 486-500.
 Yu,P. L.(1973).A Class of Solutions for Group Decision Problems. Management Science (pre-1986); Apr 1973; 19, 8; pg. 936-947.
 Yu, P.L., and M. Zeleny, M. (1975). The set of all non-dominated solutions in linear cases and a multicriteria simplex method, Journal of Mathematical Analysis and Applications 49 (1975) 430-448.
 Yu, P.L.(1985). Multiple-Criteria Decision Making: Concepts, Techniques, and Extensions, Plenum, New York, 1985.
 Zeleny, M.(1973).Compromise programming, in: J.L. Cochrane and M. Zeleny (eds.), Multiple Criteria Decision Making, University of South Carolina, Columbia, SC, 1973, 262-300.
 Zeleny, M.(1982). Multiple Criteria Decision Making, McGraw-Hill, New York, 1982.
 Hwang, C.L., and Yoon, K.(1981). Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, Heidelberg, 1981.
 Opricovic, S., and 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.
 Opricovic, S., and Tzeng, G.H. (2007). Extended VIKOR Method in Comparison with Outranking Methods, European Journal of Operational Research, 178, 514–529.
 Chen, Meifang (2004). Combining Grey Relation and TOPSIS Concepts for Selecting an Expatriate Host Country. Mathematical and Computer Modelling 40 (2004) 1473-1490.
 Kracka, M., Brauers, W.K.M., and Zavadskas, E. K. (2010). Ranking Heating Losses in a Building by Applying the MULTIMOORA.Inzinerine Ekonomika-Engineering Economics, 2010, 21(4), 352-359.
 Zardari, N.H., Ahmed, K., Shirazi, S.M., Yusop, Z.B. (2015). Weighting Methods and their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management. Springer International Publishing AG.
 World Development Indicators, Accessed in January 2019, http://www.worldbank.org
 The Balkan peninsula countries, Accessed in January 2019, https://www.britannica.com/place/Balkans
 Huang, J.J., Tzeng, G.H., Liu, H.H. (2009) A Revised VIKOR Model for Multiple Criteria Decision Making - The Perspective of Regret Theory, Communications in Computer and Information Science, vol. 35, 2009, no.11, pp. 761-768.
 Chatterjee, P., Chakraborty, S. (2014) Investigating the Effect of Normalization Norms in Flexible Manufacturing Sytem Selection Using Multi-Criteria Decision-Making Methods, Journal of Engineering Science and Technology Review 7 (3), (2014) 141 – 150.
 Chena,Ye., Kilgour, D. Marc, Hipel, Keith W. (2011) An extreme-distance approach to multiple criteria ranking. Mathematical and Computer Modelling 53 (2011) 646–658.
 Chatterjee, P., Chakraborty, S.(2016) A comparative analysis of VIKOR method and its variants. Decision Science Letters 5 (2016) 469–486.
 The Balkan peninsula countries indicators, Accessed in January 2019, https://en.wikipedia.org/wiki/Balkans