A Hybrid Moora-Fuzzy Algorithm For Special Education and Rehabilitation Center Selection

Yıl 2014, Cilt: 2 Sayı: 3, 53 - 62, 21.09.2014

Gökhan Ozcelik Emel Aydoğan Cevriye Gencer

https://doi.org/10.17858/jmisci.53708

Cited By: 9

Öz

Special education and rehabilitation centers are established in order to train children and young people who need
special education. The main goal of this study is to determine the most appropriate special education and rehabilitation center, in terms of various criteria by evaluating three different corporations which are active in Kayseri/Turkey. For that purpose, we apply Fuzzy Analytic Hierarchy Process and MOORA which are the methods of multi-criteria decision making. Education, compliance of ergonomic, compliance of corporation building, cost, public opinion and prestige and assessment of personnel are considered as the criteria. Firstly, these criteria are weighted by using Fuzzy Analytic Hierarchy Process, later MOORA method is used to choose the most appropriate corporation.

Anahtar Kelimeler

Special Education and Rehabilitation Center; MOORA; Fuzzy Analytic Hierarchy Process; Multi Criteria Decision Making.

Kaynakça

• Point Tche byche ff MO O RA Re fe re nce Point with Sig. C oe f. Full Multiplicative Form MultiMO O RA Parilti 3 2 - 3 2 3 3 Nida 1 1 1 1 1 Ilgim 2 2 - 3 3 2 2 Conclusion All calculation results show that the best alternative is Nida. According to MultiMOORA, the best center is Nida, the second center is Ilgim and the third center is Parilti. In this study it is shown that MOORA is an effective method for the selection of alternatives. The ranking of this case study is summarized in Fig 1. Fig 1. Ranking for all methods The main advantage of these methods is that a simple ratio system is adopted to make the decision matrices dimensionless and comparable. The performance of these methods is also comparable with other popular and widely used Multi-Criteria Decision Making methods. Thus, these methods can also be applied to the other decision-making scenario with any number of alternatives and criteria. MOORA and MULTIMOORA optimization technique with discrete alternatives was used for ranking alternatives in the selection of the special education and rehabilitation center. In the future work, the case study will be analyzed using grey numbers. Moreover, the results will be compared results with other multi-criteria decision making methods. 0,5 1 1,5 2 2,5 3 3,5 Nida Parilti Ilgim MOORA Ratio System Reference Point Reference Point with Sig. Coef. Full Multiplicative Form MultiMOORA Ayağ, Z. (2005). A fuzzy AHP-based simulation approach to concept evaluation in a NPD environment. IIE transactions, 37(9), 827-842.
• Baležentis, A., Valkauskas, R., & Baležentis, T. (2010).
• Evaluating situation of Lithuania in the European Union: structural indicators and multimoora method.Technological and Economic Development of Economy, (4), 578-602. Brauers, W. K. M.; Ginevicius, R., (2009). Robustness in regional development studies. The case of Lithuania, Journal of Business Economics and Management, 10(2), 121-140.
• Brauers, W. M. K.; Ginevicius, R., (2010). The economy of the Belgian regions tested with MULTIMOORA, Journal of
• Business Economics and Management, 11(2), 173-209. Brauers, W. K., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35, 445-469.
• Brauers, W. K. M., Zavadskas, E. K., (2008). Multi-objective optimization in location theory with a simulation for a department store, Transformations in Business & Economics 7(3), 163-183.
• Brauers, W. K. M.; Zavadskas, E. K.; Peldschus, F.; Turskis, Z. (2008a). Multi-objective decision-making for road design, Transport 23(3), 183-193.
• Brauers, W. K. M.; Zavadskas, E. K.; Turskis, Z.; Vilutiene, T. (2008b). Multi-objective contractor’s ranking by applying the MOORA method, Journal of Business Economics and Management 9(4), 245-255.
• Brauers WKM., Zavadskas EK., (2009). Robustness of the multiobjective MOORA method with a test for the facilities sector. Technological and Economic Development of
• Economy: Baltic J on Sustainability, 15(2), 352-375. Brauers, WKM., & Zavadskas EK., (2010). Project management by MULTIMOOORA as an instrument for transition economies. Technological and Economic
• Development of Economy, 16(1), 5-24. Brauers WKM., Zavadskas EK., (2011). MULTIMOORA optimization used to decide on a bank loan to buy property.
• Technol Econ Dev Econ,17, 174-88. Cavkaytar A., (2006). Teacher Traınıng On Specıal
• Educatıon In Turkey. The Turkish Online Journal of Educational Technology – TOJET July (2006) ISSN: 13036521, 5(3)
• Chakraborty, S. (2010). Application of the MOORA method for decision making in manufacturing environment, The International Journal of Advanced Manufacturing Technology 54 (9-12), 1155-1166.
• Dağdeviren, M., (2007). Personnel selection with fuzzy analytical hierarchy process and an application, J. Fac. Eng. Arch. Gazi Univ., 22(4), 791-799.
• Deng, H., (1999) Multicriteria analysis with fuzzy pairwise comparison, International Journal of Approximate Reasoning, 21(3), 215-231.
• Ghodsypour, S. H., & O'brien, C. (1998). A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International journal of production economics, 56, 199-212.
• Ginevičius R., Podvezko V., (2008) Multi-criteria graphical analytical evaluation of the financial state of construction enterprises. Baltic J on Sustainability 14, 452-461.
• Karande P., Chakraborty S., (2012) Application of multiobjective optimization on the basis of ratio analysis (MOORA) method for materials selection. Materials and Design, 37, 317-324.
• Kracka M., Brauers WKM., Zavadskas EK., (2010). Ranking Heating Losses in a Building by Applying the MULTIMOORA. ISSN 1392 – 2785 Inzinerine EkonomikaEngineering Economics, 21(4), 352-359.
• Nooramin, A. S., Kiani Moghadam, M., Moazen Jahromi, A. R., & Sayareh, J. (2012). Comparison of AHP and FAHP for selecting yard gantry cranes in marine container terminals. Journal of the Persian Gulf, 3(7), 59-70.
• Prakash, T.N., (2003) Land Suitability Analysis for Agricultural Crops: A Fuzzy Multicriteria Decision Making Approach, MSc Thesis, ITC Institute.
• Stanujkic D., Magdalinovic N.,Jovanovic R and Stojanovic S., (2012). An objective multi-criteria approach to optimization using MOORA method and interval grey numbers. Technological and Economic Development of Economy., 18(2), 331-363.
• Van Delf, A., & Nijkamp, P. (1977), “Multi- criteria Analysis and Regional Decision- making”, M.Nijhoff, Leiden, Nl.
• Yazgan, H. R., Boran, S., & Goztepe, K. (2010). Selection of dispatching rules in FMS: ANP model based on BOCR with choquet integral.
• The International Journal of Advanced Manufacturing Technology, 49(5-8), 785-801. APPENDIX A Table 10. Pairwise comparison matrix and fuzzy weights for criteria Pairwise comparison matrix and fuzzy weights for criteria Criteria ED ER IB CO IP AP Education (ED) (1, 1, 1) (1, 2, 4) (5, 7, 9) (3, 5, 7) (1/7, 1/5, 1/3) (3, 5, 7) Ergonomics (ER) (1/4, 1/2, 1) (1, 1, 1) (1, 1, 1) (3, 5, 7) (1, 2, 4) (1, 2, 4) Institution’s Building (IB) (1/9, 1/7, 1/5) (1, 1, 1) (1, 1, 1) (1/4, 1/2, 1) (1/7, 1/5, 1/3) (1/4, 1/2, 1) Cost (CO) (1/7, 1/5, 1/3) (1/7, 1/5, 1/3) (1, 2, 4) (1, 1, 1) (1, 2, 4) (1/7, 1/5, 1/3) Image and Prestige (IP) (3, 5, 7) (1/4, 1/2, 1) (3, 5, 7) (1/4, 1/2, 1) (1, 1, 1) (5, 7, 9) Assessment of Personnel (AP) (1/7, 1/5, 1/3) (1/4, 1/2, 1) (1, 2, 4) (3, 5, 7) (1/9, 1/7, 1/5) (1, 1, 1) Geometric mean of the 1 th row: 1/6 1/6 1/6 {(1×1×5×3×1/7×3) ,(1×2×7×5×1/5×5) ,(1×4×9×7x1/3×7) }=(36, 2.03, 2.89) Geometric mean of the 2nd row: 1/6 1/6 1/6 {(1/4×1×1×3×1×1) ,(1/2×1×1×5×2×2) ,(1×1×1×7×4×4) }=(0.95, 1.47, 2.20) Geometric mean of the 3 rd row: 1/6 1/6 1/6 {(1/9×1×1×1/4×1/7×1/4) ,(1/7×1×1×1/2×1/5×1/2) ,(1/5×1×1×1×1/3×1) }=(0.31, 0.44, 0.64) Geometric mean of the 4 th row: 1/6 1/6 1/6 {(1/7×1/7×1×1×1×1/7) ,(1/5×1/5×2×1×2×1/5) ,(1/3×1/3×4×1×4×1/3) }=(0.38, 0.56, 0.92) Geometric mean of the 5 th row: 1/6 1/6 1/6 {(3×1/4×3×1/4×1×5) ,(5×1/2×5×1/2×1×7) ,(7×1×7×1×1×9) }=(19, 1.88, 2.76) Geometric mean of the 6 th row: 1/6 1/6 1/6 {(1/7×1/4×1×3×1/9×1) ,(1/5×1/2×2×5×1/7×1) ,(1/3×1×4×7×1/5×1) }=(0.48, 0.72, 1.11) The sum of the fuzzy geometric averages: (4.67, 7.1, 10.52) The fuzzy weight of ED Factor: {(1.36/10.52, 2.03/7.1, 2.89/4.67)}=(0.13, 0.29, 0.62) The fuzzy weight of ER Factor: {(0.95/10.52, 1.47/7.1, 2.20/4.67)}= (0.09, 0.21, 0.47) The fuzzy weight of IB Factor: {(0.31/10.52, 0.44/7.1, 0.64/4.67)}=(0.03, 0.06, 0.14) The fuzzy weight of CO Factor: {(0.38/10.52, 0.56/7.1, 0.92/4.67)}=(0.04, 0.08, 0.20) The fuzzy weight of IP Factor: {(1.19/10.52, 1.88/7.1, 2.76/4.67)}=(0.11, 0.26, 0.59) The fuzzy weight of AP Factor: {(0.48/10.52, 0.72/7.1, 1.11/4.67)}= (0.05, 0.10, 0.24)