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

Abstract

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.

Keywords

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

Kaynakça

1. 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.
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26. 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)