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Birlik Hava Savunma Önceliklerinin Tespitine Bulanık Bir Yaklaşım

A Fuzzy Approach to Determination of a Unit’s Air Defense Priorities

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Abstract (2. Language): 
The problem of air defense support determination is a complex issue and has a significant impact on the efficiency of defense systems. On the other hand, the selection of the units which should get air defense support among many alternatives is a multi-criteria decision-making (MCDM) problem. The aim of this study is to show that the Fuzzy TOPSIS method could be used for military issues and specifically how to use it for the determination of air defense support to the sub units of a brigade. The Fuzzy TOPSIS method, which is one of the Multiple Criteria Decision Making (MCDM) methods, is based on the calculation of the closeness coefficients by means of Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS). The alternatives are ranked according to the calculated closeness of coefficients. In this study, six aspects of getting air defense support were assessed in terms of four decision criteria by five decision makers (DM’s). The decision makers made their evaluations using linguistic variables and these variables were transformed into positive trapezoidal fuzzy numbers. The six candidates which were evaluated by DMs were ranked according to the air defense priorities by using Fuzzy TOPSIS.
Abstract (Original Language): 
Hava savunma desteğinin belirlenmesi problemi savunma sistemlerinin verimliliğinde önemli bir etkiye sahip ve karmaşık bir konudur. Diğer taraftan alternatifler arasından hava savunma desteği alacak birliklerin seçimi çok kriterli karar verme (ÇKKV) problemidir. Çalışmanın amacı Bulanık (Fuzzy) TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) yönteminin askerî konularda kullanılabileceğini göstermek ve yöntem yardımıyla bir tugayın ast birliklerinin hava savunmasının nasıl sağlanması gerektiğini ortaya koymaktır. ÇKKV yöntemlerinden biri olan Bulanık TOPSIS yönteminin temel mantığı Bulanık Pozitif İdeal Çözüm (FPİÇ) ve Bulanık Negatif İdeal Çözüm (FNİÇ) vasıtasıyla yakınlık katsayılarının hesaplanmasıdır. Yakınlık katsayılarına göre alternatifler sıralanır. Bu çalışmada, hava savunma desteği alacak altı unsur beş karar verici (KV) tarafından dört kritere göre değerlendirilmiştir. KV’ler değerlendirmelerini dilsel ifadelerle yapmış, sonra bu ifadeler pozitif yamuk bulanık sayılara dönüştürülmüştür. KV’ler tarafından değerlendirmesi yapılan altı aday, Bulanık TOPSIS yöntemiyle hava savunma önceliği fazla olandan az olana göre sıralanmıştır.
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REFERENCES

References: 

Air Defense Artillery Reference Handbook, (2000). Headquarters
Department of the Army, FM 3-01.11 (FM 44-100-2),
http://www.militarynewbie.com/pubs/FM%203-01.11%20AIR
%20DEFENSE%20ARTILLERY%20REFERENCE%20
HANDBOOK.pdf adresinden alınmıştır.
Büyüközkan, G., Feyzioğlu, O. ve Nebol, E. (2007). Selection of the
strategic alliance partner in logistics value chain. International
Journal of Production Economics. doi:10.1016/ j.ijpe.2007.01.016.
Byun, H.S. ve Lee, K.H., (2004). A decision support system for the
selection of rapid prototyping process using the modified TOPSIS
method. International Journal of Advanced Manufacturing
Technology-Published Online, April, 1-10.
Chen, C.T. (2000). Extensions of the TOPSIS for group decision-making
under fuzzy environment. Fuzzy Sets and Systems, 114, 1-9.
Chen, C.T., Lin, C.T. ve Huang, S.F. (2005). A fuzzy approach for supplier
evaluation and selection in supply chain management. International
Journal of Production Economies, 1-13.
Chen, C.T., Lin, C.T. ve Huang, S.F. (2006). A fuzzy approach for supplier
evaluation and selection in supply chain management. International
Journal of Production Economics, 102, 289–301.
Chen, T.Y. ve Tsao, C.Y. (2007). The interval-valued fuzzy TOPSIS
methods and experimental analysis. Fuzzy Sets and Systems,
doi:10.1016/j.fss.2007.11.004.
Cheng, C.H. (1996). Evaluating naval tactical missile systems by fuzzy
AHP based on the grade value of membership function. European
Journal of Operational Research, 96, 343-350.
16 Mehmet KABAK
Cheng, C.H. ve Lin, Y. (2002). Evaluating the best main battle tank using
fuzzy decision theory with linguistic criteria evaluation. European
Journal of Operational Research, 142, 174-186.
Chu, T.C. ve Lin, Y.C., (2003). A fuzzy TOPSIS method for robot selection.
The International Journal of Advanced Manufacturing Technology,
21, 284-290.
Ertuğrul, İ. ve Karakasoğlu, N. (2007). Performance evaluation of Turkish
cement firms with fuzzy analytic hierarchy process and TOPSIS
methods. Expert Systems with Applications, 36(1), 702–715.
Kahraman, C., Büyüközkan, G. ve Ateş, N. Y. (2007). A two phase multiattribute
decision making approach for new product introduction.
Information Sciences, 177, 1567–1582.
Hwang, C.L. ve Yoon, K. (1981). Multiple attribute decision making:
Methods and applications, A State of the Art Survey. New York:
Springer-Verlag.
Önüt, S. ve Soner, S. (2007). Transshipment site selection using the AHP
and TOPSIS approaches under fuzzy environment. Waste
Management. doi:10.1016/ j.wasman.2007.05.019.
Parkan, C. ve Wu, M.L. (1999). Decision Making and Performance
Measurement Models with Applications to Robot Selection.
Computers and Industrial Engineering, 36, 503-523.
Raj, P.A. ve Kumar, D.N. (1999). Ranking alternatives with fuzzy weights
using maximizing set and minimizing set. Fuzzy Sets and Systems,
105, 365–375.
Yang, T. ve Hung, C.C. (2007). Multiple-attribute decision making methods
for plant layout design problem. Robotics and Computer-Integrated
Manufacturing, 23, 126–137.
Yong, D. (2006). Plant location selection based on fuzzy TOPSIS.
International Journal of Advanced Manufacturing Technology, 28,
839–844.
Yurdakul, M. ve Çoğun C. (2003). Development of a multi-attribute
selection procedure for non- traditional machining processes”
Journal of Engineering Manufacture, 217 Part B: 993- 1009.
Yurdakul, M. ve İç, Y.T. (2005). development of a performance
measurement model for manufacturing companies using the AHP
and TOPSIS approaches”, International Journal of Production
Research, 43, 4609-4641.
Wang, Y.M. ve Elhag, T.M.S. (2006). Fuzzy TOPSIS method based on
alpha level sets with an application to bridge risk assessment. Expert
Systems with Applications, 31, 309–319.
Savunma Bilimleri Dergisi, Kasım 2011, Cilt 10, Sayı 2, 1-17. 17
Wang, T.C. ve Chang, T.H. (2007). Application of TOPSIS in evaluating
initial training aircraft under a fuzzy environment. Expert Systems
with Applications, 33, 870-880.
Zimmerman, H.J. (1996). Fuzzy sets theory and its applications. Boston:
Kluwer Academic Publishers.

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