Evaluating Tactical Missile Systems by Using Fuzzy AHP and TOPSIS Technique

Evaluating Tactical Missile Systems by Using Fuzzy AHP and TOPSIS Technique

Evaluating tactical missile systems is a complex system of interacting elements. For example, a good missile system requests good missile performance and minimal cost; the performance and cost depend on improvement of science and technology and economic resources; technology depends on ideas and resources; ideas depend on politics for their acceptance and support; and so on. In such an intricate network of factors, first causes and then final effects cannot be identified easily. These factors directly depend on the expectations of the decision-maker, and on additional complex subfactors, etc. In the complex system, our minds have not yet evolved to the point where we can clearly see these ultimate relationships and readily resolve important issues. Evaluating tactical missile systems is a multi-criteria decision-making (MCDM) problem. This paper presents an evaluation model based on the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The AHP is used to analyze the structure of systems evaluating and to determine weights of the criteria, and fuzzy TOPSIS method is used to obtain final ranking.

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  • Albayrak, E., & Erensal, Y. C. (2004). Using analytic hierarchy process (AHP) to improve human performance. An application of multiple criteria decision making problem. Journal of Intelligent Manufacturing, 15, 491–503.
  • Boran, S., Yazgan, H. R., & Goztepe, K. (2011). A fuzzy ANP-based approach for prioritising projects: a Six Sigma case study. International Journal of Six Sigma and Competitive Advantage, 6(3), 133-155.
  • Büyüközkan, G., & Çifçi, G. (2012). A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Systems with Applications, 39, 2341– 2354.
  • Chamodrakas, I., Batis, D., & Martakos, D. (2010). Supplier selection in electronic marketplaces using satisficing and fuzzy AHP. Expert Systems with Applications, 37, 490–498.
  • Chang, Y. H., & Yeh, C. H. (2002). A survey analysis of service quality for domestic airlines. European Journal of Operational Research, 139, 166–177.
  • Chen, S. M. (1996). Evaluating weapon systems using fuzzy arithmetic operations. Fuzzy Sets Syst., 77, 265–276.
  • Cheng, C. H. (1996). Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. Eur. J. Oper. Res., 96, 343–350.
  • Cheng, C. H. (1999). Evaluating weapon systems using ranking fuzzy numbers. Fuzzy Sets Syst., 107, 25–35.
  • Cheng, C. H., & Lin, Y. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. Eur. J. Oper. Res., 142, 174–186.
  • Cheng, C. H., & Mon, M. L. (1994). Evaluating weapon system by analytic hierarchy process based on fuzzy scales. Fuzzy Sets Syst., 63, 1 - 10.
  • Cheng, C. H., Yang, K. L., & Hwang, C. L. (1999). Evaluating attack helicopters by AHP based on linguistic variable weights. Eur. J. Oper. Res., 116, 423–435.
  • Dagdeviren, M., Yavuz, S., & Kilinc, N. (2009). Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Syst. Appl., 36, 8143–8151.
  • Göleç, A., & Taşkın, H. (2007). Novel methodologies and a comparative study for manufacturing systems performance evaluations. Information Sciences, 177, 5253–5274.
  • Juniora, F. R. L., Osirob, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods tosupplier selection. Applied Soft Computing, 21, 194–209.
  • Kahraman, C., Beşkese, A., & Ruan, D. (2004). Measuring flexibility of computer integrated manufacturing systems using fuzzy cash flow analysis. Information Sciences, 168, 77–94.
  • Kahraman, C., Cebeci, U., & Ruan, D. (2004). Multi-attribute comparison of catering services using fuzzy AHP: The case of Turkey. International Journal of Production Economics, 87, 171– 184.
  • Kaya, T., & Kahraman, C. (2011). An integrated Fuzzy AHP– ELECTRE methodology for environmental impact assessment. Expert Syst. Appl., 38, 8553–8562.
  • Mergias, I., Moustakas, K., Papadopoulos, A., & Loizidou, M. (2007). Multi-criteria decision aid approach for the selection of the best compromise management scheme for ELVs: The case of Cyprus. Journal of Hazardous Materials, 147, 706–717.
  • Mon, M. L., Cheng, C. H., & Lin, J. C. (1994). Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight. Fuzzy Sets Syst., 62, 127-134.
  • Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
  • Vinodh, S., Prasanna, M., & Hari Prakash, N. (2014). Integrated Fuzzy AHP–TOPSIS for selecting the best plastic recycling method: A case study. Applied Mathematical Modelling.
  • Wang, T. C., & Chang, T. H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environmen. Expert Systems with Applications, 33, 870–880. Zadeh, L. A. (1965). Fuzzy set. Information and Control, 8(3), 338– 353.