Integration of Fuzzy AHP and Fuzzy GTMA for Location Selection of Gas Pressure Reducing Stations: A Case Study

Ahmad Jafarnejad Chaghooshi, Hossein Safari, Mohammad Reza Fathi

Abstract


Location selection is a multi-criteria decision problem and has a strategic importance for many companies. In this study, an integrated approach which employs Fuzzy AHP and Fuzzy GTMA is proposed for location selection of Gas pressure reducing stations. The Fuzzy AHP is usedto analyze the structure of location selection problem and to determine weights of the criteria, and Fuzzy GTMA method is used to obtain final ranking. A numerical example demonstrates the application of the proposed method. We apply the integrated approach in a real case to demonstrate the application of the proposed method.



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References


Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17, 233–247.

Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95, 649–655.

Darvish, M., M. Yasaei, and A. Saeedi. (2009). Application of the graph theory and matrix methods to contractor ranking. International Journal of Project Management, 27(6): p. 610-619.

Ertuğrul, I., and Karakaşoğlu N. (2008). Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. Int J Adv Manuf Technol , 39:783–795.

Faisal, M.N., D. Banwet, and R. Shankar. (2007). Quantification of risk mitigation environment of supply chains using graph theory and matrix methods. European Journal of Industrial Engineering, 1(1): p. 22-39.

Kahraman, C., Cebeci, U., Ulukan, Z. (2003). Multi-criteria supplier selection using fuzzy AHP. Logist Inf Manag 16(6):382–394.

Karsak, E. E. (2002). Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. International Journal of Production Research 40(13), 3167–3181.

Kaufmann, A., and Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. Amsterdam: North-Holland.

Liang, GS . (1999). Fuzzy MCDM based on ideal and anti-ideal concepts. European Journal of Operational Research, 112:682–691.

Nourani, Y. and B. Andresen. (1999). Exploration of NP-hard enumeration problems by simulated annealing--the spectrum values of permanents. Theoretical computer science, 215(1-2): p. 51-68.

Rao, RV. (2007). Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods. Springer, London.

Rao, R.V. (2006). A decision-making framework model for evaluating flexible manufacturing systems using digraph and matrix methods. The International Journal of Advanced Manufacturing Technology, 30(11): p. 1101-1110.

Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw- Hill.

Stevenson, WJ . (1993). Production / operations management, 4th edn. Richard D. Irwin Inc., Homewood.

Van Laarhoven, P. J. M., & Pedrcyz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11, 229–241.

Yang, J., Lee, H . (1997). An AHP decision model for facility location selection. Facilities 15(9/10): 241–254

Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences, 8(3), 199–249.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.




DOI: http://dx.doi.org/10.5296/jmr.v4i3.1751

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