### APPLICATION OF FUZZY TOPSIS METHOD AND DOPGA ALGORITHM IN PURCHASING DECISION PROCESS: 3D TELEVISION EXAMPLE

#### Öz

Nowadays, television is at the head of devices where people can have fun in their homes, and people spend most of their time on television. So, they want to purchase a pretty good television. At the head of these televisions are three-dimension televisions (3DTVs). Since purchasing a television is a long-term shopping, it is necessary to evaluate it with great care and to determine its best before purchasing it. Evaluation of the best 3D TV is a complex problem with no definite structure and it depends on the features of the 3D TVs. There are many performance indicators as dynamic contrast ratio, refresh rate, power consumption on mode, depth, weight, cost, response time *etc.* affecting the decision-making process about 3D TVs. Therefore, evaluation of 3D TVs is a complex decision- making process. Hence, this problem can be solved by multi-criteria decision-making (MCDM) methods. In this paper, eight popular 3D TVs considered to be purchased in technology markets are compared by using six specifications obtained from its catalog and the experts’ opinion. In this study, in order to determine the best 3D TV, alternative 3D TVs were first sorted by using Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) method, and then, the sensitivity analysis was performed. Secondly, to compare the 3D TV ranking obtained from Fuzzy TOPSIS method, 3D TVs were ordered by using Domination Power of an Individual Genetic Algorithm (DOPGA) method and necessary comparisons and evaluations were made and the best 3D TV was selected.

#### Anahtar Kelimeler

#### Tam Metin:

PDF (English)#### Referanslar

Altiparmak, F., Gen, M., Lin L. & Paksoy, T.A. (2006). Genetic algorithm approach for multi-objective optimization of supply chain Networks. Computers & Industrial Engineering, 51(1), pp 197–216.

Bandyopadhyay, S. & Bhattacharya, R. (2013). Applying modified NSGA-II for bi-objective supply chain problem. Journal of Intelligent Manufacturing, 24(1), pp 707-716.

Bas, E. (2013). The integrated framework for analysis of electricity supply chain using an integrated SWOT-fuzzy TOPSIS methodology combined with AHP: The case of Turkey. Electrical Power and Energy Systems, 44, pp 897–907.

Chan, F.T.S. & Chung, S.H. (2004). A multi-criterion genetic algorithm for order distribution in a demand driven supply chain. International Journal of Computer Integrated Manufacturing, 17(4), pp 339-351.

Che, Z.H. (2009). Pricing strategy and reserved capacity plan based on product life cycle and production function on LCD TV manufacturer. Expert Systems with Applications, 36, pp 2048–2061.

Chen, C.F. & Tsai, D. (2007). How destination image and evaluative factors affect behavioral intentions? Tourism Management, 28, pp 1115-1122.

Chen, C.T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114, pp 1-9.

Cheng, C.T., Zhao, M.Y., Chau, K.W. & Wu, X.Y. (2006). Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure. Journal of Hydrology, 316, pp 129–140.

Chiang, Tzu-An. (2012). Multi-objective decision-making methodology to create an optimal design chain partner combination. Computers & Industrial Engineering, 63, pp 875–889.

Chiu, M. C., Kuo, M. Y., & Kuo, T. C. (2015). A Systematic Methodology to Develop Business Model of A Product Service System. International Journal of Industrial Engineering: Theory, Applications and Practice, 22(3).

Coello, C.A.C. (2006). Evolutionary multi-objective optimization: A historical view of the field. IEEE Computational Intelligence Magazine, 1(1), pp 28-36.

Dymova, L., Sevastjanov, P. & Tikhonenko, A. (2013). An approach to generalization of fuzzy TOPSIS method. Information Sciences, 238, pp 149–162.

Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Chichester, U.K, Wiley.

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp 182-197.

Ergul, E.U. & Eminoglu, I. (2014). DOPGA: A new fitness assignment scheme for multi-objective evolutionary algorithms. International Journal of Systems Science, 45(3), pp 407-426.

Fonseca, C.M. & Fleming, P.J. (1993). Genetic algorithms for multi-objective optimization: formulation, discussion and generalization. In Proceedings of the Fifth International Conference on Genetic Algorithms, pp 416-423.

Grobbelaar, S., & Visser, J. K. (2015). Determining the cost of predictive component replacement in order to assist with maintenance decision-making. South African Journal of Industrial Engineering, 26(1), 150-162.

Ic, Y.T. (2014). A TOPSIS based design of experiment approach to assess company ranking. Applied Mathematics and Computation, 227, pp 630–647.

Kannan, D., Jabbour, A.B.L.S & Jabbour, C.J.C. (2014). Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233, pp 432–447.

Kim, Y., Chung, E.S., Jun, S.M. & Kim, S.U. (2013). Prioritizing the best sites for treated wastewater instream use in an urban watershed using fuzzy TOPSIS. Resources, Conservation and Recycling, 73, pp 23- 32.

Lee, G., Jun, K.S. & Chung, E.S. (2014). Robust spatial flood vulnerability assessment for Han River using fuzzy TOPSIS with a-cut level set. Expert Systems with Applications, 41, pp 644–654.

Ozcan, E. C., Unlusoy, S., & Eren, T. (2017). ANP ve TOPSIS Yontemleriyle Turkiye'de Yenilenebilir Enerji Yatirim Alternatiflerinin Degerlendirilmesi. Selcuk University Journal of Engineering, Science And Technology, 5(2), pp 204-219.

Shahanaghi, K. S & Yazdian, S.A. (2009). Vendor selection using a new fuzzy group TOPSIS approach. Journal of Uncertain Systems, 3(3), pp 221-231.

Roshandel, J., Miri-Nargesi, S.S. & Hatami-Shirkouhi, L. (2013). Evaluating and selecting the supplier in detergent production industry using hierarchical fuzzy TOPSIS. Applied Mathematical Modelling, 37, pp 10170–10181.

Rouyendegh, B.D. & Saputro, T.E. (2014). Supplier selection using integrated fuzzy TOPSIS and MCGP: A case study. Procedia - Social and Behavioral Sciences, 116, pp 3957–3970.

Srinivas, N. & Deb, K. (1994). Multi-objective function optimization using nondominated sorting genetic algorithm. Evolutionary Computation, 2(3), pp 221–248.

Taylan, O., Alidrisi, H., & Kabli, M. (2014). A multi-criteria decision-making approach that combines fuzzy topsis and DEA methodologies. South African Journal of Industrial Engineering, 25(3), 39-56.

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, In press.

Wang, J.V., Cheng, C.H. & Kun-Cheng, H. (2009). Fuzzy hierarchical TOPSIS for supplier selection. Applied Soft Computing, 9, pp 377-386.

Yan, G. (2009). Research on green suppliers’ evaluation based on AHP & genetic algorithm. In: International Conference on SPS, IEEE, 15-17 May, pp 615-619.

Yayla, A.Y., Yildiz, A. & Özbek, A. (2012). Fuzzy TOPSIS method in supplier selection and an application in garment industry. Fibres & Textiles in Eastern Europe, 20, 4(93), pp 20-23.

Yayla, A.Y. & Yildiz, A. (2013). Fuzzy ANP based MCDM methodology for a family automobile purchasing decision. South African Journal of Industrial Engineering, 24(2), pp 167-180.

Yıldız, A., Yayla, A.Y. (2015). Multi-Criteria Decision-Making Methods For Supplier Selection: A Literature Review. South African Journal of Industrial Engineering, 26(2), 158-177.

Yeh, W.-C. & Chuang, M.C. (2011). Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Systems with Applications, 38(4), pp 4244-4253.

Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N. & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), pp 32-49.

Zitzler, E. & Thiele, L. (1999). Multi-objective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. on Evolutionary Computation, 3(4), pp 257-271.

Zitzler, E., Laumanns, M. & Thiele, L. (2001). SPEA2: Improving the strength pareto evolutionary algorithm. TIK-Report 103, Swiss Federal Institute of Technology, Zurich.

#### Madde Ölçümleri

_{Metrics powered by PLOS ALM}

### Refback'ler

- Şu halde refbacks yoktur.

Telif Hakkı (c) 2018 Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

**Tarayan Veri Tabanları**