An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design

  • Süleyman Çakır


In respond to new market requirements and competitive positioning of manufacturing companies selecting optimal machines that are consistent with manufacturing goals is of crucial importance. As it involves multiple conflicting criteria and inherent ambiguity and vagueness, election of a suitable machine can be regarded as a fuzzy multi-criteria decision making problem. In this study, for the first time in the literature, an integrated approach consisting of fuzzy simple multiattribute rating technique (SMART) approach and fuzzy weighted axiomatic design (FWAD) approach is proposed to determining the optimal continuous fluid bed tea dryer for a privately owned tea plant operating in Turkey. The weights of the evaluation criteria are calculated via fuzzy SMART and then FWAD is utilized to rank competing machine alternatives in terms of their overall performance. In the FWAD application phase, five experts have determined functional requirements (FRs) and have rated alternatives. Therefore, individual fuzzy opinions were required to be aggregated in order to set up a group consensus. A group decision analysis, referred to as the least squares distance method is used to aggregating the ratings of FRs and alternatives. It is concluded that the proposed hybrid methodology is a robust decision support tool for ranking machine alternatives under fuzzy environment and furthermore, it can be exploited for other fuzzy decision making problems, as well.


Machine selection Tea industry Fuzzy SMART Fuzzy weighted axiomatic design 


  1. Akgün, İ., Kandakoglu, A., & Özok, A. F. (2010). Fuzzy integrated vulnerability assessment model for critical facilities in combating the terrorism. Expert Systems with Application, 37, 3561–3573.Google Scholar
  2. Arslan, M. C., Catay, B., & Budak, E. (2004). A decision support system for machine tool selection. Journal of Manufacturing Technology Management, 15, 101–109.CrossRefGoogle Scholar
  3. Atmani, A., & Lashkari, R. S. (1998). A model of machine tool selection and operation allocation in flexible manufacturing systems. International Journal of Production Research, 36, 1339–1349.CrossRefGoogle Scholar
  4. Babic, B. (1999). Axiomatic design of flexible manufacturing systems. International Journal of Production Research, 37(5), 1159–1173.CrossRefGoogle Scholar
  5. Barla, S. B. (2003). A case study of supplier selection for lean supply by using a mathematical model. Logistics Information Management, 16(6), 451–459.CrossRefGoogle Scholar
  6. Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision-making method and applications. Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  7. Chen, Y., & Wang, T. C. (2009). Optimizing partners’ choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR. International Journal of Production Economics, 120, 233–242.CrossRefGoogle Scholar
  8. Chou, S. Y., & Chang, Y. H. (2008). A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach. Expert Systems with Applications, 34, 2241–2253.CrossRefGoogle Scholar
  9. Çelik, M., Cebi, S., Kahraman, C., & Er, I. D. (2009a). An integrated fuzzy QFD model proposal on routing of shipping investment decisions in crude oil tanker market. Expert Systems with Applications, 36(3), 6227–6235. 2.Google Scholar
  10. Çelik, M., Cebi, S., Kahraman, C., & Er, I. D. (2009b). Application of axiomatic design and TOPSIS methodologies under fuzzy environment for proposing competitive strategies on Turkish container ports in maritime transportation network. Expert Systems with Applications, 36(3), 4541–4557.Google Scholar
  11. Çelik, M., Kahraman, C., Cebi, S., & Er, I. D. (2009c). Fuzzy axiomatic design-based performance evaluation model for docking facilities in shipbuilding industry: The case of Turkish shipyards. Expert Systems with Applications, 36(1), 599–615.Google Scholar
  12. Edwards, W. (1971). Social utilities. The Engineering Economist. In Summer Symposium Series, 6, 119–129.Google Scholar
  13. Edwards, W. (1977). How to multiattribute utility measurement for social decision-making. IEEE Transactions on Systems, Man, and Cybernetics, SMC-7, 326–340.Google Scholar
  14. Edwards, W., & Barron, F. H. (1994). SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement. Organizational Behavior and Human Decision Processes, 60(3), 306–325.CrossRefGoogle Scholar
  15. Eraslan, E., Akay, D., & Kurt, M. (2006). Usability ranking of intercity bus passenger seats using fuzzy axiomatic design theory. In Cooperative design, visualization, and engineering. Lecture notes in computer science, 4001, 141–148.Google Scholar
  16. Gerrard, W. (1988). Selection procedures adopted by industry for introducing new machine tools. In Proceedings of 4th National Conference on Production Research, pp. 525–531.Google Scholar
  17. Hampton, M. G. (1992). Production of black tea. In K. C. Willson & M. N. Clifford (Eds.), Tea (pp. 459–511). Netherlands: Springer.CrossRefGoogle Scholar
  18. Kahraman, C., & Cebi, S. (2009). A new multi-attribute decision making method: Hierarchical fuzzy axiomatic design. Expert Systems with Applications, 36(3), 4848–4861.CrossRefGoogle Scholar
  19. Kahraman, C., Kaya, İ., & Cebi, S. (2009). A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy, 34, 1603–1616.CrossRefGoogle Scholar
  20. Karsak, E. E., & Kuzgunkaya, O. (2002). A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system. International Journal of Production Economics, 79(2), 101–111.CrossRefGoogle Scholar
  21. Karsak, E. E. (2008). Using data envelopment analysis for evaluating flexible manufacturing systems in the presence of imprecise data. International Journal of Advanced Manufacturing Technology, 35, 867–874.CrossRefGoogle Scholar
  22. Kim, S. J., Suh, Nam P., & Kim, S. (1991). Design of software systems based on AD. Robotics and Computer-Integrated Manufacturing, 8(4), 243–255.CrossRefGoogle Scholar
  23. Kulak, O. (2005). A decision support system for fuzzy multi-attribute selection of material handling equipments. Expert Systems with Applications, 29(2), 310–319.CrossRefGoogle Scholar
  24. Kulak, O., Durmusoglu, M. B., & Kahraman, C. (2005). Fuzzy multi-attribute equipment selection based on information axiom. Journal of Materials Processing Technology, 169, 337–345.CrossRefGoogle Scholar
  25. Kulak, O., & Kahraman, C. (2005a). Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach. International Journal of Production Economics, 95, 415–424.CrossRefGoogle Scholar
  26. Kulak, O., & Kahraman, C. (2005b). Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Information Sciences, 170, 191–210.CrossRefGoogle Scholar
  27. Kwong, C. K., Ip, W. H., & Chan, J. W. K. (2002). Combining scoring method and fuzzy expert systems approach to supplier assessment: A case study. Integrated Manufacturing Systems, 13(7), 512–519.CrossRefGoogle Scholar
  28. Liu, S. T. (2008). A fuzzy DEA/AR approach to the selection of flexible manufacturing systems. Computers & Industrial Engineering, 54(1), 66–76.CrossRefGoogle Scholar
  29. Maldonado, A., García, J. L., Alvarado, A., & Balderrama, C. O. (2013). A hierarchical fuzzy axiomatic design methodology for ergonomic compatibility evaluation of advanced manufacturing technology. The International Journal of Advanced Manufacturing Technology, 66, 171–186.CrossRefGoogle Scholar
  30. Önüt, S., Kara, S. S., & Efendigil, t. (2008). A hybrid fuzzy MCDM approach to machine tool selection. Journal of Intelligent Manufacturing, 19, 443–453.CrossRefGoogle Scholar
  31. Panchariya, P. C., Popovic, D., & Sharma, A. L. (2002). Thin-layer modeling of black tea drying process. Journal of Food Engineering, 52, 349–357.CrossRefGoogle Scholar
  32. Samvedi, A., Jain, V., & Felix, T. S. C. (2012). An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis. International Journal of Production Research, 50(12), 3211–3221.CrossRefGoogle Scholar
  33. Sarkis, J. (1997). Evaluating flexible manufacturing systems using data envelopment analysis. The Engineering Economist, 43(1), 25–46.CrossRefGoogle Scholar
  34. Seydel, J. (2006). Data envelopment analysis for decision support. Industrial Management & Data Systems, 106(1), 81–95.CrossRefGoogle Scholar
  35. Sivarao, P. B., El-Tayeb, N. S. M., & Vengkatesh, V. C. (2009a). Mamdani fuzzy inference system modeling to predict surface roughness in laser machining. International Journal of Intelligent Information Technology Application, 2(1), 12–18.Google Scholar
  36. Sivarao, P. B., El-Tayeb, N. S. M., & Vengkatesh, V. C. (2009b). GUI based ANFIS modeling: Back propagation optimization method for CO2 laser machining. International Journal of Intelligent Information Technology Application, 2(4), 191–198.Google Scholar
  37. Suh, N. P. (1990). The principles of design. New York: Oxford University Press.Google Scholar
  38. Suh, N. P. (1995). Design and operation of large systems. Annals of CIRP, 14(3), 203–213.Google Scholar
  39. Suh, N. P. (1997). Design of systems. Annals of CIRP, 46(1), 75–80.CrossRefGoogle Scholar
  40. Tabucanon, M. T., Batanov, D. N., & Verma, D. K. (1994). Intelligent decision support system (DSS) for the selection process of alternative machines for flexible manufacturing systems (FMS). Computers in Industry, 25, 131–143.Google Scholar
  41. Taha, Z., & Rostam, S. (2011). Axiomatic design principles in analyzing the ergonomics design parameter of a virtual environment. The International Journal of Advanced Manufacturing Technology, 57, 719–733Google Scholar
  42. Wang, T. Y., & Parkan, C. (2006). Two new approaches for assessing the weights of fuzzy opinions in group decison analysis. Information Sciences, 176, 3538–3555.CrossRefGoogle Scholar
  43. Wang, T. Y., Shaw, C. F., & Chen, Y. L. (2000). Machine selection in flexible manufacturing cell: A fuzzy multiple attribute decision making approach. International Journal of Production Research, 38, 2079–2097.CrossRefGoogle Scholar
  44. Yao, J. S., & Chiang, J. (2003). Inventory without backorder with fuzzy total cost and fuzzy storing cost defuzzified by centroid and signed distance. European Journal of Operational Research, 148, 401–409.CrossRefGoogle Scholar
  45. Yetton, P., & Botter, P. (1983). The relationships among group size, member ability, social decision schemes, and performance. Organizational Behavior and Human Performance, pp. 145–159.Google Scholar
  46. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.CrossRefGoogle Scholar
  47. Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20, 165–179.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Business AdministrationRecep Tayyip Erdogan UniversityRizeTurkey

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