How to cite this paper
Khandekar, A & Chakraborty, S. (2015). Selection of industrial robot using axiomatic design principles in fuzzy environment.Decision Science Letters , 4(2), 181-192.
Refrences
Athawale, V.M., & Chakraborty, S. (2011). A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection, International Journal of Industrial Engineering Computations, 2(4), 831-850.
Alinezhad, A., & Amini, M. (2014). Practical common weight maxmin approach for technology selection. Journal of Fundamental and Applied Sciences, 6(1), 31-47.
Bahadir, M.C., & Satoglu, S.I. (2012). A decision support system for robot selection based on axiomatic design principles. Proc. Int. Conf. on Industrial Engineering and Operations Management, Turkey, 674-683.
Bhangale, P.P., Agrawal, V.P., & Saha, S.K. (2004). Attribute based specification, comparison and selection of a robot. Mechanism and Machine Theory, 39(12), 1345-1366.
Büyük?zkan, G. & Ersoy, M. ?. (2009). Applying fuzzy decision making approach to IT outsourcing supplier selection. World Academy of Science, Engineering and Technology, 3(7), 382-386.
Celik, M., Kahraman,C., Cebi, S. & Er, I.D. (2009). 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.
Chatterjee, P., Athawale, V.M., & Chakraborty, S. (2010). Selection of industrial robots using compromise ranking and outranking methods. Robotics and Computer-Integrated Manufacturing, 26(5), 483-489.
Devi, K. (2011). Extension of VIKOR method in intuitionistic fuzzy environment for robot selection. Expert Systems with Applications, 38(11), 14163-14168.
Glaser, A. (2008). Industrial Robotics: How to Implement the Right System for Your Plant Automation. New York, Industrial Press, Inc.
?ç, Y.T. (2012). An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies. Robotics and Computer-Integrated Manufacturing, 28(2), 245-256.
?ç, Y.T., Yurdakul, M., & Dengiz, B. (2013). Development of a decision support system for robot selection. Robotics and Computer-Integrated Manufacturing, 29(4), 142-157.
Karsak, E.E., Sener, Z., & Dursun, M. (2012). Robot selection using a fuzzy regression-based decision-making approach. International Journal of Production Research, 50(23), 6826-6834.
Koulouriotis, D.E., & Ketipi, M.K. (2011). A fuzzy digraph method for robot evaluation and selection. Expert Systems with Applications, 38(9), 11901-11910.
Kulak, O. (2005). A decision support system for fuzzy multi-attribute selection of material handling equipments. Expert Systems with Applications, 29(2), 310-319.
Kumar, R., & Garg, R.K. (2010). Optimal selection of robots by using distance based approach method. Robotics and Computer-Integrated Manufacturing, 26(5), 500-506.
Mondal, S., & Chakraborty, S. (2013). A solution to robot selection problems using data envelopment analysis. International Journal of Industrial Engineering Computations, 4(3), 355-372.
Rao, R.V., Patel, B.K., & Parnichkun, M. (2011). Industrial robot selection using a novel decision making method considering objective and subjective preferences. Robotics and Autonomous Systems, 59(6), 367-375.
Rashid, T., Beg, I., & Husnine, S.M. (2014). Robot selection by using generalized interval-valued fuzzy numbers with TOPSIS. Applied Soft Computing, 21, 462-468.
Singh, A., & Saha, R. (2014). Selection and comparison of industrial robots for packaging and palletizing using graph theory. International Journal of Applied Engineering Research, 9(5), 505-509.
Suh, N.P. (1990). The Principles of Design. Oxford University Press, New York.
Suh, N.P. (2001). Axiomatic Design: Advances and Applications. Oxford University Press, New York.
Tao, L., Chen, Y., Liu, X., & Wang, X. (2012). An integrated multiple criteria decision making model applying axiomatic fuzzy set theory. Applied Mathematical Modelling, 36(10), 5046-5058.
Vahdani, B., Mousavi, S.M., & Tavakkoli-Moghaddam, R. (2011). Group decision making based on novel fuzzy modified TOPSIS method. Applied Mathematical Modelling, 35(9), 4257-4269.
Vahdani, B., Tavakkoli-Moghaddam, R., Mousavi, S.M., & Ghodratnama, A. (2013). Soft computing based on new interval-valued fuzzy modified multi-criteria decision-making method. Applied Soft Computing, 13(1), 165-172.
Vahdani, B., Mousavi, S.M., Tavakkoli-Moghaddam, R., Ghodratnama, A., & Mohammadi, M. (2014). Robot selection by a multiple criteria complex proportional assessment method under an interval-valued fuzzy environment. International Journal of Advanced Manufacturing Technology, 73(5-8), 687-697.
Yuen, K.K.F. (2014). Combining compound linguistic ordinal scale and cognitive pair wise comparison in the rectified fuzzy TOPSIS method for group decision making. Fuzzy Optimization and Decision Making, 13(1), 105-130.
Zadeh. L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
Alinezhad, A., & Amini, M. (2014). Practical common weight maxmin approach for technology selection. Journal of Fundamental and Applied Sciences, 6(1), 31-47.
Bahadir, M.C., & Satoglu, S.I. (2012). A decision support system for robot selection based on axiomatic design principles. Proc. Int. Conf. on Industrial Engineering and Operations Management, Turkey, 674-683.
Bhangale, P.P., Agrawal, V.P., & Saha, S.K. (2004). Attribute based specification, comparison and selection of a robot. Mechanism and Machine Theory, 39(12), 1345-1366.
Büyük?zkan, G. & Ersoy, M. ?. (2009). Applying fuzzy decision making approach to IT outsourcing supplier selection. World Academy of Science, Engineering and Technology, 3(7), 382-386.
Celik, M., Kahraman,C., Cebi, S. & Er, I.D. (2009). 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.
Chatterjee, P., Athawale, V.M., & Chakraborty, S. (2010). Selection of industrial robots using compromise ranking and outranking methods. Robotics and Computer-Integrated Manufacturing, 26(5), 483-489.
Devi, K. (2011). Extension of VIKOR method in intuitionistic fuzzy environment for robot selection. Expert Systems with Applications, 38(11), 14163-14168.
Glaser, A. (2008). Industrial Robotics: How to Implement the Right System for Your Plant Automation. New York, Industrial Press, Inc.
?ç, Y.T. (2012). An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies. Robotics and Computer-Integrated Manufacturing, 28(2), 245-256.
?ç, Y.T., Yurdakul, M., & Dengiz, B. (2013). Development of a decision support system for robot selection. Robotics and Computer-Integrated Manufacturing, 29(4), 142-157.
Karsak, E.E., Sener, Z., & Dursun, M. (2012). Robot selection using a fuzzy regression-based decision-making approach. International Journal of Production Research, 50(23), 6826-6834.
Koulouriotis, D.E., & Ketipi, M.K. (2011). A fuzzy digraph method for robot evaluation and selection. Expert Systems with Applications, 38(9), 11901-11910.
Kulak, O. (2005). A decision support system for fuzzy multi-attribute selection of material handling equipments. Expert Systems with Applications, 29(2), 310-319.
Kumar, R., & Garg, R.K. (2010). Optimal selection of robots by using distance based approach method. Robotics and Computer-Integrated Manufacturing, 26(5), 500-506.
Mondal, S., & Chakraborty, S. (2013). A solution to robot selection problems using data envelopment analysis. International Journal of Industrial Engineering Computations, 4(3), 355-372.
Rao, R.V., Patel, B.K., & Parnichkun, M. (2011). Industrial robot selection using a novel decision making method considering objective and subjective preferences. Robotics and Autonomous Systems, 59(6), 367-375.
Rashid, T., Beg, I., & Husnine, S.M. (2014). Robot selection by using generalized interval-valued fuzzy numbers with TOPSIS. Applied Soft Computing, 21, 462-468.
Singh, A., & Saha, R. (2014). Selection and comparison of industrial robots for packaging and palletizing using graph theory. International Journal of Applied Engineering Research, 9(5), 505-509.
Suh, N.P. (1990). The Principles of Design. Oxford University Press, New York.
Suh, N.P. (2001). Axiomatic Design: Advances and Applications. Oxford University Press, New York.
Tao, L., Chen, Y., Liu, X., & Wang, X. (2012). An integrated multiple criteria decision making model applying axiomatic fuzzy set theory. Applied Mathematical Modelling, 36(10), 5046-5058.
Vahdani, B., Mousavi, S.M., & Tavakkoli-Moghaddam, R. (2011). Group decision making based on novel fuzzy modified TOPSIS method. Applied Mathematical Modelling, 35(9), 4257-4269.
Vahdani, B., Tavakkoli-Moghaddam, R., Mousavi, S.M., & Ghodratnama, A. (2013). Soft computing based on new interval-valued fuzzy modified multi-criteria decision-making method. Applied Soft Computing, 13(1), 165-172.
Vahdani, B., Mousavi, S.M., Tavakkoli-Moghaddam, R., Ghodratnama, A., & Mohammadi, M. (2014). Robot selection by a multiple criteria complex proportional assessment method under an interval-valued fuzzy environment. International Journal of Advanced Manufacturing Technology, 73(5-8), 687-697.
Yuen, K.K.F. (2014). Combining compound linguistic ordinal scale and cognitive pair wise comparison in the rectified fuzzy TOPSIS method for group decision making. Fuzzy Optimization and Decision Making, 13(1), 105-130.
Zadeh. L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.