How to cite this paper
Azimi, M., Taghizadeh, H., Farahmand, N & Pourmahmoud, J. (2014). Selection of industrial robots using the Polygons area method.International Journal of Industrial Engineering Computations , 5(4), 631-646.
Refrences
Agrawal, V. P., Kohli, V., & Gupta, S. (1991). Computer aided robot selection: the ‘multiple attribute decision making approach. The International Journal of Production Research, 29(8), 1629-1644.
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.
Azar, A., & Radjab Zadeh, A. (2010). Application Decision MADM Approach. Danesh Publication, 4th Ed.
Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051-13069.
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.
Chatterjee, P., Manikrao Athawale, V., & 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.
Fendel, G., & Spronk, J. (1983). Multiple Criteria Decision Methods and Applications. New York: Spring-Verlag.
Goh, C. H. (1997). Analytic hierarchy process for robot selection. Journal of Manufacturing Systems, 16, 381–386.
Honarmande Azimi, M., & Pourmahmoud, J. (2012). A new multiple attribute decision making method. The 5th International Conference of Iranian Operations Research Society, Tabriz, Iran, 65-67.
Hwang, C. L., & Yoon, K. (1981). Multiple attributes decision making methods and applications. New York: Springer.
Tansel ?ç, Y., Yurdakul, M., & Dengiz, B. (2013). Development of a decision support system for robot selection. Robotics and Computer-Integrated Manufacturing, 29(4), 142-157.
Kahraman, C., Cevik, S., Ates, N. Y., & Gülbay, M. (2007). Fuzzy multi-criteria evaluation of industrial robotic systems. Computers & Industrial Engineering,52(4), 414-433.
Karsak, E. E. (2008). ‘Robot selection using an integrated approach based on quality function deployment and fuzzy regression’. International Journal of Production Research, 46, 723-738.
Karsak, E.E., & Ahiska, S.S. (2005). Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection. International Journal of Production Research, 43, 1537-1554.
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.
Khouja, M. (1995). The use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 28(1), 123-132.
Momeni, M. (2007). New topics in operations research. Tehran: University of Tehran publication, 2nd Ed.
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.
Murray, R. S., & Liu, J. (1968). Mathematical handbook of formulas and tables. Spiegel-Schaum’s outline series.
Myers, J. L., & Well, A. D. (2003). Research Design and Statistical Analysis, 2nd ed., Lawrence Erlbaum.
Opricovic, S. (1998). Multicriteria Optimization of Civil Engineering Systems. Faculty of Civil Engineering, Belgrade.
Opricovic, S., & Tzeng, G. H. (2004). Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156, 445-455.
Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR Method in Comparison with Outranking Methods. European Journal of Operational Research, 178, 514-529.
Rao, P. V., & Baral, S. S. (2011). Attribute based specification, comparison and selection of feed stock for anaerobic digestion using MADM approach. Journal of Hazardous Materials, 186, 2009–2016.
Rao, R. V., & Padmanabhan, K. K. (2006). Selection, identification and comparison of industrial robots using digraph and matrix methods. Robotics and Computer-Integrated Manufacturing, 22(4), 373-383.
Rao, R.V. (2007). Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods. London: Springer-Verlag.
Rao R.V., & Patel B.K. (2010). Decision making in the manufacturing environment using an improved PROMETHEE method. International Journal of Production Research, 48(16), 4665-4682.
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.
Rao, R.V. (2013). Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods, 2nd ed., London: Springer-Verlag.
Saaty, T.L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
Sheskin, D. J. (2004). Handbook of parametric and nonparametric statistical procedures. Chapman and Hall /CRC.
Shivanand, H. K. (2006). Flexible manufacturing system. New Age International.
Singh, D., & Rao, R. (2011). A hybrid multiple attribute decision making method for solving problems of industrial environment. International Journal of Industrial Engineering Computations, 2(3), 631-644.
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.
Triantaphyllou, E., & Mann, S. H. (1989). An examination of the effectiveness of multi-dimensional decision-making methods: A decision-making paradox. Decision Support Systems, 5, 303-312.
Triantaphyllou, E. (1998). Multi-criteria decision making methods: An operation research approach. Encyclopedia of Electrical and Electronic, 15,175-186.
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.
Voogd, H. (1983). Multicriteria Evaluation for Urban and Regional Planning. Pion, London.
Waigaonkar, S., Babu, B. J. C., & Prabhakaran, R. D. (2008). A new approach for resin selection in rotational molding. Journal of Reinforced Plastics and Composites, 27, 1021-1037.
Wang, M. J. J., Singh, H. P., & Huang, W. V. (1991). A decision support system for robot selection. Decision Support Systems, 7(3), 273-283.
Wang, Y. M., Yang, J. B., & Xu, D. L. (2005). A preference aggregation method through the estimation of utility intervals. Computers & Operations Research,32(8), 2027-2049.
Yoon, K. P., & Hwang, C. L. (1995). Multiple attribute decision making: an introduction (Vol. 104). Sage Publications.
Yu, P.L. (1973). A class of solutions for group decision problems. Management Science, 19 (8), 936–946.
Yurdakul, M., & Ic, Y. T. (2009). Application of correlation test to criteria selection for multi criteria decision making (MCDM) models. The International Journal of Advanced Manufacturing Technology, 40(3-4), 403-412.
Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European journal of operational research, 107(3), 507-529.
Zeleny, M. (2002). Multiple Criteria Decision Making. New York: McGraw Hill.
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.
Azar, A., & Radjab Zadeh, A. (2010). Application Decision MADM Approach. Danesh Publication, 4th Ed.
Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051-13069.
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.
Chatterjee, P., Manikrao Athawale, V., & 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.
Fendel, G., & Spronk, J. (1983). Multiple Criteria Decision Methods and Applications. New York: Spring-Verlag.
Goh, C. H. (1997). Analytic hierarchy process for robot selection. Journal of Manufacturing Systems, 16, 381–386.
Honarmande Azimi, M., & Pourmahmoud, J. (2012). A new multiple attribute decision making method. The 5th International Conference of Iranian Operations Research Society, Tabriz, Iran, 65-67.
Hwang, C. L., & Yoon, K. (1981). Multiple attributes decision making methods and applications. New York: Springer.
Tansel ?ç, Y., Yurdakul, M., & Dengiz, B. (2013). Development of a decision support system for robot selection. Robotics and Computer-Integrated Manufacturing, 29(4), 142-157.
Kahraman, C., Cevik, S., Ates, N. Y., & Gülbay, M. (2007). Fuzzy multi-criteria evaluation of industrial robotic systems. Computers & Industrial Engineering,52(4), 414-433.
Karsak, E. E. (2008). ‘Robot selection using an integrated approach based on quality function deployment and fuzzy regression’. International Journal of Production Research, 46, 723-738.
Karsak, E.E., & Ahiska, S.S. (2005). Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection. International Journal of Production Research, 43, 1537-1554.
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.
Khouja, M. (1995). The use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 28(1), 123-132.
Momeni, M. (2007). New topics in operations research. Tehran: University of Tehran publication, 2nd Ed.
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.
Murray, R. S., & Liu, J. (1968). Mathematical handbook of formulas and tables. Spiegel-Schaum’s outline series.
Myers, J. L., & Well, A. D. (2003). Research Design and Statistical Analysis, 2nd ed., Lawrence Erlbaum.
Opricovic, S. (1998). Multicriteria Optimization of Civil Engineering Systems. Faculty of Civil Engineering, Belgrade.
Opricovic, S., & Tzeng, G. H. (2004). Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156, 445-455.
Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR Method in Comparison with Outranking Methods. European Journal of Operational Research, 178, 514-529.
Rao, P. V., & Baral, S. S. (2011). Attribute based specification, comparison and selection of feed stock for anaerobic digestion using MADM approach. Journal of Hazardous Materials, 186, 2009–2016.
Rao, R. V., & Padmanabhan, K. K. (2006). Selection, identification and comparison of industrial robots using digraph and matrix methods. Robotics and Computer-Integrated Manufacturing, 22(4), 373-383.
Rao, R.V. (2007). Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods. London: Springer-Verlag.
Rao R.V., & Patel B.K. (2010). Decision making in the manufacturing environment using an improved PROMETHEE method. International Journal of Production Research, 48(16), 4665-4682.
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.
Rao, R.V. (2013). Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods, 2nd ed., London: Springer-Verlag.
Saaty, T.L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
Sheskin, D. J. (2004). Handbook of parametric and nonparametric statistical procedures. Chapman and Hall /CRC.
Shivanand, H. K. (2006). Flexible manufacturing system. New Age International.
Singh, D., & Rao, R. (2011). A hybrid multiple attribute decision making method for solving problems of industrial environment. International Journal of Industrial Engineering Computations, 2(3), 631-644.
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.
Triantaphyllou, E., & Mann, S. H. (1989). An examination of the effectiveness of multi-dimensional decision-making methods: A decision-making paradox. Decision Support Systems, 5, 303-312.
Triantaphyllou, E. (1998). Multi-criteria decision making methods: An operation research approach. Encyclopedia of Electrical and Electronic, 15,175-186.
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.
Voogd, H. (1983). Multicriteria Evaluation for Urban and Regional Planning. Pion, London.
Waigaonkar, S., Babu, B. J. C., & Prabhakaran, R. D. (2008). A new approach for resin selection in rotational molding. Journal of Reinforced Plastics and Composites, 27, 1021-1037.
Wang, M. J. J., Singh, H. P., & Huang, W. V. (1991). A decision support system for robot selection. Decision Support Systems, 7(3), 273-283.
Wang, Y. M., Yang, J. B., & Xu, D. L. (2005). A preference aggregation method through the estimation of utility intervals. Computers & Operations Research,32(8), 2027-2049.
Yoon, K. P., & Hwang, C. L. (1995). Multiple attribute decision making: an introduction (Vol. 104). Sage Publications.
Yu, P.L. (1973). A class of solutions for group decision problems. Management Science, 19 (8), 936–946.
Yurdakul, M., & Ic, Y. T. (2009). Application of correlation test to criteria selection for multi criteria decision making (MCDM) models. The International Journal of Advanced Manufacturing Technology, 40(3-4), 403-412.
Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European journal of operational research, 107(3), 507-529.
Zeleny, M. (2002). Multiple Criteria Decision Making. New York: McGraw Hill.