How to cite this paper
Athawale, V & 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.
Refrences
Agrawal, V.P., Kohil, V., & Gupta, A. (1991). Computer aided robot selection: the ‘multiple attribute decision making’ approach. International Journal of Production Research, 29, 1629-1644.
Baker, R.C., & Talluri, S. (1997). A closer look at the use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 32, 101-108.
Behzadian, M., Kazemzadeh, R.B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 200, 198-215.
Bhangale, P.P., Agrawal, V.P., & Saha, S.K. (2004). Attribute based specification, comparison and selection of a robot. Mechanism and Machine Theory, 39, 1345-1366.
Bhattacharya, A., Sarkar, B., & Mukherjee, S.K. (2005). Integrating AHP with QFD for robot selection under requirement perspective. International Journal of Production Research, 43, 3671-685.
Braglia, M., & Petroni, A. (1999) Evaluating and selecting investments in industrial robots. International Journal of Production Research, 37, 4157-4178.
Braglia, M., & Gabbrielli, R. (2000). Dimensional analysis for investment selection in industrial robots. International Journal of Production Research, 38, 4843-4848.
Caterino, N., Iervolino, I., Manfredi, G., & Cosenza, E. (2009). Comparative analysis of multi-criteria decision-making methods for seismic structural retrofitting. Computer-Aided Civil and Infrastructure Engineering, 24, 432-445.
Chatterjee, P., Athawale, V.M., & Chakraborty, S. (2010). Selection of industrial robots using compromise ranking and outranking methods. Robotics and Computer-Integrated Manufacturing, 26, 483-489.
Chu, T-C., & Lin, Y-C. (2003). A fuzzy TOPSIS method for robot selection. International Journal of Advanced Manufacturing Technology, 21, 284-290.
Goh, C-H., Tung, Y-C.A., & Cheng, C-H. (1996). A revised weighted sum decision model for robot selection. Computers & Industrial Engineering, 30, 193-199.
Goh, C-H. (1997). Analytic hierarchy process for robot selection. Journal of Manufacturing Systems, 16, 381-386.
Guitouni, A., & Martel, J-M. (1998). Tentative guidelines to help choosing an appropriate MCDA method. European Journal of Operational Research, 109, 501-521.
Hajkowicz, S., & Higgins, A. (2008). A comparison of multiple criteria analysis techniques for water resource management. European Journal of Operational Research, 184, 255-265.
Kahraman, C., Çevik, S., Ates, N.Y., & Gülbay, M. (2007). Fuzzy multi-criteria evaluation of industrial robotic systems. Computers & Industrial Engineering, 52, 414-433.
Karni, R., Sanchez, P., & Rao Tummala, V.M. (1990). A comparative study of multiattribute decision making methodologies. Theory and Decision, 29, 203-222.
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. (2008). Robot selection using an integrated approach based on quality function deployment and fuzzy regression. International Journal of Production Research, 46, 723-738.
Kentli, A., & Kar, A.K. (2011). A satisfaction function and distance measure based multi-criteria robot selection procedure. International Journal of Production Research, DOI: 10.1080/00207543.2010.530623.
Khouja, M. (1995). The use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 28, 123-132.
Khouja, M.J., & Kumar, R.L. (1999). An options view of robot performance parameters in a dynamic environment. International Journal of Production Research, 37, 1243-1257.
Koulouriotis, D.E., & Ketipi, M.K. (2011). A fuzzy digraph method for robot evaluation and selection. Expert Systems with Applications, doi: 10.1016/j.eswa.2011.03.082.
Kumar, R., & Garg, R.K. (2010). Optimal selection of robots by using distance based approach method. Robotics and Computer-Integrated Manufacturing, 26, 500-506.
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.
Parkan, C., & Wu, M-L. (1999). Decision-making and performance measurement models with applications to robot selection. Computers & Industrial Engineering, 36, 503-523.
Raju, K.S., & Pillai, C.R.S. (1999). Multicriterion decision making in river basin planning and development. European Journal of Operational Research, 112, 249-257.
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, 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. (2009) Decision making in the manufacturing environment using an improved PROMETHEE method. International Journal of Production Research, 48, 4665-82.
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, doi:10.1016/j.robot.2011.01.005
Roy, B., & Vincke, P. (1981). Multi-criteria analysis: Survey and new directions. European Journal of Operational Research, 8, 207-218.
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.
Singh, D., & Rao, R.V. (2011). A hybrid multiple attribute decision making method for solving problems of industrial environment. International Journal of Industrial Engineering Computations, doi: 10.5267/j.ijiec.2011.02.001.
Talluri, S., & Yoon, K.P. (2000). A cone-ratio DEA approach for AMT justification. International Journal of Production Economics, 66, 119-129.
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, 507-529.
Zeleny, M. (2002). Multiple criteria decision making. New York: McGraw Hill.
Zhao, L., Tsujimura, Y., & Gen, M. (1996). Genetic algorithm for robot selection and work station assignment problem. Computers & Industrial Engineering, 31, 599-602.
Baker, R.C., & Talluri, S. (1997). A closer look at the use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 32, 101-108.
Behzadian, M., Kazemzadeh, R.B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 200, 198-215.
Bhangale, P.P., Agrawal, V.P., & Saha, S.K. (2004). Attribute based specification, comparison and selection of a robot. Mechanism and Machine Theory, 39, 1345-1366.
Bhattacharya, A., Sarkar, B., & Mukherjee, S.K. (2005). Integrating AHP with QFD for robot selection under requirement perspective. International Journal of Production Research, 43, 3671-685.
Braglia, M., & Petroni, A. (1999) Evaluating and selecting investments in industrial robots. International Journal of Production Research, 37, 4157-4178.
Braglia, M., & Gabbrielli, R. (2000). Dimensional analysis for investment selection in industrial robots. International Journal of Production Research, 38, 4843-4848.
Caterino, N., Iervolino, I., Manfredi, G., & Cosenza, E. (2009). Comparative analysis of multi-criteria decision-making methods for seismic structural retrofitting. Computer-Aided Civil and Infrastructure Engineering, 24, 432-445.
Chatterjee, P., Athawale, V.M., & Chakraborty, S. (2010). Selection of industrial robots using compromise ranking and outranking methods. Robotics and Computer-Integrated Manufacturing, 26, 483-489.
Chu, T-C., & Lin, Y-C. (2003). A fuzzy TOPSIS method for robot selection. International Journal of Advanced Manufacturing Technology, 21, 284-290.
Goh, C-H., Tung, Y-C.A., & Cheng, C-H. (1996). A revised weighted sum decision model for robot selection. Computers & Industrial Engineering, 30, 193-199.
Goh, C-H. (1997). Analytic hierarchy process for robot selection. Journal of Manufacturing Systems, 16, 381-386.
Guitouni, A., & Martel, J-M. (1998). Tentative guidelines to help choosing an appropriate MCDA method. European Journal of Operational Research, 109, 501-521.
Hajkowicz, S., & Higgins, A. (2008). A comparison of multiple criteria analysis techniques for water resource management. European Journal of Operational Research, 184, 255-265.
Kahraman, C., Çevik, S., Ates, N.Y., & Gülbay, M. (2007). Fuzzy multi-criteria evaluation of industrial robotic systems. Computers & Industrial Engineering, 52, 414-433.
Karni, R., Sanchez, P., & Rao Tummala, V.M. (1990). A comparative study of multiattribute decision making methodologies. Theory and Decision, 29, 203-222.
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. (2008). Robot selection using an integrated approach based on quality function deployment and fuzzy regression. International Journal of Production Research, 46, 723-738.
Kentli, A., & Kar, A.K. (2011). A satisfaction function and distance measure based multi-criteria robot selection procedure. International Journal of Production Research, DOI: 10.1080/00207543.2010.530623.
Khouja, M. (1995). The use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 28, 123-132.
Khouja, M.J., & Kumar, R.L. (1999). An options view of robot performance parameters in a dynamic environment. International Journal of Production Research, 37, 1243-1257.
Koulouriotis, D.E., & Ketipi, M.K. (2011). A fuzzy digraph method for robot evaluation and selection. Expert Systems with Applications, doi: 10.1016/j.eswa.2011.03.082.
Kumar, R., & Garg, R.K. (2010). Optimal selection of robots by using distance based approach method. Robotics and Computer-Integrated Manufacturing, 26, 500-506.
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.
Parkan, C., & Wu, M-L. (1999). Decision-making and performance measurement models with applications to robot selection. Computers & Industrial Engineering, 36, 503-523.
Raju, K.S., & Pillai, C.R.S. (1999). Multicriterion decision making in river basin planning and development. European Journal of Operational Research, 112, 249-257.
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, 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. (2009) Decision making in the manufacturing environment using an improved PROMETHEE method. International Journal of Production Research, 48, 4665-82.
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, doi:10.1016/j.robot.2011.01.005
Roy, B., & Vincke, P. (1981). Multi-criteria analysis: Survey and new directions. European Journal of Operational Research, 8, 207-218.
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.
Singh, D., & Rao, R.V. (2011). A hybrid multiple attribute decision making method for solving problems of industrial environment. International Journal of Industrial Engineering Computations, doi: 10.5267/j.ijiec.2011.02.001.
Talluri, S., & Yoon, K.P. (2000). A cone-ratio DEA approach for AMT justification. International Journal of Production Economics, 66, 119-129.
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, 507-529.
Zeleny, M. (2002). Multiple criteria decision making. New York: McGraw Hill.
Zhao, L., Tsujimura, Y., & Gen, M. (1996). Genetic algorithm for robot selection and work station assignment problem. Computers & Industrial Engineering, 31, 599-602.