Robot selection using a fuzzy regression-based decision-making approach

International Journal of Production Research

Volumn 50, Issue 23, 2012, Pages 6826-6834

  Karsak, E Ertugrul ^a   Sener, Zeynep ^a   Dursun, Mehtap ^a  

^a GALATASARAY UNIVERSITY   (Turkey)

Author keywords

advanced manufacturing technology; decision support systems; fuzzy regression; robot selection

Indexed keywords

ADVANCED MANUFACTURING TECHNOLOGIES; ALTERNATIVE APPROACH; ANALYTICAL METHOD; DATA SETS; DECISION MODELS; FUZZY LINEAR REGRESSION; FUZZY REGRESSIONS; HIGH-QUALITY PRODUCTS; MANUFACTURING FIRMS; ROBOT SELECTION; STATISTICAL REGRESSION;

ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS; INDUSTRIAL ROBOTS; LINEAR REGRESSION;

MANUFACTURE;

EID: 84868545691     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207543.2011.627886     Document Type: Article

References (31)

1. Agrawal, V.P., Kohli, V., and Gupta, S., 1991. Computer aided robot selection: The 'multiple attribute decision making' approach. International Journal of Production Research, 29 (8), 1629-1644.
2. Baker, R.C. and Talluri, S., 1997. A closer look at the use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 32 (1), 101-108. (Pubitemid 127422472)
3. Bhattacharya, A., Sarkar, B., and Mukherjee, S.K., 2005. Integrating AHP with QFD for robot selection under requirement perspective. International Journal of Production Research, 43 (17), 3671-3685. (Pubitemid 41648303)
4. Braglia, M. and Gabbrielli, R., 2000. Dimensional analysis for investment selection in industrial robots. International Journal of Production Research, 38 (18), 4843-4848. (Pubitemid 35401881)
5. Chatterjee, P., Athawale, V.M., and Chakraborty, S., 2010. Selection of industrial robots using compromise ranking and outranking methods. Robotics and Computer-Integrated Manufacturing, 26 (5), 483-489.
6. Huang, P.Y. and Ghandforoush, P., 1984. Procedures given for evaluating, selecting robots. Industrial Engineering, 16 (4), 44-48.
7. Imany, M.M. and Schlesinger, R.J., 1989. Decision models for robot selection: A comparison of ordinary least squares and linear goal programming methods. Decision Sciences, 20 (1), 40-53.
8. Karsak, E.E., 1998. A two-phase robot selection procedure. Production Planning & Control, 9 (7), 675-684. (Pubitemid 128624359)
9. Karsak, E.E., 1999. A DEA-based robot selection procedure incorporating fuzzy criteria values. In: Proceedings of the IEEE international conference on systems, man, and cybernetics, Tokyo, Japan, 1073-1078.
10. Karsak, E.E., 2008. Robot selection using an integrated approach based on quality function deployment and fuzzy regression. International Journal of Production Research, 46 (3), 723-738. (Pubitemid 350136946)
11. Karsak, E.E. and 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 (8), 1537-1554. (Pubitemid 41633047)
12. Karsak, E.E. and Ahiska, S.S., 2008. An improved common weight MCDM model for technology selection. International Journal of Production Research, 46 (24), 6933-6944.
13. Karsak, E.E. and Tolga, E., 2001. Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments. International Journal of Production Economics, 69 (1), 49-64. (Pubitemid 32029981)
14. Kentli, A. and 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.
15. Khouja, M., 1995. The use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 28 (1), 123-132.
16. Khouja, M. and Booth, D.E., 1991. A decision model for the robot selection problem using robust regression. Decision Sciences, 22 (3), 656-662.
17. Khouja, M.J. and Kumar, R.L., 1999. An options view of robot performance parameters in a dynamic environment. International Journal of Production Research, 37 (6), 1243-1257.
18. Kim, K.J., Moskowitz, H., and Koksalan, M., 1996. Fuzzy versus statistical linear regression. European Journal of Operational Research, 92 (2), 417-434. (Pubitemid 126372242)
19. Koulouriotis, D.E. and 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.
20. Liang, G.S. and Wang, M.J.J., 1993. A fuzzy multi-criteria decision-making approach for robot selection. Robotics and Computer-Integrated Manufacturing, 10 (4), 267-274.
21. Offodile, O.F. and Ugwu, K., 1991. Evaluating the effect of speed and payload on robot repeatability. Robotics and Computer- Integrated Manufacturing, 8 (1), 27-33.
22. Parkan, C. and Wu, M.L., 1999. Decision-making and performance measurement models with applications to robot selection. Computers & Industrial Engineering, 36 (3), 503-523. (Pubitemid 129303999)
23. Peters, G., 1994. Fuzzy linear regression with fuzzy intervals. Fuzzy Sets and Systems, 63 (1), 45-55.
24. Rao, R.V., Patel, B.K., and 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.
25. Redden, D.T. and Woodall, W.H., 1994. Properties of certain fuzzy linear regression methods. Fuzzy Sets and Systems, 64 (3), 361-375.
26. Shih, H.S., 2008. Incremental analysis for MCDM with an application to group TOPSIS. European Journal of Operational Research, 186 (2), 720-734.
27. Talluri, S. and Yoon, K.P., 2000. A cone-ratio DEA approach for AMT justification. International Journal of Production Economics, 66 (2), 119-129.
28. Tanaka, H. and Guo, P., 1999. Possibilistic data analysis for operations research. New York: Physica.
29. Tanaka, H., Uejima, S., and Asai, K., 1982. Linear regression analysis with fuzzy model. IEEE Transactions on System, Man and Cybernetics, 12 (6), 903-907. (Pubitemid 13493493)
30. Tanaka, H. and Watada, J., 1988. Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets and Systems, 27 (3), 275-289.
31. Vahdani, B., Mousavi, S.M., and Tavakkoli-Moghaddam, R., 2011. Group decision making based on novel fuzzy modified TOPSIS method. Applied Mathematical Modelling, doi:10.1016/j.apm.2011.02.040.