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Integration of Fuzzy Shannon’s Entropy with fuzzy TOPSIS for industrial robotic system selection

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2012
DOI: 
http://dx.doi.org/10.3926/jiem.397

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Purpose: The aim of this study is applying a new method for Industrial robotic system selection. Design/methodology/approach: In this paper, the weights of each criterion are calculated using fuzzy Shannon's Entropy. After that, fuzzy TOPSIS is utilized to rank the alternatives. After that we compare the result of Fuzzy TOPSIS with Fuzzy VIKOR method. Then we select the best Industrial Robotic System based on these results. Findings: The outcome of this research is ranking and selecting industrial robotic systems with the help of Fuzzy Shannon's Entropy and Fuzzy TOPSIS techniques. Originality/value: This paper offers a new integrated method for industrial robotic system selection.
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FULL TEXT (PDF): 
PDF icon arastrmx_141136_5_pp_102-114.pdf
  • 1
102-114
  • English

REFERENCES

References: 

Ashtiani, B., Haghighirad, F., Makui, A., & Montazer, G. A. (2009).Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets. Applied Soft Computing, 9(2), 457-461. http://dx.doi.org/10.1016/j.asoc.2008.05.005
Booth, D. E., Khouja, M., & Hu, M. (1992).A robust multivariate statistical procedure for evaluation and selection of industrial robots.International Journal of Operations & Production Management, 12, 15-24.
http://dx.doi.org/10.1108/01443579210009023
Boubekri, N., Sahoui, M., & Lakrib, C. (1991).Development of an expert system for industrial robot selection.Computers & Industrial Engineering, 20, 119-127. http://dx.doi.org/10.1016/0360-8352(91)90047-ABüyükozkan,
G.
, Feyziog-lu, O., & Nebol, E. (2007).Selection of the strategic alliance partner in logistics value chain. International Journal of Production Economics, 113(1), 148-158.
Hosseinzadeh Lotfi, F., & Fallahnejad, R. (2010). Imprecise Shannon's entropy and multi attribute decision making. Entropy, 12, 53-62. http://dx.doi.org/10.3390/e12010053
Karsak, E. E. (2002). Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives.International Journal of Production Research, 40(13), 3167-3181. http://dx.doi.org/10.1080/00207540210146062
Kaufmann, A., & Gupta, M. M. (1988).Fuzzy mathematical models in engineering and management science. Amsterdam: North-Holland.
Khouja, M. (1995).The use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 28, 123-132. http://dx.doi.org/10.1016/0360-8352(94)00032-I
Khouja, M., & Offodile, O. F. (1994). The industrial robots selection problem: A literature review and directions for future research. IIE Transactions, 26, 50-61. http://dx.doi.org/10.1080/07408179408966618
Liang, G. H., Wang, M. J. (1993).A fuzzy multi-criteria decision-making approach for robot selection. Robotics and Computer Aided Manufacturing, 10, 267-274. http://dx.doi.org/10.1016/0736-5845(93)90040-Q
Rao, R. V. (2007). Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods. London: Springer.
Wang, M. J., Singh, H. P., & Huang, W. V. (1991).A decision support system for robot selection. Decision Support Systems, 7, 273-283. http://dx.doi.org/10.1016/0167-9236(91)90044-C
Wang, T. C., & Chang, T. H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33, 870-880. http://dx.doi.org/10.1016/j.eswa.2006.07.003
Yang, T., & Hung, C. C. (2007). Multiple-attribute decision making methods for plant layout design problem. Robotics and Computer-Integrated Manufacturing, 23(1), 126-137. http://dx.doi.org/10.1016/j.rcim.2005.12.002Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences, 8(3), 199-249. http://dx.doi.org/10.1016/0020-0255(75)90036-5

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