Abstract and Applied Analysis

A Method for Multiple Attribute Decision Making Based on the Fusion of Multisource Information

F. W. Zhang, S. H. Xu, B. J. Wang, and Z. J. Wu

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We propose a new method for the multiple attribute decision making problem. In this problem, the decision making information assembles multiple source data. Two main advantages of this proposed approach are that (i) it provides a data fusion technique, which can efficiently deal with the multisource decision making information; (ii) it can produce the degree of credibility of the entire decision making. The proposed method performs very well especially for the scenario that there exists conflict among the multiple source information. Finally, a traffic engineering example is given to illustrate the effect of our method.

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Abstr. Appl. Anal., Volume 2014 (2014), Article ID 972159, 6 pages.

First available in Project Euclid: 2 October 2014

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Zhang, F. W.; Xu, S. H.; Wang, B. J.; Wu, Z. J. A Method for Multiple Attribute Decision Making Based on the Fusion of Multisource Information. Abstr. Appl. Anal. 2014 (2014), Article ID 972159, 6 pages. doi:10.1155/2014/972159. https://projecteuclid.org/euclid.aaa/1412273213

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  • C. L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications, Springer, New York, NY, USA, 1981.
  • J. S. Dyer, P. C. Fishburn, R. E. Steuer et al., “Multiple criteria decision making, multi-attribute utility theory: the next ten years,” Management Science, vol. 38, no. 5, pp. 645–654, 1992.
  • Z.-P. Fan, J. Ma, and Q. Zhang, “An approach to multiple attribute decision making based on fuzzy preference information on alternatives,” Fuzzy Sets and Systems, vol. 131, no. 1, pp. 101–106, 2002.
  • G.-W. Wei, “Maximizing deviation method for multiple attribute decision making in intuitionistic fuzzy setting,” Knowledge-Based Systems, vol. 21, no. 8, pp. 833–836, 2008.
  • Z. B. Wu and Y. H. Chen, “The maximizing deviation method for group multiple attribute decision making under linguistic environment,” Fuzzy Sets and Systems, vol. 158, no. 14, pp. 1608–1617, 2007.
  • Z. S. Xu, “Multiple-attribute group decision making with different formats of preference information on attributes,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 37, no. 6, pp. 1500–1511, 2007.
  • G.-W. Wei, “Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information,” Expert Systems with Applications, vol. 38, no. 5, pp. 4824–4828, 2011.
  • X. Y. Shao, L. Zhang, L. Gao, and R. Chen, “Fuzzy multiple attributive group decision-making for conflict resolution in collaborative design,” in Fuzzy Systems and Knowledge Discovery, vol. 4223 of Lecture Notes in Computer Science, pp. 990–999, 2006.
  • D.-F. Li, “Compromise ratio method for fuzzy multi-attribute group decision making,” Applied Soft Computing Journal, vol. 7, no. 3, pp. 807–817, 2007.
  • C.-I. Brändén and T. A. Jones, “Between objectivity and subjectivity,” Nature, vol. 343, no. 6260, pp. 687–689, 1990.
  • N. Ford, “Creativity and convergence in information science research: the roles of objectivity and subjectivity, constraint, and control,” Journal of the American Society for Information Science and Technology, vol. 55, no. 13, pp. 1169–1182, 2004.
  • F. Giannessi, P. M. Pardalos, and T. Rapcsak, Optimization Theory, Springer, New York, NY, USA, 2001.
  • E. E. Bassett, Statistics: Problems and Solutions, World Scientific, Singapore, 2000.
  • R. M. Gagné, The Conditions of Learning, Holt, Rinehart and Winston, New York, NY, USA, 1965.
  • Y.-M. Wang, J.-B. Yang, D.-L. Xu, and K.-S. Chin, “The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees,” European Journal of Operational Research, vol. 175, no. 1, pp. 35–66, 2006.
  • F.-W. Zhang and B.-X. Yao, “A method for multiple attribute decision making without weight information,” Pattern Recognition and Artificial Intelligence, vol. 20, no. 1, pp. 69–71, 2007. \endinput