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Efficient Multiple Attribute Group Decision Making Models with Correlation Coefficient of Vague Sets

Efficient Multiple Attribute Group Decision Making Models with Correlation Coefficient of Vague Sets

John Robinson, Henry Amirtharaj
Copyright: © 2014 |Volume: 5 |Issue: 3 |Pages: 23
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781466656031|DOI: 10.4018/ijoris.2014070102
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MLA

Robinson, John, and Henry Amirtharaj. "Efficient Multiple Attribute Group Decision Making Models with Correlation Coefficient of Vague Sets." IJORIS vol.5, no.3 2014: pp.27-49. http://doi.org/10.4018/ijoris.2014070102

APA

Robinson, J. & Amirtharaj, H. (2014). Efficient Multiple Attribute Group Decision Making Models with Correlation Coefficient of Vague Sets. International Journal of Operations Research and Information Systems (IJORIS), 5(3), 27-49. http://doi.org/10.4018/ijoris.2014070102

Chicago

Robinson, John, and Henry Amirtharaj. "Efficient Multiple Attribute Group Decision Making Models with Correlation Coefficient of Vague Sets," International Journal of Operations Research and Information Systems (IJORIS) 5, no.3: 27-49. http://doi.org/10.4018/ijoris.2014070102

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Abstract

A new approach for multiple attribute group decision making (MAGDM) problems where the attribute weights and the expert weights are real numbers and the attribute values take the form of vague values, is presented in this paper. Since families of ordered weighted averaging (OWA) operators are available in the literature, and only a few available for vague sets, the vague ordered weighted averaging (VOWA) operator and the induced vague ordered weighted averaging (IVOWA) operator are introduced in this paper and utilized for aggregating the vague information. The correlation coefficient for vague sets is used for ranking the alternatives and a new MAGDM model is developed based on the IVOWA operator and the vague weighted averaging (VWA) operator. In addition to the proposed model, two different models are proposed based on Linguistic Quantifiers for the situation when the expert weights are completely unknown. An illustrative example is given and a comparison is made between the models to demonstrate the applicability of the proposed approach of MAGDM.

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