Minimum Deviation Method for Single-valued Neutrosophic Multiple Attribute Decision Making with Preference Information on Alternatives
Dong-Sheng Xu, Cun Wei, Gui-Wu Wei
Journal of Intelligent Computing, Vol. 9, No. 2, 2018.
DOI: https://doi.org/10.6025/jic/2018/9/1/54-75
Link: http://www.dline.info/jic/fulltext/v9n2/jicv9n2_2.pdf
Abstract:
In this paper, we investigate the single-valued neutrosophic multiple attribute decision making (MADM) problems with preference information on alternatives, some operational laws of single-valued neutrosophic numbers, score function where accuracy function of single-valued neutrosophic numbers are introduced. An optimization model based on
the minimum deviation method, by which the attribute weights can be determined, is established. For the special situations
where the information about attribute weights is completely unknown, we establish another optimization model. By solving
this model, we get a simple and exact formula, which can be used to determine the attribute weights. We utilize the singlevalued
neutrosophic weighted averaging (SVNWA) operator to aggregate the single-valued neutrosophic information
corresponding to each alternative, and then rank the alternatives and select the most desirable one(s) according to the score
function and accuracy function. The method can sufficiently utilize the objective information, and meet decision makersâ€™
subjective preference and can be easily performed on computer. Furthermore, we have extended the above results to interval
neutrosophic environment. Finally, an illustrative example for potential evaluation of emerging technology commercialization
is given to verify the developed approach and to demonstrate its practicality and effectiveness.