What is it about?
In the present paper, we defined an algorithmic method for the selection of a robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.
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Why is it important?
Missing data exists in almost every dataset in science. Generally, these missing data need to be estimated or should be collected again from an application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess highly desirable properties of noise tolerance and robustness. However, to collect data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems
Perspectives
Many researchers are very active in solving problems related to incomplete rough fuzzy sets and they have developed many useful results in decision-making problems. In most of the problems, data is not fully available due to unavoidable reasons. Decision-makers have to deal with this incomplete information by either recollecting the data or estimating the data. Hence, a method in which there is no need to estimate the data is described and thus, we can easily make the decision in an incomplete environment. The algorithm is illustrated with the help of an example for the selection of Robots for industrial purposes. It can be noted from the example that although the data is missing for Robot 2 still Robot 2 is selected for industrial applications.
Prof. D S Hooda
Guru Jambheshwar University of Science and Technology
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This page is a summary of: An algorithmic robot selection method for incomplete rough fuzzy set, Research in Statistics, March 2023, Taylor & Francis,
DOI: 10.1080/27684520.2023.2186194.
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