Most of data in Multi-attribute decision making (MADM) problems are changeable rather than constant and stable. Therefore, sensitivity analysis after problem solving can effectively contribute to making accurate decisions. In this paper, we offer a new method for sensitivity analysis in multi-attribute decision making problems in which if the weights of one attribute changes, then we can dete...

this article studies the generative grammar approach and some other theories driven from it to lexicon and lexical entries. generative grammar is known as a syntax-based theory, so some aspects of language which have been studied in this theory may have been ignored in other theories. this article shows that more attention has been given to lexicon and lexical entries since 50s. we may claim th...

Since big data sets are structurally complex, high-dimensional, and their attributes exhibit some redundant irrelevant information, the selection, evaluation, combination of those large-scale pose huge challenges to traditional methods. Fuzzy rough have emerged as a powerful vehicle deal with uncertain fuzzy in problems that involve very large number variables be analyzed short time. In order f...

A number of approaches have been proposed to determine the weights for multiple attribute decision making. However, resultant are usually assumed be fixed, making it lack tolerance accommodate variation if patterns subsequent data subject change. This article proposes a method facilitate adjustment weights, which accommodates relevant characteristics. model is first constructed that able expres...

Most of data in a multi attribute decision making (MADM) problem are unstable and changeable, and thus sensitivity analysis can effectively contribute to making proper decisions. Here, we offer a new method for sensitivity analysis of multi-attribute decision making problems so that by changing one element of decision making matrix, we can determine changes in the results of a decision making p...