A Bayesian Method for Decision of Weight for MADM Model with Interval Data

The weight in TOPSIS approach (technique for order preference by similarity ideal solution - TOPSIS ) is given by experts or decision makers. The value of weight would be influenced by experts’ subjective judgments. A slight difference in value of weight may result in diversity of order of alternatives. In this paper, a Bayesian method for decision of weight for MADM model with interval data is introduced. The value of weight is decided by prior information (other experts’ knowledge, or numerical simulation etc.) and experts’ knowledge (or decision makers’ experience/ preference). This method effectively takes advantage of experts’ knowledge and avoids the problem with experts’ subjectivity. An illustrative example is showed to explore the applications of proposed method. The method is valuable for field of multi-attribute decision- making with interval data.