An Approach to Hybrid Multiple Attribute Decision-Making with Time Series Based on Incomplete Information on Weights

Multiple attribute decision-making (MADM) with incomplete information are one of the important research areas in decision analysis. This paper investigates a type of multiple attribute decision-making problems with time series, in which the performances of the alternatives on attributes are represented in three different formats, namely: 1) precise number; 2) probability density function; and 3) fuzzy linguistic judgment. With incomplete information on both attribute weights and time weights, optimization models are constructed to determine the range of the distance between each alternative and the ideal solution (anti-ideal solution). Further, a ranking approach based on the TOPSIS method is proposed for the problem. This paper provides a new way to solve hybrid multiple attribute decision problems with incomplete information.