RANKING RANGE BASED APPROACH TO MADM UNDER INCOMPLETE CONTEXT AND ITS APPLICATION IN VENTURE INVESTMENT EVALUATION

Abstract

In real-world Multiple Attribute Decision Making (MADM) problem, the attribute weights information may be unknown or partially known. Several approaches have been suggested to address this kind of incomplete MADM problem. However, these approaches depend on the determination of attribute weights, and setting different attribute weight vectors may result in different ranking positions of alternatives. To deal with this issue, this paper develops a novel MADM approach: the ranking range based MADM approach. In the novel MADM approach, the minimum and maximum ranking positions of every alternative are generated using several optimization models, and the average ranking position of every alternative is produced applying the Monte Carlo simulation method. Then, the minimum, maximum and average ranking positions of the alternative are integrated into a new ranking position of the alternative. This novel approach is capable of dealing with venture investment evaluation problems. However, in the venture investment evaluation process, decision makers will present different risk attitudes. To deal with this issue, two ranking range based MADM approaches with risk attitudes are further designed. A case study and a simulation experiment are presented to show the validity of the proposal.