A Novel Intelligent Multi-attribute Three-Way Group Sorting Method Based on Dempster-Shafer Theory

Multi-attribute group sorting (MAGS) has become a popular subject in multi-attribute decision making fields. The optimization preference disaggregation method and the outranking relation method are frequently used to solve this kind of problems. However, when faced with a MAGS with more attributes and alternatives, these methods show their limitations such as the intensive computations and the difficulty to determine the necessary parameters. To overcome these limitations, we here propose an intelligent three-way group sorting method based on Dempster-Shafer theory for obtaining a more credible sorting result. In the proposed method, decision evidences are constructed by computing the fuzzy memberships of an alternative belonging to the decision classes; the famous Dempster combination approach is further used to aggregate these evidences for making the final group sorting. In the end, a simulation example is employed to show the effectiveness of the new method.