Research on subsequent decision for the fuzzy comprehensive evaluation based on improved costs

If a system is not up to the standard in an evaluation, what solution should be selected to amend the system is a subsequent decision problem after evaluation. The subsequent decision for fuzzy comprehensive evaluation is studied from the perspective of economics, namely, the evaluation threshold requirements are met at the least cost. Firstly, the models of score cost, score unit cost, and improved cost are established. Based on the three models, each index score is encoded as a genetic information. The genetic algorithm is used to iteratively calculate an index score, which could meet the requirements of fuzzy evaluation score threshold with the least improved cost. Finally, the iteration process and results are analyzed, an an index modification priority model is established. When the budget is tight or the system is difficult to be operated, the model could be used to decide the priority for modifying the indexes. The system reaches a set standard and the modification sequence of the indexes through 2000 iterative calculations The proposed method can be used for the conventional evaluation, such as gray evaluation, extenics assessment and set pair analysis.