The max-min Delphi method and fuzzy Delphi method via fuzzy integration

Abstract

Abstract The traditional Delphi method is one of the effective methods which enables forecasting by converging a possibility value through the feedback mechanism of the results of questionnaires, based on experts' judgments. Some points needing revision are: (1) By pinpointing the intuition of the first response on the part of experts, feasible inference values need to be extracted so that the quality-oriented and semantic structure of the responses may be analyzed. (2) By removing the effect caused by feedback in the Delphi method, natural and non-converged results need to be acquired; Moreover, two and more repetitive surveys are likely to cause a decline in the response rate, which may produce negative effects in the ensuing analyses. (3) In general, as it is repeated, the survey becomes more costly and time-consuming. In order to resolve these issues, we have identified two kinds of membership functions in regard to ‘the attainable period with a high degree’ and ‘the unattainable period with a high degree’. Next, through the implementation of the Max-Min Fuzzy Delphi Method and the New Delphi Method via Fuzzy Integration, we have developed algorithms which enable forecasting attainable periods. Third, we have applied such algorithms to two concrete questions, compared the result with one obtained from the Delphi method, and ascertained the feasible outcome. While more examination needs to be undertaken, the new methods look valid and applicable to further analyses of other questions and items on questionnaires. While both methods can forecast attainable periods, using these methods simultaneously as well as the traditional Delphi method, may prove a really effective result.