THE MAX-MIN DELPHI METHOD AND FUZZY DELPHI METHOD VIA FUZZY INTEGRATION

The traditional Delphi method is one of the effective methods which en ables forecasting by converging a possibility value through the feedba ck mechanism of the results of questionnaires, based on experts' judgm ents. Some points needing revision are: (1) By pinpointing the intuiti on of the first response on the part of experts, feasible inference va lues need to be extracted so that the quality-oriented and semantic st ructure of the responses may be analyzed. (2) By removing the effect c aused by feedback in the Delphi method, natural and non-converged resu lts need to be acquired; Moreover, two and more repetitive surveys are likely to cause a decline in the response rate, which may produce neg ative effects in the ensuing analyses. (3) In general, as it is repeat ed, the survey becomes more costly and time-consuming. In order to res olve these issues, we have identified two kinds of membership function s in regard to 'the attainable period with a high degree' and 'the una ttainable period with a high degree'. Next, through the implementation of the Max-Min Fuzzy Delphi Method and the New Delphi Method via Fuzz y Integration, we have developed algorithms which enable forecasting a ttainable periods. Third, we have applied such algorithms to two concr ete questions, compared the result with one obtained from the Delphi m ethod, and ascertained the feasible outcome. While more examination ne eds to be undertaken, the new methods look valid and applicable to fur ther analyses of other questions and items on questionnaires. While bo th methods can forecast attainable periods, using these methods simult aneously as well as the traditional Delphi method, may prove a really effective result.