Abstract
This study used standardized problem and solution models of Problem Characteristic Array and Solution Arrays to render quantitative calculations convenient for objectively obtaining the solution models of the corresponding problem models. A computational process was established to rapidly obtain all the relevant solution models and to objectively arrange them with priorities based on expected problem-solving potential for the given problems. This study extended a quantitative way to identify TRIZ solution models based on fuzzy and crisp similarity measures to automatically and objectively identify possible solution models to the given problems. The use of quantitative methods allows the users quickly and objectively obtaining solution models to a problem with priority based on solved data base which is the accumulation of many expert knowledge and experiences instead of individual expert’s judgment. The contributions of this study include: (1) Applying fuzzy and crisp similarity calculation methods to the problem-solving process of trends of evolution tool, generating solutions based on objective and rapid calculation and display it with priority. (2) Developing an objective and repeatable trend identification software system with high hit rate and feasible solutions substantially higher than randomly selected solutions. (3) Providing a way to continually accumulate expert knowledge and experience and integrating more expert-solved cases to provide a rapid, objective, and effective problem-solving system. This allows a continuous learning system which uses cumulative knowledge from many experts’ solved cases objectively instead of otherwise knowledge from individual experts. The method represents new ways of identifying prioritized solution models using quantitate measures instead of traditional qualitative reasoning. Keyword: TRIZ, Trend of Evolution, Model of Problem, Model of Solution, Fuzzy, Similarity