Abstract
Based on the concept of “Like Problem, like solutions” in TRIZ theory, the problems with similar problem characteristics are likely to have similar solution characteristics. With a set of known solved problems and their corresponding solutions as a casebase, solving a problem becomes a matter of identifying highly similar known problems in terms of Problem Characteristic Array similarity and integrating the Solution Arrays of the corresponding similar problems to form the set of solution models. In this research, relevant trends identification system using similarity measures were developed. In identifying relevant trend solutions, characteristic attributes of the problem are compared against the characteristic attributes of certain earlier stage of a trend first. If they match, the ensuing stages of the same trend can imply model of solutions as jumping into that stage can provide functions needed to solve the problem. By encoding the 'knowledge' embedded in the trends, a piece of software is written to identify the relevant trends for problem solving quickly and objectively without needing to rely on expert experience and knowledge. K-fold validity verification was used to verify the effectiveness of this method. With 124 known cases and 51 trend examples, the results showed that the solutions recommended by the 10 most likely trends achieved 100% coverage of problem known solutions and is significantly better than randomly selected 10 solutions which covered less than 9% of the known solutions. The contributions of this research include: 1) Opening up a new branch of TRIZ research using mathematical methods to objectively identify model of solutions. 2) Establishing a computer aided problem-solving tool that can automatically and quickly identify the relevant trends for problem solving.