A new method for diagnostic problem solving based on a fuzzy abductive inference model and the tabu search approach

In this paper, the well developed parsimonious set covering theory based abductive inference model for diagnostic problem solving is extended, in order to deal with degrees of cause-and-effect relationship between disorders and manifestations, and degrees of manifestations. A new fuzzy abductive inference model capable of handling these problems is developed, and a new criterion for describing the relative plausibility of different diagnosis hypotheses proposed. Based on this criterion, the diagnostic problem is then formulated as a 0–1 integer programming problem, and a tabu search (TS) approach is presented for solving the problem. Three sample studies are served for demonstrating the reasonableness of the developed fuzzy abductive inference model and the computational efficiency of the TS based method.