No abstract

No abstract

This paper points out the importance of fuzzy preference by using a teaching course assignment problem as a case study. The model with fuzzy teaching preference provides a more satisfactory solution to a course assignment problem than assigning arbitrary weights. A method for improving a fuzzy membership function by using sensitivity analysis is devised. The method with fuzzy preferences is compared with a model using weighted probabilities.

Some uncertainties can be represented by random sets, while some others may be in the form of probability intervals. Even though random sets and probability intervals are defined differently, we may be able to represent a given probability interval as a unique random set when this probability interval satisfies some conditions. In this paper, we present these conditions which will check whether or not there is a unique random set expressing the same information as a given probability interval. We construct the random set when the conditions are satisfied. We also give examples of when a user should use a random set over a given probability interval.

No abstract