Fuzzy Numbers in Cost Range Estimating

Abstract

Range estimating is a simple form of simulating a project estimate by breaking the project into work packages and approximating the variables in each package using statistical distributions. This paper explores an alternate approach to range estimating that is grounded in fuzzy set theory. The approach addresses two shortcomings of Monte Carlo simulation. The first is related to the analytical difficulty associated with fitting statistical distributions to subjective data, and the second relates to the required number of simulation runs to establish a meaningful estimate of a given parameter at the end of the simulation. For applications in cost estimating, the paper demonstrates that comparable results to Monte Carlo simulation can be achieved using the fuzzy set theory approach. It presents a methodology for extracting fuzzy numbers from experts and processing the information in fuzzy range estimating analysis. It is of relevance to industry and practitioners as it provides an approach to range estimating that more closely resembles the way in which experts express themselves, making it practically easy to apply an approach.