A Piecewise Linear FGM Approach for Efficient and Accurate FAHP Analysis: Smart Backpack Design as an Example

Abstract

Most existing fuzzy AHP (FAHP) methods use triangular fuzzy numbers to approximate the fuzzy priorities of criteria, which is inaccurate. To obtain accurate fuzzy priorities, time-consuming alpha-cut operations are usually required. In order to improve the accuracy and efficiency of estimating the fuzzy priorities of criteria, the piecewise linear fuzzy geometric mean (PLFGM) approach is proposed in this study. The PLFGM method estimates the alpha cuts of fuzzy priorities and then connects these alpha cuts with straight lines. As a result, the estimated fuzzy priorities will have piecewise linear membership functions that resemble the real shapes. The PLFGM approach has been applied to the identification of critical features for a smart backpack design. According to the experimental results, the PLFGM approach improved the accuracy and efficiency of estimating the fuzzy priorities of these critical features by 33% and 80%, respectively.