On Heptadecagon Fuzzy Numbers and their Ranking in Transportation Problem

Abstract

The Heptadecagon Fuzzy Number (HDGFN), a symmetric 17-point fuzzy structure with exact granularity for uncertainty modelling, is proposed in this study as a novel expansion in the class of polygonal fuzzy numbers. For HDGFN a formal α-cut formulation is established, which allows for the analytical computation of interval-valued representations at different levels of confidence. Furthermore, a innovative ranking function is suggested that is based on weighted symmetric averaging, using this ranking function the fuzzy valued transportation problem is converted to crisp valued problem, which is then solved using the MODI method and a new proposed method. The computational findings indicate that the HDGFN provides a more intricate and expressive modelling framework compared to traditional fuzzy numbers.