A Comparative Study of Three Lot-Sizing Methods for the Case of Fuzzy Demand

Abstract

Most of the literature published regarding the performance of lot-sizing algorithms has been in a deterministic environment. The first objective of this article is to propose a way to incorporate fuzzy sets theory into lotsizing algorithms for the case of uncertain demand in a fuzzy master production schedule. Triangular fuzzy numbers are used to represent uncertainty in the master production schedule. It is shown that the fuzzy sets theory approach provides a better representation of fuzzy demand and more information to aid the determination of lot size. The second objective is to evaluate three lot sizing methods: part-period balancing, Silver-Meal, and Wagner-Whitin. The performance of each lot-sizing algorithm was calculated over nine examples. The results indicate that the part-period balancing algorithm may be a better overall choice to determine lot sizes.