Fuzzy Nonlinear Optimization Model to Improve Intermittent Demand Forecasting

Abstract

This chapter proposes a fuzzy nonlinear programming model for intermittent demand forecasting purposes. The authors formulated the Syntetos and Boylan (SB) forecasting method as a crisp nonlinear programming model. They also attempted to improve it with a new fuzzy nonlinear programming formulation. This fuzzy model is based on fuzzy decision variables, which represent fuzzy triangular numbers. The authors applied fuzzy arithmetic operations, such as addition and subtraction of fuzzy numbers, fuzzy decision variables. They carried out the defuzzification of the fuzzy decision variables through the possibilistic mean value of fuzzy numbers. Finally, the authors validated and tested it by comparing it with the deterministic nonlinear programming model that they adopted as the basis of this work. The computational studies show that fuzzy model performance is consistently better than the SB nonlinear programming model, especially when intermittency is high.