The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems

Abstract

The statistical theories are not expected to generate significant conclusions, when applied to very small data sets. Knowledge derived from limited data gathered in the early stages is considered too fragile for long term production decisions. Unfortunately, this work is necessary in the competitive industry and business environments. Our previous researches have been aimed at learning from small data sets for scheduling flexible manufacturing systems, and this article will focus development of an incremental learning procedure for small sequential data sets. The main consideration concentrates on two properties of data: that the data size is very small and the data are time-dependent. For this reason, we propose an extended algorithm named the Generalized-Trend-Diffusion (GTD) method, based on fuzzy theories, developing a unique backward tracking process for exploring predictive information through the strategy of shadow data generation. The extra information extracted from the shadow data has proven useful in accelerating the learning task and dynamically correcting the derived knowledge in a concurrent fashion.