Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling

Abstract

Knowledge derived from limited data gathered in the early manufacturing stages is usually too fragile for a flexible manufacturing system (FMS). Unfortunately, production decisions have to be made quickly in a competitive environment. In a previous study, a strategy using continuous data and domain external expansion methods under a known data domain range was proposed to solve the so-called small data set learning problem in FMS. The present paper goes further in seeking a quantitative method to determine the range of domain external expansion under unknown domain bounds. The research considers the data bias phenomenon that often occurs in small data sets and provides a method for its adjustment. Beyond this, the study also compares the learning results among three types of membership functions (Bell, Trapezoid, Triangular) for data fuzzification. The results show that the proposed approach can advance the learning accuracy of a broad range of applications.

Keywords:

• Small data set
• Scheduling
• Flexible manufacturing system
• Machine learning