Evaluating the effectiveness of FDM in identifying important factors in a dynamic flowshop

• Published in: Robotics and computer-integrated manufacturing Vol. 25; no. 6; pp. 894 - 900
• Main Authors: Horng, H.C., Hu, C.C., Cheng, T.M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-12-2009
• Subjects: Design of experiments; Dynamic flowshop; Frequency domain methodology; Systems simulation; Frequency domain methodology; Systems simulation; Dynamic flowshop; Design of experiments
• ISSN: 0736-5845; 1879-2537
• DOI: 10.1016/j.rcim.2009.04.009

Dynamic events such as machine breakdown and hot jobs may induce problems on the production system such as order delay, increasing machine load, and changing inventory level. Past studies of dynamic events often use traditional design of experiments (DOE) to analyze the effects of dynamic events on system's performance. The shortcoming of this approach is that the number of experimental runs conducted would become exponentially increased as the number of factors increased. This study tries to use frequency domain methodology (FDM) instead so as to detect the higher order effects and rank important factors in a few experimental runs. Spectrum analysis is used to comprehend the effects of different location of machine breakdown and different size of hot jobs on the system's performance of flowshops with different traffic (utilization) and stability (oscillation). This study finds that the important factors identified by the FDM analysis are the same as that of DOE. However, only in some cases can the rankings of important factors be the same for both approaches. The dissimilarity between rankings of important factors found by these two methods is further measured using Kendall tau distance.