A Simulation-Based Study for Multi-response Optimization of FMS Performance Measures Using Combined Grey Relational and Principal Component Analyses

Abstract

Flexible manufacturing systems (FMSs) are highly efficient, and they have become the backbone of the modern manufacturing systems. This work attempts to explore the effect of a few critical input parameters of FMSs with an aim to determine their optimal setting which yields optimal multi-performance measures. Four input parameters of FMS viz. type of layout, velocity of the automated guided vehicle (AGV), number of AGVs, and sequencing rule each at four levels, except the sequencing rule at two levels, are considered. ARENA simulation software is used to create models as per Taguchi’s L16 orthogonal array, and three performance measures, i.e., make span time, AGV utilization, and average machine utilization, are determined for a specific FMS problem taken from literature. Multi-response optimization using combined grey relational analysis (GRA) and principal component analysis (PCA) is done. Result reveals that a combination of open field layout, 40 m/min AGV velocity, 5 AGVs, and first-come first-served sequencing rule yields optimal performance measures of the FMS. Further, sequencing rule is found to affect the multi-performance measures of the FMS significantly. The result of the confirmatory test validates the optimal result obtained and reported in this paper.