Analysis of the Robustness of the Operational Performance Using a Combined Model of the Design of Experiment and Goal Programming Approaches for a Flexible Manufacturing Cell

Abstract

A combined model of a 2 k design of experiment (DOE) and goal programming (GP) approaches is presented to determine optimum levels of input variables and analyze their robustness for a multiobjective performance of a flexible manufacturing cell (FMC) in this study. Two main performance metrics, namely, manufacturing lead time (MLT) and surface roughness (SR), are considered performance outputs for the FMC. Machine sequence, robot speed, tool type, and material type are selected as the four input variables on the input side of the proposed model. The study shows that even with a limited number of experiments, one can determine optimum input levels for the multiobjective performance of the FMC and determine their robustness.