Taguchi method or Compromise Programming as Robust Design optimization tool: The case of a flexible manufacturing system

Competitive advantage of a firm is usually reflected through its superiority in production resources and performance outcomes. In order to achieve high performance (e.g., productivity) and significantly improve product quality, major US industries have promoted and implemented Robust Design (RD) techniques during the last decade. RD is a cost-effective procedure for determining the optimal settings of the control factors that make the product performance insensitive to the influence of noise factors. In this research, we employ and compare two RD optimum-seeking methods to optimize a flexible manufacturing system (FMS). Taguchi Method (TM), which uses robust design concept, i.e., Signal-To-Noise Ratio (S/N) to reduce the output variation, is applied first. Taguchi's approach to robust design drawn much criticism because it relies on the signal-to-noise (S/N) ratio for the optimization procedure. Because of this paramount criticism, a second method known as the Compromise Programming (CP) approach, i.e., the weighted Tchebycheff, is also used. This method formulates the robust design as a bi-objective robust design (BORD) problem by taking into account the two aspects of the RD problem, i.e. minimize the variation and optimize the mean. This approach seeks to determine the RD solution which is guaranteed to belong to the set of efficient solutions (Pareto points). Both methods use a RD formulation to determine an optimal and robust configuration of the system under study. The results gained through simulations and analytical formulations show that the current ways of handling the multiple aspects of the RD problem by using Taguchi's S/N ratio may not be adequate.