Integrating Taguchi Optimization for Multi-Criteria Decision Making in Engineering Applications

Abstract

This chapter discusses the use of Taguchi optimization, a statistical method for process optimization in engineering, to solve multi-criteria decision making (MCDM) problems. It focuses on achieving robust designs by minimizing variations and defects by identifying optimal control factors. The method uses orthogonal arrays for efficient experimentation and signal-to-noise ratios for performance measurement. It incorporates utility concepts, weighted principal component analysis, and multi-objective optimization. It has real-world applications in automotive, electronics, and chemical engineering. Taguchi's efficiency and cost-effectiveness are compared to response surface methodology and genetic algorithm optimization. It reduces experimental runs, improves product quality, and effectively handles MCDM problems. Future advancements could involve machine learning integration and broader application in emerging fields.

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