title: An Efficient Taguchi Approach for the Performance Optimization of Health, Safety, Environment and Ergonomics in Generation Companies.
abstract: BACKGROUND
  A unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented.
  
  
  METHODS
  To rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data.
  
  
  RESULTS
  The results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs.
  
  
  CONCLUSIONS
  The approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.
authors.affiliation: School of Industrial and Systems Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Azadeh
authors.affiliation: Centre for Industrial Management/Traffic and Infrastructure, KU Leuven, Heverlee, Belgium ; School of Industrial and Systems Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Iran.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Sheikhalishahi
keywords: Taguchi methods; data envelopment analysis; generation companies; health, safety, environment, and ergonomics; performance optimization