Reliability-Based Robust Design Optimization Method for Engineering Systems with Uncertainty Quantification

Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Syed Alam

Research output: Chapter in Book/Report/Conference proceedingChapter


Robust optimization is a method for optimization under uncertainties in engineering systems and designs for applications ranging from aeronautics to nuclear. In a robust design process, parameter variability (or uncertainty) is incorporated into the engineering systems’ optimization process to assure the systems’ quality and reliability. This chapter focuses on a robust optimization approach for developing robust and reliable advanced systems and explains the framework for using uncertainty quantification and optimization techniques. For the uncertainty analysis, a polynomial chaos-based approach is combined with the optimization algorithms MOSA (Multi-Objective Simulated Annealing), and the process is discussed with a simplified test function. For the optimization process, gradient-free genetic algorithms are considered as the optimizer scans the whole design space, and the optimal values are not always dependent on the initial values.
Original languageEnglish (US)
Title of host publicationHandbook of Smart Energy Systems
EditorsMichel Fathi, Enrico Zio, Panos M. Pardalos
ISBN (Electronic)9783030723224
StatePublished - Jan 6 2023
Externally publishedYes


  • Machine Learning
  • Reliability analysis
  • Uncertainty quantification
  • Optimizations


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