Robust economic optimization of process design under uncertainty

Jin-Su Kang, Min Ho Suh, Tai Yong Lee*

*Corresponding author for this work

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

In this study, ranges of model parameters are analyzed for robustness measures. In particular, the properties of partial mean and worst-case cost in robust optimization are investigated. The robust optimization models are considered as multiobjective problems having two objectives, the expected performance (i.e. expected cost) and a robustness measure (Suh, M. and Lee, T. (2001) Robust optimization method for the economic term in chemical process design and planning. Industrial & Engineering Chemical Research, 40, 5950-5959). The robust partial mean model is defined with objectives of expected value and partial mean. The robust worst-case model is defined with the objective of expected value and worst-case. They are proved to guarantee Pareto optimality, which should be satisfied for multiobjective optimization problems. A graphical representation of the meaningful parameter ranges is clearly defined with mathematical proofs. The robustness of the solutions is discussed, based on the analysis of the ranges of the parameters. Three meaningful ranges of the parameters are investigated to choose a proper target value for the robust partial mean model. The worst-case value obtained from the worst-case analysis is recommended as the most effective target value, in order to obtain the optimal solution in a tradeoff between robustness and optimality. The proposed analysis in this study is validated with examples in chemical process design problems.

Original languageEnglish
Pages (from-to)51-75
Number of pages25
JournalEngineering Optimization
Volume36
Issue number1
DOIs
StatePublished - 1 Feb 2004

Keywords

  • Pareto optimality
  • Process design
  • Robust partial mean model
  • Robust worst-case model
  • Robustness measures
  • Target value

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