Constrained optimization of black-box stochastic systems using a novel feasibility enhanced Kriging-based method

Zilong Wang, Marianthi Ierapetritou

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Stochastically constrained simulation optimization problems are challenging because the inherent noise terms to a black-box system lead to the need of considering the uncertainty at both the objective and the constraint function. To address this problem, we propose a Kriging-based optimization framework, which uses stochastic Kriging models to approximate the objective and the constraint functions and an adaptive sampling approach to sequentially search for the next point that is promising to have a better objective. The main contribution of this work is to incorporate a feasibility-enhanced term to the infill sampling criterion, which improves the Kriging-based algorithm's capabilities of returning a truly feasible near-optimal solution for stochastic systems. The efficacy of the Kriging-based algorithm is demonstrated with eight benchmark problems and a case study from the pharmaceutical manufacturing domain.

Original languageEnglish (US)
Pages (from-to)210-223
Number of pages14
JournalComputers and Chemical Engineering
Volume118
DOIs
StatePublished - Oct 4 2018

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Keywords

  • Adaptive sampling
  • Feasibility analysis
  • Kriging
  • Simulation optimization
  • Stochastic constraint

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