Optimization under uncertainty

The PhD thesis of Dr Johannes Wiebe (Wiebe, 2021), co-supervised by Dr Inês Cecílio of Schlumberger New Energy and myself, investigated the application of optimization under uncertainty to equipment degradation. Our collaboration , which was featured by both Imperial and Schlumberger as an example of excellence in academic / industrial collaborations, used the paradigm of data-driven robust optimization to address challenges in drilling geothermal wells.

Together with Dr Wiebe, we:

  • Apply robust optimization to applications with uncertain equipment degradation and explore the effect of uncertainty on optimal decision-making (Wiebe et al., 2018),
  • Develop robust optimization methods for efficiently incorporating data-driven models into (process-level) decision-making problems (Wiebe et al., 2022).
  • Develop open-source software ROmodel for modeling robust optimization problems in Pyomo (Wiebe & Misener, 2021).
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