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).