The use of stochastic optimization into industrial contexts has been widely disseminated in literature. Many mention the fact that the consideration of uncertainty within decision support schemes can yield many benefits in terms of better decision making. However, it is also largely acknowledged that there is a gap between academic developments and industrial relevant problems. In this talk, we will present recent applications of stochastic optimization techniques to several problems in the Oil & Gas business. Comprising decisions from strategic to operational nature, we intend to share a few examples in which the use of stochastic programming was capable of improve operational performance in large-size real-world systems.
About the speaker:
Dr. Fabricio Oliveira has recently joined the School of Mathematics and Geospatial Sciences at RMIT as a Research Fellow. He holds a Ph.D. Degree in Production Engineering (2012) and a Bachelor Degree in Industrial Engineering (2008), both from PUC-Rio. During his Ph.D., he worked as a Visiting Researcher at the Center of Advanced Process Decision-making (CAPD) at Carnegie Mellon University. He also worked in the Optimisation and Supply Chain Management group in Tecgraf Research Institute, where he acted as project coordinator (2009-2013). More recently, he has worked as an Assistant Professor in the Department of Industrial Engineering (DEI) at PUC-Rio (2013-2015), where he supervised a number of postgraduate students and published results in several high-quality journals. He has experience in production planning and logistics, with an emphasis on optimisation under uncertainty using mathematical programming techniques for oil and gas production and power distribution.
How to participate in this seminar:
1. Book your nearest ACE facility;
2. Notify Vera Roshchina at RMIT (maths.colloquia@rmit.edu.au) to notify you will be participating.
No access to an ACE facility? Contact Maaike Wienk to arrange a temporary Visimeet licence for remote access (limited number of licences available – first come first serve)