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X-ORIGINAL-URL:http://optimisation.doc.ic.ac.uk
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DTSTART:20150101T000000
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DTSTART;TZID=UTC:20160119T150000
DTEND;TZID=UTC:20160119T150000
DTSTAMP:20260418T142805
CREATED:20170124T102135Z
LAST-MODIFIED:20170124T102135Z
UID:546-1453215600-1453215600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: The Decision Rule Approach to Optimization Under Uncertainty: Theory and Applications
DESCRIPTION:Title: The Decision Rule Approach to Optimization Under Uncertainty: Theory and ApplicationsSpeaker: Dr. Angelos GeorghiouAffiliation: Automatic Control Laboratory – Swiss Federal Institute of Technology (ETH)Location: Room 217 Huxley BuildingTime: 3:00pm \nAbstract. Decision making under uncertainty has a long and distinguished history in operations research. However\, most of the existing solution techniques suffer from the curse of dimensionality\, which restricts their applicability to small and medium-sized problems\, or they rely on simplifying modeling assumptions (e.g. absence of recourse actions). Recently\, a new solution technique has been proposed\, which is referred to as the decision rule approach. By approximating the feasible region of the decision problem\, the decision rule approach aims to achieve tractability without changing the fundamental structure of the problem. Despite their success\, existing decision rules (a) are typically constrained by their a priori design and (b) do not incorporate in their modeling binary recourse decisions. In this talk\, we present a methodology for the near optimal design of continuous and binary decision rules using mixed-integer optimization\, and demonstrate its potential in operations management applications. \nAbout the speaker. Angelos Georghiou is a post-doctoral researcher with the Automatic Control Laboratory at ETH Zurich. He joined ETH in 2013\, having previously been a post-doctoral researcher at the Process Systems Engineering Laboratory at MIT. He received the MSci degree in Mathematics in 2008 from Imperial College London\, and the Ph.D. degree in Operations Research in 2012 from the Department of Computing at Imperial College London. Angelos’s research focuses on the development of efficient computational methods for the solution of stochastic and robust optimization problems. His work is primarily application driven\, the main application areas being energy systems\, operations management\, and control.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-the-decision-rule-approach-to-optimization-under-uncertainty-theory-and-applications/
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