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PRODID:-//Computational Optimisation Group - ECPv4.7.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Computational Optimisation Group
X-ORIGINAL-URL:http://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20190425T110000
DTEND;TZID=Europe/London:20190425T120000
DTSTAMP:20190422T180022
CREATED:20190416T132348Z
LAST-MODIFIED:20190416T132348Z
UID:1226-1556190000-1556193600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Data-Driven Methods for Integrated Production Scheduling and Process Control
DESCRIPTION:Title: Data-Driven Methods for Integrated Production Scheduling and Process Control\nSpeaker: Calvin Tsay\nAffiliation: McKetta Dept of Chemical Engineering\, University of Texas at Austin\nLocation: 218 Huxley Building\nTime: 11:00 – 12:00 \nAbstract. Due to the fast-changing market conditions enabled by globalization and modern infrastructures\, industrial production scheduling is often performed over relatively short time intervals to maximize profits. For chemical processes\, plant dynamics and control become highly relevant at these shortened time intervals\, and careful attention is required to ensure computed schedules are feasible when implemented in the physical process. With this motivation\, many recent works focused on integrating dynamic information from the process control layer into production scheduling. Unfortunately\, the resulting optimization problems are high-dimensional and often intractable because of the broad range of time scales involved. \nIn this presentation\, we describe data-driven techniques for learning low-order dynamic models of the behavior of a process and its controller (i.e.\, “closed-loop” behavior). Then\, we formulate an optimal scheduling problem involving the learned\, reduced-order representation of the relevant dynamics. Furthermore\, we present a data-mining approach that exploits historical process data to reduce the dimensionality of the aforementioned dynamic models. Throughout the presentation\, we will focus on applications to the scheduling of industrial air separation units\, which consume immense amounts of electricity\, in response to time-varying electricity prices (an operational strategy termed “demand response”). Several case studies will be presented\, ranging from model-based analyses to a real-world application on an industrial air separation unit. \n
URL:http://optimisation.doc.ic.ac.uk/event/seminar-data-driven-methods-for-integrated-production-scheduling-and-process-control/
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BEGIN:VEVENT
DTSTART;TZID=Europe/London:20190502T133000
DTEND;TZID=Europe/London:20190502T143000
DTSTAMP:20190422T180022
CREATED:20190410T081934Z
LAST-MODIFIED:20190410T081934Z
UID:1222-1556803800-1556807400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Exact and heuristic MIP methods for the solution of MINLP - Examples from gas transport optimization problems
DESCRIPTION:Title: Exact and heuristic MIP methods for the solution of MINLP – Examples from gas transport optimization problems\nSpeaker: Dr Lars Schewe\nAffiliation: Dept of Mathematics\, FAU Erlangen-Nürnberg\nLocation: 217 Huxley Building\nTime: 13:30 – 14:30 \nAbstract. In this talk\, we present exact and heuristic methods for MINLP\, the development of which was motivated by applications in gas transport optimization. In this talk\, we present a sample of our approaches and focus on provable results for both the exact and the heuristic methods. The methods have been applied on both academic and real-world instances. We first discuss how to solve MINLPs using a hierarchy of piece-wise linear relaxations and discuss a convergence result for such an algorithm. We show how this algorithm performs on problems in instationary gas transport. We then show how we can use a combination of penalty and alternating-direction methods to solve difficult instances of gas transport optimization problems and on instances from the MINLPLib. For these methods\, we can also give convergence results and discuss their relation to feasibility pump methods. \n
URL:http://optimisation.doc.ic.ac.uk/event/seminar-exact-and-heuristic-mip-methods-for-the-solution-of-minlp-examples-from-gas-transport-optimization-problems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20190502T150000
DTEND;TZID=Europe/London:20190502T160000
DTSTAMP:20190422T180022
CREATED:20190410T081937Z
LAST-MODIFIED:20190410T081937Z
UID:1223-1556809200-1556812800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Robust Discrete Optimization: Globalized Gamma Robustness and Radius of Robust Feasibility
DESCRIPTION:Title: Robust Discrete Optimization: Globalized Gamma Robustness and Radius of Robust Feasibility\nSpeaker: Prof. Dr Frauke Liers\nAffiliation: Dept of Mathematics\, FAU Erlangen-Nürnberg\nLocation: 217 Huxley Building\nTime: 15:00 – 16:00 \nAbstract. In this talk\, we extend the notion of two robust optimization methodologies that were originally introduced for continuous problems towards robust discrete tasks. On the one hand\, we look at globalized robust optimization that has been proposed as a generalization of the standard robust optimization framework in order to allow for a controlled decrease in protection. It depends on the distance of the realized from the predefined uncertainty set. In this talk\, we specialize the notion of globalized robustness to Gamma-uncertainty in order to extend its usability for discrete optimization. We show that the generalized robust counterpart possesses algorithmically tractable reformulations for mixed-integer linear nominal problems that use only slightly more variables and constraints than the standard robust counterpart under Gamma-uncertainty. For combinatorial problems\, our globalized robust counterpart remains fixed-parameter tractable\, although with a runtime exponential in Gamma. In computational studies\, it turns out that our algorithmically tractable reformulations are not more difficult to solve than the respective standard robust counterparts\, while globalized robustness is guaranteed. Secondly\, we extend the notion of determining the radius of robust feasibility for a mixed integer linear problem (MIP) with uncertain constraints. The radius of robust feasibility (RRF) determines a value for the maximal size of the uncertainty set such that robust feasibility of the MIP can be guaranteed. We will analyze relations between the RRF of a MIP and its continuous relaxation. In contrast to the general setting of the literature\, we extend the concept to computing the RRF to MIPs that might include safe constraints. Finally\, we apply our methods to the standard benchmark set of the MIPLIB in order to test their performance and analyze the price of robustness with respect to the RRF.The work about Globalized Gamma Robustness is joint with Andreas Bärmann (FAU Erlangen-Nürnberg\, Germany) and Christina Büsing (RWTH Aachen\, Germany). The work about the radius of robust feasibility is joint with Lars Schewe and Johannes Thürauf (both FAU Erlangen-Nürnberg\, Germany) \n
URL:http://optimisation.doc.ic.ac.uk/event/seminar-robust-discrete-optimization-globalized-gamma-robustness-and-radius-of-robust-feasibility/
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