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X-WR-CALNAME:Computational Optimisation Group
X-ORIGINAL-URL:https://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20100101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20111201T140000
DTEND;TZID=UTC:20111201T140000
DTSTAMP:20260407T053106
CREATED:20170124T102152Z
LAST-MODIFIED:20170124T102152Z
UID:622-1322748000-1322748000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: How IBM enables industries to better leverage optimisation technology?
DESCRIPTION:Title: How IBM enables industries to better leverage optimisation technology?Speaker: Dr. Mozafar Hajian Affiliation: Location: 219A William Penney Lab (entrance from walkway) Time: 2:00pm \nAbstract. IBM ILOG CPLEX Optimisation Studio is used by leading academic & business institutions to rapidly create both mathematical programming and constraint programming models. It is used to build efficient optimisation models and state-of-the-art applications for the full range of planning and scheduling problems. The tool provides a powerful Integrated Development Environment (IDE) using the Optimisation Programming Language (OPL) language\, programmatic APIs\, or indeed other 3rd party modelling interfaces. This makes it easy to evaluate different modelling approaches and to integrate external data. With its built-in development tools\, it supports the entire model development process.In addition\,  IBM ILOG ODM Enterprise provides an enterprise scale platform for developing and deploying highly effective optimisation based analytical planning and scheduling solutions for business decision makers across a variety of industries. In this presentation\, you will find out\, how IBM is providing a powerful platform to enable\, a) better scenario management\, b) faster decision making process\, c) distributed planning processes for large scale applications\, and d) a role based planning and collaboration environment. \nAbout the speaker. Mozafar Hajian completed his Ph.D. at Brunel University of West London in Combinatorial Optimisation\, before joining IC-Parc at Imperial College as a Senior Research Fellow. During this period\, he collaborated with both Computing and Manufacturing departments to pioneer new methods in solving combinatorial optimisation problems and distributed Supply Chain Optimisation. Outside of academia\, he has held senior roles at ILOG\, SAP and IBM\, including senior business development manager for Supply Chain Optimisation at SAP. He is presently a certified Client Technical Advisor for IBM ILOG Optimisation & Supply Chain solutions.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-how-ibm-enables-industries-to-better-leverage-optimisation-technology/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20111208T170000
DTEND;TZID=UTC:20111208T170000
DTSTAMP:20260407T053106
CREATED:20170124T102152Z
LAST-MODIFIED:20170124T102152Z
UID:619-1323363600-1323363600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Decision rules for information discovery in multi-stage stochastic programming
DESCRIPTION:Title: Decision rules for information discovery in multi-stage stochastic programmingSpeaker: Phebe VayanosAffiliation: Department of Computing – Imperial College LondonLocation: CPSE Seminar room (C615 Roderic Hill)Time: 5:00pm \nAbstract. Stochastic programming and robust optimization are disciplines concerned with optimal decision-making under uncertainty over time. Traditional models and solution algorithms have been tailored to problems where the order in which the uncertainties unfold is independent of the controller actions. Nevertheless\, in numerous real-world decision problems\, the time of information discovery can be influenced by the decision maker\, and uncertainties only become observable following an (often costly) investment. Such problems can be formulated as mixed-binary multi-stage stochastic programs with decision-dependent non-anticipativity constraints. Unfortunately\, these problems are severely computationally intractable. We propose an approximation scheme for multi-stage problems with decision-dependent information discovery which is based on techniques commonly used in modern robust optimization. In particular\, we obtain a conservative approximation in the form of a mixed-binary linear program by restricting the spaces of measurable binary and real-valued decision rules to those that are representable as piecewise constant and linear functions of the uncertain parameters\, respectively. We assess our approach on a problem of infrastructure and production planning in offshore oil fields from the literature.  \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-decision-rules-for-information-discovery-in-multi-stage-stochastic-programming/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20111208T170000
DTEND;TZID=UTC:20111208T170000
DTSTAMP:20260407T053106
CREATED:20170124T102152Z
LAST-MODIFIED:20170124T102152Z
UID:620-1323363600-1323363600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: A Scenario Approach for Estimating the Suboptimality of Linear Decision Rules in Two-Stage Robust Optimization
DESCRIPTION:Title: A Scenario Approach for Estimating the Suboptimality of Linear Decision Rules in Two-Stage Robust Optimization Speaker: Michael HadjiyiannisAffiliation: Department of Computing – Imperial College LondonLocation: CPSE Seminar room (C615 Roderic Hill)Time: 5:00pm \nAbstract. Robust dynamic optimization problems involving adaptive decisions are computationally intractable in general. Tractable upper bounding approximations can be obtained by requiring the adaptive decisions to be representable as linear decision rules (LDRs). In this presentation we investigate families of tractable lower bounding approximations\, which serve to estimate the degree of suboptimality of the best LDR. These approximations are obtained either by solving a dual version of the robust optimization problem in LDRs or by utilizing an inclusion-wise discrete approximation of the problem’s uncertainty set. The quality of the resulting lower bounds depends on the distribution assigned to the uncertain parameters or the choice of the discretization points within the uncertainty set\, respectively. We prove that identifying the best possible lower bounds is generally intractable in both cases and propose an efficient procedure to construct suboptimal lower bounds. The resulting instance-wise bounds outperform known worst-case bounds in the majority of our test cases.  \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-a-scenario-approach-for-estimating-the-suboptimality-of-linear-decision-rules-in-two-stage-robust-optimization/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20111208T170000
DTEND;TZID=UTC:20111208T170000
DTSTAMP:20260407T053106
CREATED:20170124T102152Z
LAST-MODIFIED:20170124T102152Z
UID:621-1323363600-1323363600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Scenario-Free Stochastic Programming with Polynomial Decision Rules
DESCRIPTION:Title: Scenario-Free Stochastic Programming with Polynomial Decision RulesSpeaker: Dimitra BampouAffiliation: Department of Computing – Imperial College LondonLocation: CPSE Seminar room (C615 Roderic Hill)Time: 5:00pm \nAbstract. Multi-stage stochastic programming provides a versatile framework for optimal decision making under uncertainty\, but it gives rise to hard functional optimization problems since the adaptive recourse decisions must be modeled as functions of some or all uncertain parameters. We propose to approximate these recourse decisions by polynomial decision rules and show that the best polynomial decision rule of a fixed degree can be computed efficiently. We also show that the suboptimality of the best polynomial decision rule can be estimated efficiently by solving a dual version of the stochastic program in polynomial decision rules. \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-scenario-free-stochastic-programming-with-polynomial-decision-rules/
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