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X-ORIGINAL-URL:https://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20110101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120208T140000
DTEND;TZID=UTC:20120208T140000
DTSTAMP:20260403T173824
CREATED:20170124T102150Z
LAST-MODIFIED:20170124T102150Z
UID:613-1328709600-1328709600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Performance-Based Contracts for Outpatient Medical Services
DESCRIPTION:Title: Performance-Based Contracts for Outpatient Medical ServicesSpeaker: Dr. Houyuan Jiang – Senior Lecturer in Management ScienceAffiliation: Judge Business School – University of CambridgeLocation: Room 218 Huxley BuildingTime: 2:00pm \nAbstract. In recent years\, the performance-based approach to contracting for medical services has been gaining popularity across different healthcare delivery systems\, both in the US (under the name of “Pay-for-Performance”\, or P4P)\, and abroad (“Payment-by-Results”\, or PbR\, in the UK). The goal of our research is to build a unified performance-based contracting (PBC) framework that incorporates patient access-to-care requirements and that explicitly accounts for the complex outpatient care dynamics facilitated by the use of an online appointment scheduling system. We address the optimal contracting problem in a principal-agent framework where a service purchaser (the principal) minimizes her cost of purchasing the services and achieves the performance target (a waiting time target) while taking into account the response of the provider (the agent) to the contract terms. Given the incentives offered by the contract\, the provider maximizes his payoff by allocating his outpatient service capacity among three patient groups: urgent patients\, dedicated advance patients and flexible advance patients. We model the appointment dynamics as that of an M=D=1 queue and analyze several contracting approaches under adverse selection (asymmetric information) and moral hazard (private actions) settings. We study the first-best and the second-best solutions\, as well as their specific contracting implementation schemes. Our results show that simple and popular schemes used in practice cannot implement the first-best solution and that the linear PBC cannot implement the second-best solution. In order to overcome these limitations\, we propose a threshold-penalty PBC approach and show that it coordinates the system for an arbitrary patient mix and that it achieves the second-best performance for the setting where all advance patients are dedicated. \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-performance-based-contracts-for-outpatient-medical-services/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120215T140000
DTEND;TZID=UTC:20120215T140000
DTSTAMP:20260403T173824
CREATED:20170124T102150Z
LAST-MODIFIED:20170124T102150Z
UID:612-1329314400-1329314400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Complexity and heuristics in stochastic optimization
DESCRIPTION:Title: Complexity and heuristics in stochastic optimizationSpeaker: Prof. Teemu Pennanen – Professor of Mathematical Finance Probability and StatisticsAffiliation: King’s College LondonLocation: Room 218 Huxley BuildingTime: 2:00pm \nAbstract. Combining recent results on numerical integration and convex optimization\, we derive a polynomial bound on the worst case complexity of a class of static stochastic optimization problems. We then describe a technique for reducing dynamic problems to static ones. The reduction technique is only a heuristic but it can effectively employ good guesses for good solutions. This is illustrated on an 82-period problem coming from pension insurance industry. \nAbout the speaker. Teemu Pennanen is the Professor of Mathematical Finance\, Probability and Statistics at King’s College\, London. Before joining KCL\, Professor Pennanen worked as Managing Director at QSA Quantitative Solvency Analysts Ltd\, with a joint appointment as Professor of Stochastics at University of Jyvaskyla\, Finland. His earlier appointments include a research fellowship of the Finnish Academy and several visiting positions in universities abroad.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-complexity-and-heuristics-in-stochastic-optimization/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120223T140000
DTEND;TZID=UTC:20120223T140000
DTSTAMP:20260403T173824
CREATED:20170124T102150Z
LAST-MODIFIED:20170124T102150Z
UID:611-1330005600-1330005600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Enclosing with Simple Bodies
DESCRIPTION:Title: Enclosing with Simple BodiesSpeaker: Dr. Selin Damla Ahipasaoglu – research officerAffiliation: Management Science Group at LSELocation: Room 217 Huxley BuildingTime: 2:00pm \nAbstract. The Minimum Volume Enclosing Ellipsoid and Minimum Enclosing Ball problems are related to covering a given set of points with an ellipsoid or a ball with the smallest volume possible. These problems have many applications\, especially in statistics. We will survey recent theoretical results and take a look at efficient algorithms for solving these problems. We will show that these algorithms can be used for very large data sets. \nAbout the speaker. Selin Damla Ahipasaoglu received her PhD in 2009 from Cornell University under the supervision of Prof. Mike Todd. After her PhD\, she worked as a postdoctoral researcher at Princeton University and London School of Economics. She specialises in developing algorithms for large scale optimization problems\, in particular first-order methods for convex problems. She is also working on auctions and game theory. In April 2012\, she will be joining the Singapore University of Technology and Design as an assistant professor.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-enclosing-with-simple-bodies/
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