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DTSTART;TZID=Europe/Berlin:20220804T160000
DTEND;TZID=Europe/Berlin:20220804T170000
DTSTAMP:20220721T081156Z
CREATED:20220718T130428Z
LAST-MODIFIED:20220721T081156Z
UID:6654-1659628800-1659632400@alop.uni-trier.de
SUMMARY:ALOP Colloquium with Kendra Reiter
DESCRIPTION:On Thursday\, August 4 2022 at 16:00 c.t. Kendra Reiter\, MTU Maintenance (Hannover)\, will speak at the ALOP Colloquium about her recent work: \n  \nBuilding a geo-referenced microsimulation model with discrete optimization* \n*joint work with Dr. Ulf Friedrich\, OVGU Magdeburg and Prof. Dr. Ralf Münnich\, Trier University \nAbstract: \nMicrosimulation is an important tool to support evidence-based policies. To produce a fully geo-coded dataset\, where information is generally available on aggregate levels of different hierarchies\, micro units (households and persons) have to be placed into geo-coded dwellings. The microsimulation model involves mathematical optimization problems with integrality constraints on some of the optimization variables. It is therefore necessary to employ combinatorial optimization techniques to handle these discrete structures efficiently. More specifically\, fast algorithms for the sub-problem of address selection are needed: Given a population generated in the first step of the microsimulation process and a target region\, the households in the population have to be assigned to actual addresses within the region\, i.e.\, an address has to be selected for each household in the population. \nWhile the computation time is often not crucial when considering only a subset of the population\, e.g.\, for the simulation of a certain region or city\, the big-data setting of a complete model typically requires specialized\, fast algorithms and techniques from data science. For example\, in the address selection model for Germany more than 40 million households are assigned to over 25 million addresses while using several statistical variables to measure the quality of the assignment. General purpose heuristics such as simulated annealing do generally not solve this instance within an acceptable time limit and do not provide quality certificates. In addition\, large data sets from several sources (e.g.\, Open Street Map\, city registers\, grid-based census data) have to be combined and pre-processed in an efficient and secure way. \nPlease join us at 16:00 c.t. in HS 9.
URL:https://alop.uni-trier.de/event/alop-colloquium-with-ulf-friedrich-and-kendra-reiter/
LOCATION:Trier University E Building\, Universitätsring 15\, Trier\, 54296\, Germany
CATEGORIES:Colloquium
ORGANIZER;CN="RTG ALOP at Trier University":MAILTO:ALOP@uni-trier.de
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