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X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART;TZID=UTC:20130808T140000
DTEND;TZID=UTC:20130808T140000
DTSTAMP:20260505T053304
CREATED:20170124T102141Z
LAST-MODIFIED:20170124T102141Z
UID:576-1375970400-1375970400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems
DESCRIPTION:Title: Risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problemsSpeaker: Dr. Wajdi TekayaAffiliation: Decision Trees GmbHLocation: Room 217 Huxley BuildingTime: 2:00pm \nAbstract. This talk gives an overview of risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems. In the first part of this talk\, we discuss risk neutral and risk averse approaches using coherent risk measures to multistage (linear) stochastic programming problems based on the Stochastic Dual Dynamic Programming (SDDP) method. We give a general description of the algorithm and present computational studies related to planning of the Brazilian interconnected power system.In the second part of this talk\, we discuss multistage programming with the data process subject to uncertainty. We consider a situation where the data process can be naturally separated into two components\, one can be modeled as a random process\, with a specified probability distribution\, and the other one can be treated from a robust (worst-case) point of view. We formulate this in a time consistent way and derive the corresponding dynamic programming equations. In order to solve the obtained multistage problem we develop a variant of the (SDDP) method. We give a general description of the algorithm and present computational studies related to planning of the Brazilian interconnected power system. \nAbout the speaker. Wajdi Tekaya is currently an HPCfinance postdoctoral fellow at Cambridge Systems Associates. He obtained his B.S. in industrial engineering from Tunisia Polytechnic School\, M.S. in Operations Research from Paris IX University\, Georgia Institute of Technology and Ph.D. in Operations Research from Georgia Institute of technology. His research interests are in computational approaches to stochastic programming with applications in energy and finance.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-risk-neutral-and-risk-averse-approaches-to-multistage-stochastic-programming-with-applications-to-hydrothermal-operation-planning-problems/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20130812T140000
DTEND;TZID=UTC:20130812T140000
DTSTAMP:20260505T053304
CREATED:20170124T102141Z
LAST-MODIFIED:20170124T102141Z
UID:575-1376316000-1376316000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Managing with Incomplete Inventory Information
DESCRIPTION:Title: Managing with Incomplete Inventory Information Speaker: Prof. Suresh P. SethiAffiliation: Center for Intelligent Supply Networks – University of Texas at DallasLocation: Room 217-218 Huxley BuildingTime: 2:00pm \nAbstract. A critical assumption in the vast literature on inventory management has been that the current level of inventory is known to the decision maker. Some of the most celebrated results such as the optimality of base-stock policies have been obtained under this assumption. Yet it is often the case in practice that the decision makers have incomplete or partial information about their inventory levels. The reasons for this are many: Inventory records or cash register information differ from actual inventory because of a variety of factors including transaction errors\, theft\, spoilage\, misplacement\, unobserved lost demands\, and information delays. As a result\, what are usually observed are some events or surrogate measures\, called signals\, related to the inventory level. These relationships can provide the distribution of current inventory levels. Therefore\, the system state in the inventory control problems is not the current inventory level\, but rather its distribution given the observed signals. Thus\, the analysis for finding optimal production or ordering policies takes place generally in the space of probability distributions. The purpose of this talk is to review recent developments in the analysis of inventory management problems with incomplete information. \nAbout the speaker. Suresh P. Sethi is Eugene McDermott Professor of Operations Management and Director of the Center for Intelligent Supply Networks at The University of Texas at Dallas. He has written 7 books and published nearly 400 research papers in the fields of manufacturing and operations management\, finance and economics\, marketing\, and optimization theory. He teaches a course on optimal control theory/applications and organizes a seminar series on operations management topics. He initiated and developed the doctoral programs in operations management at both University of Texas at Dallas and University of Toronto. He serves on the editorial boards of several journals including Production and Operations Management and SIAM Journal on Control and Optimization. He was named a Fellow of The Royal Society of Canada in 1994. Two conferences were organized and two books edited in his honor in 2005-6. Other honors include: IEEE Fellow (2001)\, INFORMS Fellow (2003)\, AAAS Fellow (2003)\, POMS Fellow (2005)\, IITB Distinguished Alum (2008)\, SIAM Fellow (2009)\, POMS President (2012).
URL:http://optimisation.doc.ic.ac.uk/event/seminar-managing-with-incomplete-inventory-information/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20130822T110000
DTEND;TZID=UTC:20130822T110000
DTSTAMP:20260505T053304
CREATED:20170124T102140Z
LAST-MODIFIED:20170124T102140Z
UID:574-1377169200-1377169200@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Added value of scenario tree based stochastic optimization in long and medium term planning of hydro power systems
DESCRIPTION:Title: Added value of scenario tree based stochastic optimization in long and medium term planning of hydro power systemsSpeaker: Dr. Georg OstermaierAffiliation: Location: Room 217 Huxley BuildingTime: 11:00am \nAbstract. The changes in the dynamics of power prices in Germany within the last few years have implied significant decreases of revenues of pumped storage hydro systems. The earnings from daily peak/offpeak price spreads have declined whereby at the same time long term management of large reservoirs has become even more difficult due hardly predictable power price and inflow evolutions. Stochastic Optimization has been applied for hydro system management for a long time already\, but only recently operators expect additional benefits given the changed and even more uncertain power market situation. Besides stochastic dual dynamic programming\, scenario tree based methods are meanwhile applied\, which so far have hardly been used for complex hydro systems due to the curse of dimensionality in stochastic programming. We apply efficient discretization methods for the generation of multi-dimensional scenario trees of power prices and inflows\, based on moment matching and multinomial distributions. Furthermore\, by using increased hardware efficiency it is meanwhile possible to set up and solve scenario tree based stochastic optimization models for even complex pumped storage systems. Ex post analyses have shown that outperformance over deterministic optimization in the range of 1-5 percent is achievable. \nAbout the speaker. Georg Ostermaier is founder\, owner and managing director of Decision Trees GmbH\, a Munich based firm focussing on the practical application of mathematical and stochastic programming in the European Energy industry. Georg received his graduate degree in Electrical Engineering from the Technical University of Munich and his PhD in Operations Research from the University of St.Gallen\, Switerland. Since 2006 he and his team have been developing stochastic optimization software systems for thermal power generation portfolios\, hydro power generation systems\, gas storage and gas contract valuation and gas procurment portfolio optimisation. Decision Trees GmbH has today a solid customer base in Germany\, Austria\, Switzerland\, Norway and the United Kingdom and has proven to contribute to enhanced profits for energy producers in practice through stochastic optimization.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-added-value-of-scenario-tree-based-stochastic-optimization-in-long-and-medium-term-planning-of-hydro-power-systems/
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