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X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20160101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20171114T160000
DTEND;TZID=UTC:20171114T170000
DTSTAMP:20260419T013946
CREATED:20170921T091809Z
LAST-MODIFIED:20170921T091809Z
UID:989-1510675200-1510678800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems
DESCRIPTION:Title: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems\nSpeaker: Prof. Stratos Pistikopoulos\, FREng\nAffiliation: Artie McFerrin Department of Chemical Engineering\, Texas A&M University\nLocation: Room 217 Huxley Building\nTime: 4:00pm \nAbstract. Optimization problems involving multiple decision makers at different decision levels are referred to as multi-level programming problems. We are considering bi-level (two decision levels) and tri-level (three decision levels) programming problems. Multi-level programming problems are very challenging to solve even when considering just two linear decision levels. For classes of problems where the lower level problems also involve discrete variables\, this complexity is further increased\, typically requiring global optimization methods for its solution. Solution approaches for mixed integer bi-level problems with discrete variables in both levels mainly include reformulation approaches\, branch and bound techniques or genetic algorithms\, all of which result in approximate solutions. \nIn this work\, we present novel algorithms for the exact\, global and parametric solution of two classes of multi-level programming problems\, namely (i) bi-level mixed-integer linear or quadratic programming problems (B-MILP or B-MIQP) and (ii) tri-level mixed-integer linear or quadratic programming problems (T-MILP or T-MIQP) containing both integer and continuous variables at all optimization levels. Based on multi-parametric theory and our earlier results for bi-level programing problems [5\, 6]\, the main idea is to recast the lower levels of the multi-level programming problem as multi-parametric programming problems\, in which the optimization variables of all the upper level problems\, both continuous and integer\, are considered as parameters for the lower level problems. \nThis novel algorithm can be then used for the exact and global solution of adjustable robust optimization problems. Classical robust optimization (RO) is an approach for incorporating uncertainty in optimization problems\, and traditionally assumes that all decisions must be made before the realization of uncertainty (referred to as “here-and-now” decisions)\, a strategy which may be overly conservative. A more realistic approach is adjustable robust optimization (ARO) which involves recourse decisions (i.e. reactive actions after the realization of the uncertainty\, “wait-and-see”) as functions of the uncertainty\, typically posed in a two-stage stochastic setting. We propose a novel method for the derivation of generalized affine decision rules for linear/quadratic/nonlinear and mixed-integer ARO problems through multi-parametric programming. The problem is treated as a multi-level programming problem that can be then solved using the presented algorithm. A set of illustrative numerical examples are provided to demonstrate the potential of the proposed novel approach. \nAbout the speaker. Professor Pistikopoulos is TEES Distinguished Research Professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University. He was a Professor of Chemical Engineering at Imperial College London\, UK (1991-2015) and the Director of its Centre for Process Systems Engineering (2002-2009). At Texas A&M\, he is the Interim Co-Director & Deputy Director of the Texas A&M Energy Institute\, the Course Director of the Master of Science in Energy\, the Director of the Gulf Coast Regional Manufacturing Centre\, and the Texas A&M Principal Investigator of the RAPID Institute on process intensification\, co-leading the Modeling & Simulation Focus Area. \nHe holds a Ph.D. degree from Carnegie Mellon University and he worked with Shell Chemicals in Amsterdam before joining Imperial. He has authored or co-authored over 400 major research publications in the areas of modelling\, control and optimization of process\, energy and systems engineering applications\, 12 books and 2 patents. He is a Fellow of IChemE and AIChE\, and the Editor-in-Chief of Computers & Chemical Engineering. He is the current Chair of the Computing and Systems Technology (CAST) Division of AIChE and he serves as a trustee of the Computer Aids for Chemical Engineering (CACHE) Organization. In 2007\, Prof. Pistikopoulos was a co-recipient of the prestigious MacRobert Award from the Royal Academy of Engineering. In 2012\, he was the recipient of the Computing in Chemical Engineering Award of CAST/AIChE. He received the title of Doctor Honoris Causa in 2014 from the University Politehnica of Bucharest\, and from the University of Pannonia in 2015. In 2013\, he was elected Fellow of the Royal Academy of Engineering in the UK.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-multi-level-optimization-by-multi-parametric-programming-its-use-for-the-solution-of-mixed-integer-adjustable-robust-optimization-problems/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20171121T140000
DTEND;TZID=UTC:20171121T150000
DTSTAMP:20260419T013946
CREATED:20171029T101434Z
LAST-MODIFIED:20171029T101434Z
UID:1012-1511272800-1511276400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Stochastic Vehicle Routing: an Overview and some Recent Advances
DESCRIPTION:Title: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems\nSpeaker: Prof. Michel Gendreau\nAffiliation: Dept. of Mathematics and Industrial Engineering\, Polytechnique Montréal\nLocation: LG19a\, Business School\nTime: 2:00pm \nAbstract. While Vehicle Routing Problems have now been studied extensively for more than 50 years\, those in which some parameters are uncertain at the time where the routes are made have received significantly less attention\, in spite of the fact that there are many real-life settings where key parameters are not known with certainty. \nIn the first part of this talk\, we will examine the main classes of Stochastic Vehicle Routing Problems: problems with stochastic demands\, stochastic customers\, and stochastic service or travel times. We will emphasize the main approaches for modeling and tackling uncertainty: a priori models\, a posteriori approaches\, and chance-constrained models.  The second part of the talk will devoted to a presentation of some of our recent work in the area. \nAbout the speaker. Michel Gendreau is Department Chair and Professor of Operations Research in the Department of Mathematics and Industrial Engineering of Polytechnique Montréal (Canada). He received both his M.Sc. and his Ph.D. degrees from University of Montreal. His main research area is the application of operations research methods to a wide range of problem areas: transportation and logistics systems planning and operation\, energy production and storage\, healthcare\, and telecommunications. Dr. Gendreau has published more than 300 papers in peer-reviewed journals and conference proceedings. He is also the co-editor of six books dealing with transportation planning and scheduling\, as well as with metaheuristics. \nDr. Gendreau was the Director of the Centre for Research on Transportation (formerly CRT and now CIRRELT) from 1999 to 2007. He completed his 6-year term as Editor in chief of Transportation Science at the end of 2014. In 2001\, he received the Merit Award of the Canadian Operational Research Society in recognition of his contributions to the development of O.R. in Canada. He was elected Fellow of INFORMS in 2010. In 2015\, Dr. Gendreau received the prestigious Robert Herman Lifetime Achievement Award of the Transportation Science & Logistics Society of INFORMS.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-stochastic-vehicle-routing-an-overview-and-some-recent-advances/
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