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DTSTART:20100101T000000
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DTSTART;TZID=UTC:20111110T160000
DTEND;TZID=UTC:20111110T160000
DTSTAMP:20260408T034840
CREATED:20170124T102153Z
LAST-MODIFIED:20170124T102153Z
UID:626-1320940800-1320940800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Optimization under Uncertainty - Recent Advances in Multi-Parametric Mixed Integer Linear Programming and its Applications
DESCRIPTION:Title: Optimization under Uncertainty – Recent Advances in Multi-Parametric Mixed Integer Linear Programming and its Applications Speaker: Martina Wittmann-Hohlbein Affiliation: CPSE – Department of Chemical Engineering – Imperial College LondonLocation: 219A William Penney Lab (entrance from walkway)Time: 4:00pm \nAbstract. In optimization under uncertainty\, multi-parametric programming is a powerful tool to account for the presence of uncertainty in mathematical models. Its objective is to derive the optimal solution as a function of the parameters without exhaustively enumerating the parameter space. We consider the general multi-parametric mixed integer linear programming (mp-MILP) problem. The presence of uncertainty in mixed integer linear programming models employed in widespread application fields\, including planning/scheduling\, process synthesis and hybrid control\, significantly increases the complexity and computational effort in retrieving optimal solutions. A particular difficulty arises when uncertainty is simultaneously present in the coefficients of the objective function\, the constraint matrix and the constraint vector\, which transforms the original linear model into a nonlinear and non-convex one with respect to both the optimization variables and the parameters. Here\, we discuss two novel methods for the solution of the general mp-MILP problem. The first approach deals with the global solution of the general mp-MILP model. Exploiting the special structure of the problem\, we outline the steps of a parametric branch and bound procedure.  The second approach is a two-stage method for the approximate solution of the general mp-MILP problem which combines state-of-the-art robust optimization and multi-parametric programming techniques. The two-stage method is applied to short-term batch process scheduling under uncertainty and we demonstrate its potential in the construction of a pro-active scheduling policy. \nAbout the speaker. Martina Wittmann-Hohlbein obtained a Diploma in Mathematics and Economics at Martin-Luther-University Halle-Wittenberg\, Germany\, in 2007. She joined CPSE at Imperial College in 2009.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-optimization-under-uncertainty-recent-advances-in-multi-parametric-mixed-integer-linear-programming-and-its-applications/
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