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Detailed schedule
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Show all the abstracts
Show all the abstracts
Thursday 11:00:00 Timetabling in education and sport Room 126 - Chair: G. Vanden Berghe
Thursday 11:00:00 Transportation management Room 130 - Chair: F. Semet
Thursday 11:00:00 Networks Room 138 - Chair: B. Fortz
Thursday 11:00:00 Nonconvex optimization 1 Room 035 - Chair: F. Bach
- On the best low multilinear rank approximation of higher-order tensors
Mariya Ishteva (Université catholique de Louvain, Department of Mathematical Engineering) Co-authors: PA. Absil, S. Van Huffel, L. De Lathauwer
- Regression on fixed-rank positive semidefinite matrices: a geometric approach
Gilles Meyer (University of Liège) Co-authors: Gilles Meyer, Silvère Bonnabel and Rodolphe Sepulchre
- Generalized Power Method for Sparse Principal Component Analysis
Rodolphe Sepulchre (Université de Liège) Co-authors: Michel Journée, Peter Richtarik, Yurii Nesterov
- A pooling approach for the feed mixing problem
Jannes Verstichel (KaHo Sint-Lieven) Co-authors: G. Vanden Berghe, H. Callens Abstract: Food production for pets and all kinds of livestock involves a large scale optimization problem with mass balance constraints, nutritional constraints and physical mixing constraints. The problem consists of mixing certain raw materials into recipes at the lowest possible cost. In the case where all the recipes can be stored on site in separate silos, the problem translates into a standard linear problem that can be efficiently solved by any linear solver. When the number of available silos is smaller than the required number of final recipes, the problem becomes much harder to solve. In this case intermediate mixtures, which can be stored separately in the available silos are introduced. These intermediates are made by mixing raw materials in such a way that all the required recipes can be produced using these intermediates. Due to the introduction of these intermediates many of the balance, nutritional and physical mixing constraints are no longer linear. In addition the objective function is no longer linear either. In the petrochemical industry, a similar problem is known as the Pooling Problem. That is a nonlinear problem, which often has nonconvexities in both constraints and objective function. The pooling problem aims at maximizing the profit of selling gasoline blends by subtracting the production cost of each blend from the selling price. Many solution methods have been applied to this pooling problem. We will tackle the problem using both the ALT heuristic from Audet et al. (2004) and the nonlinear IPOPT solver in combination with the mathematical modeling tool AMPL.
Thursday 14:00:00 Constraint programming models 1 Room 126 - Chair: Y. Deville
Thursday 14:00:00 Vehicle routing Room 130 - Chair: S. Limbourg
Thursday 14:00:00 Combinatorial optimization and IP applications Room 138 - Chair: Q. Louveaux
Thursday 14:00:00 Nonconvex Optimization 2 Room 035 - Chair: R. Sepulchre
Thursday 16:10:00 Constraint programming models 2 Room 126 - Chair: P. Schaus
Thursday 16:10:00 Performance modeling Room 130 - Chair: G. Janssens
Thursday 16:10:00 Scheduling Room 138 - Chair: K. Sorensen
Thursday 16:10:00 Planning under uncertainty Room 035 - Chair: R. Leus
Friday 09:00:00 Metaheuristics Room 126 - Chair: J. Teghem
Friday 09:25:00 Production and distribution (9:25) Room 130 - Chair: Y. Arda
Friday 09:00:00 Multiple criteria Room 138 - Chair: R. Bisdorff
Friday 09:25:00 Stochastic models (9:25) Room 035 - Chair: L. Esch
Friday 11:00:00 Constraint programming and Supply Chain Management Room 126 - Chair: Y. Deville
Friday 11:00:00 OR in health management Room 130 - Chair: P. De Causmaecker
Friday 11:00:00 Rankings and importance indices Room 138 - Chair: JL. Marichal
Friday 11:00:00 Queueing Room 035 - Chair: S. Wittevrongel
Friday 15:10:00 Optimization software Room 126 - Chair: E. Loute
Friday 15:10:00 Integrated operations planning Room 130 - Chair: B. Raa
Friday 15:10:00 Cycles in graphs Room 138 - Chair: F. Spieksma
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