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Schedule
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Thursday 11:00 - 12:20 Routing Problems Room 138 - Chair: Pieter Vansteenwegen
Thursday 11:00 - 12:20 Emergency operations scheduling Room 130 - Chair: El-Houssaine Aghezzaf
Thursday 11:00 - 12:20 Algorithm design Room 126 - Chair: Gerrit Janssens
Thursday 11:00 - 12:20 Multiple Objectives Room 120 - Chair: Filip Van Utterbeeck
Thursday 13:30 - 14:50 Integrated logistics Room 138 - Chair: Kris Braekers
Thursday 13:30 - 14:50 Person transportation Room 130 - Chair: Célia Paquay
Thursday 13:30 - 14:50 Continuous models Room 126 - Chair: Nicolas Gillis
Thursday 13:30 - 14:50 Integer programming Room 120 - Chair: Bernard Fortz
- The maximum covering cycle problem
Wim Vancroonenburg (KU Leuven - FWO) Co-authors: Andrea Grosso, Fabio Salassa
- Linear and quadratic reformulation techniques for nonlinear 0-1 optimization problems
Elisabeth Rodriguez Heck (HEC Liège - Management School of the University of Liège) Co-authors: Yves Crama
- Unit Commitment under Market Equilibrium Constraints
Jérôme De Boeck (Université Libre de Bruxelles) Co-authors: Luce Brotcorne, Fabio D'Andreagiovanni, Bernard Fortz Abstract: The classical Unit Commitment problem (UC) can be essentially described as the
problem of establishing the energy output of a set of generation units over a
time horizon, in order to satisfy a demand for energy, while minimizing the
cost of generation and respecting technological restrictions of the units
(e.g., minimum on/off times, ramp up/down constraints). The UC is typically
modelled as a (large scale) mixed integer program and its deterministic
version, namely the version not considering the presence of uncertain data, has
been object of wide theoretical and applied studies over the years.
Traditional (deterministic) models for the UC assume that the net demand for
each period is perfectly known in advance, or in more recent and more realistic
approaches, that a set of possible demand scenarios is known (leading to
stochastic or robust optimization problems).
However, in practice, the demand is dictated by the amounts that can be sold by
the producer at given prices on the day-ahead market. One difficulty
therefore arises if the optimal production dictated by the optimal solution to
the UC problem cannot be sold at the producer's desired price on the market,
leading to a possible loss. Another strategy could be to bid for additional
quantities at a higher price to increase profit, but that could lead to
infeasibilities in the production plan.
Our aim is to model and solve the UC problem with a second level of decisions
ensuring that the produced quantities are cleared at market equilibrium. In
their simplest form, market equilibrium constraints are equivalent to the
first-order optimality conditions of a linear program. The UC in contrast is
usually a mixed-integer nonlinear program (MINLP), that is linearized and
solved with traditional Mixed Integer (linear) Programming (MIP) solvers.
Taking a similar approach, we are faced to a bilevel optimization problem
where the first level is a MIP and the second level linear.
In this talk, as a first approach to the problem, we assume that demand curves
and offers of competitors in the market are known to the operator. This is a
very strong and unrealistic hypothesis, but necessary to develop a first model.
Following the classical approach for these models, we present the
transformation of the problem into a single-level program by rewriting and
linearizing the first-order optimality conditions of the second level. Then we
present some preliminary results on the performance of MIP solvers on this
model. Our future research will focus on strengthening the model using its
structure to include new valid inequalities or to propose alternative extended
formulations, and then study a stochastic version of the problem where demand
curves are uncertain.
Thursday 15:20 - 16:20 Material handling and warehousing 1 Room 138 - Chair: Greet Vanden Berghe
Thursday 15:20 - 16:20 Operations management Room 130 - Chair: Roel Leus
Thursday 15:20 - 16:20 Matrix factorization Room 126 - Chair: Pierre Kunsch
Thursday 16:30 - 17:10 Material handling and warehousing 2 Room 138 - Chair: Katrien Ramaekers
Thursday 16:30 - 17:10 Routing and local search Room 130 - Chair: An Caris
Thursday 16:30 - 17:10 Traffic management Room 126 - Chair: Joris Walraevens
Thursday 16:30 - 17:10 Pharmaceutical supply chains Room 120 - Chair: Bart Smeulders
Friday 10:50 - 12:10 Optimization in health care Room 138 - Chair: Jeroen Beliën
Friday 10:50 - 12:10 Network design Room 130 - Chair: Jean-Sébastien Tancrez
Friday 10:50 - 12:10 Local search methodology Room 126 - Chair: Patrick De Causmaecker
Friday 10:50 - 12:10 ORBEL Award Room 120 - Chair: Frits Spieksma
Friday 13:00 - 14:00 Production and inventory management Room 138 - Chair: Tony Wauters
Friday 13:00 - 14:00 Logistics 4.0 Room 130 - Chair: Thierry Pironet
Friday 13:00 - 14:00 Data clustering Room 126 - Chair: Yves De Smet
Friday 13:00 - 14:00 Collective decision making Room 120 - Chair: Bernard De Baets
Friday 14:10 - 15:10 Sport scheduling Room 138 - Chair: Dries Goossens
Friday 14:10 - 15:10 Discrete choice modeling Room 130 - Chair: Virginie Lurkin
Friday 14:10 - 15:10 Data classification Room 126 - Chair: Ashwin Ittoo
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ORBEL - Conference chair: Prof. A. Arda -
Platform: Prof. M. Schyns.
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