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Fields Industrial Optimization
Seminar
2011-12
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Supported by
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The inaugural meeting of the Fields Industrial Optimization Seminar took
place on November 2, 2004. The seminar meets in the early evening of the first
Tuesday of each month. Each meeting is comprised of two related lectures on
a topic in optimization; typically, one speaker is a university-based researcher
and the other is from the private or government sector. The series welcomes
the participation of everyone in the academic or industrial community with
an interest in optimization – theory or practice, expert or student .
Please subscribe to the Fields mail list to be informed
of upcoming seminars.
Audio and slides for Industrial Optimization Seminar available here.
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March 6, 2012
Fields Institute,
Room 230
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5:00 p.m.
Michael Chen (York University)
A stochastic integer programming approach to the optimal thermal
and wind generator scheduling problem
In recent years, the increasing capacity of wind energy,
together with solar and other renewable energy, brings in
a new challenge to the electricity generator scheduling
problem: the renewable energy is stochastic by its nature
and it is a daunting task to meet a stochastic demand by
a stochastic supply year around. In the near future, as
we approach the goal of 25% percent renewable energy by
2025, this challenge will be more and more prominent. Sufficient
reserve has been used to achieve this goal in the past when
the supply is fully controllable. Will the same approach
work with 25% stochastic supply? Do we need to increase
the reserve level? We will first model the complicated electricity
grid, physics of generators, stochastic demand and wind
power, and the day-ahead decision process. Our model aims
at a good balance of the reality and computational complexity.
Based on this stochastic integer model, we develop an effective
scenario-crossing deep cut, which accelerates the state-of-art
CPLEX solver significantly.
Peter Hall (Arcelor-Mittal Dofasco)
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| 2011-12 PAST SEMINARS
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February 7, 2012
Fields Institute,
Room 230
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5:00 p.m. - 7:00 p.m.
Kirsten Morris (University of Waterloo)
Optimal actuator location
Active noise control and control of structural vibrations
are examples of systems modelled by partial differential equations.
Because of the distribution of the system in space the location
of actuators, and sensors, in these systems is a variable
in the design of a control system. The talk will focus on
actuator location. The problem of locating sensors is mathematically
dual and will be mentioned briefly. The criterion for optimization
should be determined by the controller objective; common approaches
to controller design are linear-quadratic, H-2 and H-infinity.
Since the controller is generally calculated using an approximation,
often of high order, determination of the optimal actuator/sensor
locations is not straightforward. Conditions for approximations
that can be used in determination off optimal actuator location
have been obtained. Recently developed algorithms for calculation
of the optimal actuator locations for several different control
objectives are discussed.
(audio and slides available here)
Oleksandr Romanko (Algorithmics Incorporated, an IBM
Company, Toronto)
Scenario-Based Value-at-Risk Optimization
In financial risk management Value-at-Risk (VaR) is a
popular tail-based risk measure which forms the basis for
regulatory capital according to Basel II Accord. Thus, optimizing
VaR can have benefits in terms of freeing up capital. The
problem is that VaR is a quantile of the loss distribution
(for a particular time horizon), which is a chance-constrained
problem. Since the loss distribution is typically unknown
or computationally impractical, VaR optimization usually uses
a finite sample approximation to the distribution by means
of scenarios, so that an estimate of the VaR over a sample
scenario set is actually optimized. This, however, requires
mixed-integer programming, which makes the problem difficult
to solve.
To improve solution time, different heuristic techniques can
be used during optimization. We develop and test heuristic
algorithms for scenario-based VaR optimization. Due to high
computational complexity of VaR optimization, we utilize Conditional
Value-at-Risk (CVaR) - based proxies for VaR objectives and
constraints. Our heuristic algorithm allows obtaining robust
results with low computational complexity.
This is joint work with Helmut Mausser from Algorithmics Incorporated,
an IBM Company.
(audio and slides available here)
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December 6, 2011
Fields Institute,
Room 230
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5:00 - 6:00 p.m.
Catalin Trenchea (University of Pittsburgh)
A stochastic collocation approach to constrained optimization
for random data estimation problems
We present a scalable, parallel mechanism for estimation
of statistical moments of input random data, given the probability
distribution of some response of a system of stochastic
partial differential equations. To characterize data with
moderately large amounts of uncertainty, we introduce a
stochastic parameter identification algorithm that integrates
an adjoint-based deterministic algorithm with the sparse
grid stochastic collocation FEM. Rigorously derived error
estimates are used to compare the efficiency of the method
with other techniques.
(audio and slides available here)
Szymon Buhajczuk (SimuTech Group Inc. Toronto)
Commercial Implementations of Optimization Software and
its Application to Fluid Dynamics Problems
ANSYS Computational Fluid Dynamics (CFD) tools such
Fluent and CFX have an established place in the engineering
world and are being used to help make design decisions early
on in product design cycles. Traditionally this has been
a manual process requiring engineering intuition and in-depth
understanding of fluid dynamics. With the advent of powerful
and inexpensive computers, the optimization process can
be increasingly automated using commercial tools. Multi
Disciplinary Optimization (MDO) codes such as ANSYS Design
Explorer and Red Cedar's HEEDS are able to couple with existing
CFD software and obtain optimal designs faster. In comparison
testing of Design Explorer and HEEDS, both codes were benchmarked
with an identical fluid dynamics problem of external flow
over a simplified car body. The optimization exercise comprised
of multiple geometric design variables such as draft angle,
body height above ground, and roundness, with the ultimate
design objective function of minimizing drag. ANSYS Design
Explorer created a design response surface based on many
arbitrary design point evaluations, while HEEDS used an
iterative proprietary search algorithm to constantly refine
the design based on previous evaluations. In both cases,
challenges arose in applying the methodologies to a CFD
problem due to long simulation run times required to obtain
a fluid flow solution. The performance of the two codes
appeared to greatly depend on the nature of the design space.
In the case of the car body, with many design variables,
the design space was complex enough that the HEEDS methodology
had an advantage over the use of a response surface approach.
In other problems, where multiple competing objective functions
are present, the ANSYS Design Explorer response surface
approach is superior, and can generate a Pareto Front tradeoff
analysis much quicker than the iterative approach. Overall,
with the significant computational cost of CFD simulations,
both optimization codes require preemptive and intelligent
simplification of the problem before starting the optimization
process.(audio and slides available here)
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November 1, 2011
Fields Institute,
Room 230
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5:00 - 6:00 p.m.
Hans J. H. Tuenter (University of Toronto)
The Modeling and Forecasting of Wind Energy
Wind energy is becoming a larger part of the electricity generation
mix, and with it the need for accurate forecasting of wind energy.
We describe a fundamental model that combines turbine characteristics
and meteorological forecasts. The model has been used succesfully
to provide short-term forecasts for a major utility.
(audio and slides available here)
6:00 - 7:00 p.m.
William (Bill) Smith (Siemens)
Wind Energy in Canada
Wind energy is playing an increasingly important role in the generation
mix in Canada. We describe the global market for wind power, and the
market in Canada on a provincial basis. We outline the technological
developments that are driving the cost structure and efficiency of
wind turbines. These improved characteristics will bring wind energy
closer to parity with other generation sources.
(audio and slides available here)
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October 4, 2011
Fields Institute,
Room 230
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2:00 - 2:50 p.m.
Robert Stubbs (Axioma Inc.)
Factor Alignment Problems in Optimized Portfolio Construction
Construction of optimized portfolios entails the complex interaction
between three key entities: the risk factors, the alpha factors and
the constraints. The problems that arise due to mutual misalignment
between these three entities are collectively referred to as Factor
Alignment Problems (FAP). Examples of FAP include risk-underestimation
of optimized portfolios, undesirable exposures to factors with hidden
and unaccounted systematic risk, consistent failure in achieving ex-ante
performance targets, and inability to harvest high quality alphas
into above-average IR. In this talk, we present a detailed investigation
of these alignment problems, discuss their sources, analyze their
effects on ex-post performance of optimized portfolios and discuss
a practical and effective remedy in the form of augmented risk models.
(audio and slides available here)
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2:50 - 3:40 p.m.
Thomas Coleman (University of Waterloo)
Risk Management of Portfolios by CVaR Optimization
The optimal portfolio selection problem is the fundamental optimization
problem in finance – optimally balancing risk and return, with
possible additional constraints. Unfortunately the classical optimization
approach is very sensitive to estimation error, especially with respect
to the estimated mean return, and the resulting efficient frontier
may be of little practical value. Indeed it may be dangerous from
a risk management point of view. A popular alternative, usually under
the banner of “robust optimization” is ultra-conservative
and, we argue, not really robust! In this sense it may also be of
questionable practical value. We propose an alternative optimization
approach – a CVaR optimization formulation that is relatively
insensitive to estimation error, yields diversified optimal portfolios,
and can be implemented efficiently. We discuss this promising approach
in this talk and present strongly supportive numerical results.This
is joint work with Yuying Li and Lei Zhu
(audio and slides available here)
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