April 18, 2014

Fields Industrial Optimization Seminar

Supported by

The inaugural meeting of the Fields Industrial Optimization Seminar took place on November 2, 2004.. This series will meet once a month, on the first Tuesday, in the early evening. Each meeting will comprise two related lectures on a topic in optimization; typically, one speaker will be a university-based research and the other from the private or government sector.We welcome 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.

Seminars start at 5:00 pm at the Fields Institute, 222 College Street, Room 230

May 2, 2006 --Energy Markets: Theory and Practice.

Peter J. Vincent, Manager Quantitative Analysis, Risk Services, Ontario Power Generation
Introduction to Energy Markets
This talk gives an overview of the basic principles that govern energy markets, and discuss the characteristics and interaction of load, transmission and generation.

Hans J.H. Tuenter, Senior Model Developer, Planning & Analysis, Ontario Power Generation
Mathematical models in Energy Markets.
This talk gives an overview of the different mathematical models and techniques that are being used in energy markets.

Matt Davison, Associate professor of Applied Mathematics and Faculty of Science Scholar at the University of Western Ontario.
Valuation and Optimal Control of Energy Assets.
This talk represents joint work with Matt Thompson and Henning Rasmussen. I will discuss the ways in which real options theory can be used both to value and to determine optimal control strategies for real energy generation assets. After motivating the analysis and developing a general framework for analyzing such assets, we will look at the particular challenges of optimizing three particular assets -- a thermal (coal fired) power plant, a pump storage hydroelectric unit, and a natural gas storage facility. I will close by discussing current work on extending these ideas to other energy assets.


April 4, 2006

Workshop on IMRT Radiation Therapy Treatment Planning: Target Uncertainties, Computational Challenges and Beyond
Eva Lee (Georgia Tech)
Large-Scale Optimization Strategies for Optimal Cancer Treatment Design
Joseph Deasy (Washington University)
Computational challenges in radiation oncology

March 7, 2006

Daniel E. Rivera, Department of Chemical and Materials Engineering, Arizona State University
"Plant-Friendly" System Identification: A Challenge for the Process Industries

Dynamic modeling is a critical task to many problems in the areas of simulation, prediction, and control of process systems. Most industrial plants are either too complex, or the underlying processes too poorly understood, to be adequately modeled using first principles. A sensible approach, then, is to estimate dynamical models from data generated through well-designed experiments; this is the problem of system identification. Certain industries, such as petrochemicals and refining, rely almost exclusively on system identification as the principal means for obtaining the dynamic models necessary to design advanced control systems.

The term "plant-friendly" system identification has been used within the chemical process control community in reference to the broad-based goal of accomplishing informative identification testing while meeting the demands of industrial practice. In the formulation developed in this talk, a priori knowledge available to the engineer is used to specify a frequency spectrum for a multisine input signal. An optimization problem is then solved which seeks to find the optimal phases of the multisine signal (and additionally, the Fourier coefficients in frequency bands not specified by the user) that directly minimize friendliness criteria such as crest factor. The optimization problem is solved in the presence of explicit time-domain constraints on upper/lower limits and rate of change in either (or both) input and output signals. Such a constrained time-domain formulation is appealing to process control engineers, who tend to think more in terms of maintaining high/low limits, move size constraints, and test duration during identification testing, and less in terms of norm criteria that are typically used in the classical optimal experimental design formulations.

The optimization-based framework developed in this talk is illustrated with an application to a high-purity distillation column, a demanding nonlinear and multivariable process system. The paper concludes with a discussion of identification test monitoring as an important novel paradigm for accomplishing plant-friendly identification.


Hans D. Mittelmann, Department of Mathematics and Statistics, Arizona State University
State-of-the-Art in the Solution of Control-Related Nonlinear Optimization Problems

We start by giving an overview of some of our activities related to the computational solution of a range of optimization problems, including a leading web-based guide to software and its performance and a major installation of free web-based solvers.

Then we sketch two classes of problems from our recent research, PDE constrained optimization as it arises in the control of PDEs and system identification problems. The first class gives rise to very large and sparse nonlinear optimization problems that still challenge state-of-the art algorithms including commercial products.

The second class of problems are system identification problems including those suitable for data-centric estimation and control. At first we will address the solution of the crest-factor optimization problems described in the previous lecture, then we will introduce a novel optimization formulation which has facilitated the application of MoD (Model-on-Demand) type of control for process systems. For all problems considered formulations in the modeling language AMPL are utilized and we sketch some exemplarily.

February 7, 2006

Jorge Nocedal, McCormick School of Engineering and Applied Science, Northwestern University
New Developments in Nonlinear Optimization

In the last 5 years, the field of nonlinear optimization has grown and evolved at a rapid pace. We now have a much better understanding of constrained optimization at both the algorithmic and theoretical levels. This talk reviews these recent developments.

Nonlinear optimization is also expanding in new directions, guided by applications in financial engineering, integrated circuit design, and wireless communications. In all these areas it is important to solve optimization problems with complementarity constraints. These problems pose challenging theoretical and algorithmic challenges, which will be discussed in this talk. Some recent successful applications will be presented.


Kankar Bhattacharya, University of Waterloo
New NLP problems for Power System Analysis and Operation in Competitive Electricity Markets

This presentation discusses in detail several novel applications of NLP to the analysis and operation of power grids in the context of competitive electricity markets. In particular, various new NLP models designed and developed for dispatch and clearing of power networks and their associated markets are presented, emphasizing their unique characteristics, such as the introduction and handling of implicit constraints and the use of heuristics to transform the optimization problem into an NLP problem, which exploit the particular characteristics of these optimization problems so that they can be solved more efficiently. These optimization problems are compared with respect to more classical models currently being used by industry to highlight their unique characteristics and challenges.


December 6, 2005

John Birge, University of Chicago
Research Challenges and Opportunities for Optimization in the Energy Sector

The energy sector presents multiple opportunities for optimization applications while also presenting numerous challenges. This talk will focus on these issues for electricity markets with an emphasis on common characteristics in other energy markets: high volatility, dynamic and distributed decision making, estimation difficulties, limited storage capacity, and significant fixed costs.

The talk will consider the impact of these factors on optimization models, current methods to address these issues, and directions for further research.


Samer Takriti, IBM
Stochastic Programming Applications in Deregulated Energy Markets

Until the early 1990s, the US electric power utilities were fully regulated with captive customers and controlled tariffs. However, the deregulation of the energy market, which began in 1992 with the Energy Policy Act, changed market dynamics by opening the transmission system and creating hubs for trading energy. As a result, most utilities were faced with increased competition and opportunities. In this talk, we discuss energy markets and associated uncertainties from the point view of a power producer. We then discuss two problems, namely the unit commitment problem and the problem of selecting bids in the next-day energy market, which we model as mixed-integer stochastic programs. We present numerical results for work with two energy clients.

November 1, 2005

Natalia Alexandrov, NASA Langley Lab.
Managing models in simulation based design optimization

Advances in numerical modeling and computational power enable increasingly accurate simulation of physical and engineering phenomena. However, the enormous cost of repeated high-fidelity simulations, such as the Navier-Stokes equations or those based on fine computational meshes, makes the use of high-fidelity models impractical in the context of single-discipline or multidisciplinary design optimization.

Approximation and model management optimization (AMMO) combines the use of general variable-fidelity models with analytically substantiated algorithms to improve tractability of design with high-fidelity, expensive models while preserving provable convergence properties. While in demonstrations to-date AMMO has produced significant savings as compared to algorithms that use a single-fidelity model, these savings are highly dependent on the non-local properties of the lower-fidelity models. In this talk, we discuss the attempts to detect these properties and use them to speed up algorithms where the model quality is uncertain.
David Zingg, University of Toronto
Topics in Aerodynamic Shape Optimization

This presentation will focus on two important topics in aerodynamic shape optimization. First, a comparison of a gradient-based and a gradient-free strategy will be presented for several single-point, multi-point, and multi-objective aerodynamic optimization problems. The gradient is computed using the discrete-adjoint approach. The two algorithms use the same geometry parameterization and produce identical flow solutions for a given parameterization; consequently the two design spaces are identical. The objective is to assess the dependence of the relative cost of the two approaches on the nature of the problem, the number of design variables, and the degree of convergence required. The results indicate that the genetic algorithm is better suited to preliminary design, while the gradient-based algorithm is more appropriate for detailed design.

The second portion of the presentation will focus on airfoil optimization under variable operating conditions. Examples will be shown to demonstrate that it is difficult to pose a multi-point optimization problem a priori and that the feedback from the optimization can lead to better problem specification. A technique will be presented to automatically select sampling points and their weights in order to achieve desired performance over a range of operating conditions, in this case constant drag over a range of Mach numbers. The results provide insight both in formulating and in solving multi-point aerodynamic optimization problems.

October 4, 2005

Ellis Johnson, Georgia Institute of Technology, Atlanta GA
Imposing Station Purity using Station Decomposition

Fleet assignment models are used by many airlines to assign aircraft to flights in a schedule to maximize profit [Abara 1986, Hane et al 1995]. A major airline reported that the use of the fleet assignment model increased annual profits by more than $100 million [, 2002] a year over three years. The results of fleet assignment models affect subsequent planning, marketing and operational processes within the airline. Anticipating these processes and developing solutions favorable to them can further increase the benefits of fleet assignment models. We develop fleet assignment solutions that increase planning flexibility and reduce cost by imposing station purity, limiting the number of fleet types allowed to serve each airport in the schedule [Smith and Johnson 2005]. Imposing station purity on the fleet assignment model can limit aircraft dispersion in the network and make solutions more robust relative to crew planning, maintenance planning and operations.

Because imposition of station purity constraints can significantly increase computational difficulty, we develop a solution approach, station decomposition, which takes advantage of airline network structure. Station decomposition is an instance of Dantzig-Wolfe decomposition and uses a column generation approach to solving the fleet assignment problem. We further improve the performance of station decomposition by developing a primal-dual method that increases solution quality and reduces running times. This method can be applied generally within the Dantzig-Wolfe decomposition framework to speed convergence. It avoids "instability of the duals" and minimizes the "tailing" effect.

Station decomposition solutions can be highly fractional causing excessive running times in the branch-and-bound phase. We develop a "fix, price, and unfix" heuristic to efficiently find integer solutions to the fleet assignment problem.

Station purity can provide benefits to airlines by reducing planned crew costs, maintenance costs, and the impact of operational disruptions. We show that purity can provide compelling benefits (up to $29 million per year) to airlines based on reduced maintenance costs alone. Benefits associated with reduced crew costs are estimated at $100 million per year, giving $129 million per year increased profit. We would expect additional savings in operations.

Abara, J. (1989), "Applying Integer Linear Programming to the Fleet Assignment Problem" Interfaces 19 pp. 20-28.
Hane, C. A., C. Barnhart, E. L. Johnson, R. E. Marsten, G. L. Nemhauser, G. Sigismondi (1995), "The Fleet Assignment Problem: Solving a Large-scale Integer Program" Mathematical Programming 70 pp. 211-232.
Smith, B. C., E. L. Johnson (2005), "Robust Airline Fleet Assignment: Imposing Station Purity using Station Decomposition", submitted


Stefan Karisch, Carmen Systems, Montreal QC
Applying Optimization in the Airline Industry

What does it take to successfully apply optimization in the airline industry and build a company around it? I will try to answer this and related questions in my presentation and use Carmen Systems as an example. Carmen develops and implements resource management and optimization solutions for demanding transportation operations.
Clients include five of the ten largest airlines in the world, namely Air France, British Airways, Delta Air Lines, Lufthansa, Northwest Airlines, and Deutsche Bahn (German Railways), one of the largest passenger transportation companies in the world.

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