CIM PROGRAMS AND ACTIVITIES

5:00 p.m.

Frédéric Meunier, CERMICS, Ecole Nationale des Ponts et Chaussées, France

A routing problem raised by self-service bicycle sharing systems (slides)

Abstract: Operating bicycle-sharing systems, such as the Vélib' system in Paris or the Bixi system in Montréal or Toronto, raise many challenging problems. The repositioning of the bicycles using one or more truck is one of the most natural such problems. In this talk, we focus on the case of a single truck. We are given a graph whose vertices model the stations. Assuming that the current distribution of the bicycles is known, we are to move the bicycles using the truck to reach a target distribution at minimal cost. This problem corresponds to the situation at the end of the night when very few bicycles are moving. The talk will present special polynomial cases as well as approximation algorithms. An efficient method solving reasonably sized practical instances will be presented. The method combines the exact computation of a natural lower bound and a local search exploiting theoretical properties of the problem. Related open questions will be discussed.

Talk partially based on join works with Daniel Chemla and Roberto Wolfler Calvo.

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6:00 p.m.

Paul Raff and Prasad Kalyanaraman, Supply Chain Optimization division of Amazon.com

Opportunity Cost Techniques and Fulfillment Tie-Breaking at Amazon.com (slides)

For an online retailer that receives thousands of orders per minute and focuses obsessively on customer satisfaction, Amazon.com does not have the ability to optimize fulfillment decisions over a time frame and must instead make fulfillment decisions greedily, which is sub-optimal. This talk will give a broad detailed overview of the problem, and of the various ways in which Amazon.com is attacking the problem. The majority of the time will cover the fulfillment concept of tie-breaking, which combines the theory of opportunity cost with the recognition that in a large proportion of cases, no extra money need be spent. Various examples will be given that exhibit the core problem, and the theory of tie-breaking will be built up from the basics. Various simulations – simple and complex – will be shown that demonstrate the effectiveness of tie-breaking.

March 6,
Fields Institute,
Room 230

5:00 p.m.

Michael Chen (York University)
A stochastic integer programming approach to the optimal thermal and wind generator scheduling problem (slides)
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.
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Peter Hall (Arcelor-Mittal Dofasco)
The Practicality of Applying Optimization in the Steel Industry
I will present 3 problems where optimization can be applied in the Steel industry and specifically to areas within Supply Chain Planning and Scheduling. Along with the presentation of these problems, I will discuss the background behind these problems and some of the pros and cons of using common optimization modeling techniques to address these problems.
The first problem deals with the balancing of demand and supply between Steel Making and Finishing operations within a fully integratedSteel mill given that the manufacturing and business objectives of both areas differ.
The second problem, campaign planning, addresses technological constraints in an environment where these constraints hinder the main KPI of on-time delivery and where optimization, although very applicable, also conflicts with current work processes.
The third problem will be posed at a very high level that is intuitive to applying common network optimization modeling and solution techniques but which also poses some questions about practicality of these techniques given knowledge base of the work force, lack of cost and pricing information, and the politics of using packaged software solutions vs. custom in-house applications.

February 7, 2012
Fields Institute,
Room 230

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)
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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)

December 6, 2011
Fields Institute,
Room 230

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)

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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)

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)

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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)

October 4, 2011
Fields Institute,
Room 230

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)