This week’s journalist is Peter Yun Zhang, an M.A.Sc. candidate in the Mechanical and Industrial Engineering Department at the University of Toronto. He is currently working with the ATOMS lab.

This interview is about the wind farm layout optimization project at the ATOMS lab. In short, this research problem deals with the optimal placement of turbines in a wind farm such that the energy and noise objectives are optimized while respecting power and locational constraints.

Dr. David A. Romero is currently a Post-Doctoral Fellow at the Mechanical & Industrial Engineering Department, University of Toronto. He formerly held a position as Profesor Asociado in the Escuela de Ingenieria Mecanica, Universidad del Zulia, Maracaibo, Venezuela. David holds M.Sc. (2003) and Ph.D. (2008) degrees in Mechanical Engineering from Carnegie Mellon University.

David’s research interests are in applied computing, statistics and mathematics in support of engineering design, modeling and optimization, particularly in the thermal sciences. His previous work experience includes surrogate-modeling based optimization of thermal systems and of enhanced oil recovery methods, as well as consulting work in dynamic simulation of thermal/fluid flow systems and evaluation of wind energy resources. Current projects involve the thermal optimization of nano-scale devices with consideration of sub-continuum thermal transport effects, and optimization of wind farms.

What is the scope of the wind farm layout optimization project at the ATOMS lab?

The vision is to have a piece of software that can be integrated into the sponsoring company’s workflow, so that it automates the entire wind farm design process: design automation. For example, given the number turbines, types of resources, and some constraints such as maximum number of turbines and maximum connection power, what are the best layouts that we can produce?

What is the status of the literature in this field? What are the leading software packages?

Most software packages do energy optimization only, whereas our goal is to provide a multi-objective optimization tool that supports simultaneous energy and noise optimization. Most of them use heuristics and almost no one uses formal OR methods. There are some papers in genetic algorithms. Overall, there is a need for better documentation in the literature in terms of the experimental details so that future papers can better benchmark against current optimization strategies. In the past, genetic algorithms (GA) are used for a discrete version of the problem. We are applying GA to a continuous version of the problem and at the same time using some other OR methods.

Do we know how the software packages are implemented?

We don’t, except for OpenWind, which has an open source version. The documentation and theoretical development for some software packages in other fields (such as ANSYS for CFD) are very well documented and publicly available. There is a substantial gap between the wind farm design field and other more mature ones, especially in terms of commercial software’s technical documentation.

Who are the main players in terms of academic groups and companies?

DTU (Denmark) is a leading institution on this, and Europe in general is doing very well. For example, Germany has very high wind energy penetration rate. In Europe, there are some interesting studies about hydrogen-based fuel cells and electrolyzers so that wind energy can be stored and potentially transported. If you have distributed, small-scale storage things, then it’s very good for wind.

How is Canada doing in this field?

Regulations in Canada are fairly strict, and some health studies are still underway, it is hard to predict which way policy will swing. If negative feelings for wind energy out-weigh the benefits, we could see a slow-down in this field for at least a couple of years. Eventually we are going to have a mix of energy technologies. In the long term, there is going to be more development. That’s just my personal opinion. I think there’s a good opportunity in the US, because they have a lot of good resources, and the market is not yet saturated.

Overall, wind farm design, is definitely a multidisciplinary problem – mechanical, civil, environmental, electrical, policy, and optimization. How would OR specialists apply their knowledge in these kind of projects?

It has to happen either way – someone with mechanical/energy background picking up optimization methods (which is what I have done), the other way is for OR people to pick up energy literature. From the OR point of view, you just need a mathematical model to describe the behavior of the physical system. So if someone with an OR background works on this problem, it probably takes 6 or 8 months to learn about the mechanical and energy side of things. There is a learning curve, but nothing too much.

What are the types of OR problems in this context?

We have been studying this problem under heaviest k-subgraph and vertex packing problems, among others. And these are pretty much the only ones. The infrastructure layout optimization would involve minimum spanning tree algorithms. Traditional OR likes linearity, but this system is fairly nonlinear. But these nonlinearities are not badly behaved. For example, power loss is just a square function of the cable length – not a crazy nonlinearity. I think many things can be done in this project by using standard OR methods intelligently.

On March 5th 2013,  Professor Christopher Beck gave a lecture on constraint programming entitled Everything You Wanted to Know about Constraint Programming But Were Afraid to Ask.

This lecture, organized by UTORG, is part one of a two-part lecture series given by Professor Beck about constraint programming. Professor Beck has graciously allowed his slides to be posted online. They are available here.

Stay tuned for Part Two next week!

Three years ago, graduate students studying Operations Research (OR) at University of Toronto did not know much about each other’s work. I could not help but feeling myself narrow-minded without knowing how others applied OR differently. An OR seminar/group kind of thing would help – I thought, and that’s how UTORG got started. Starting a student group is not a new thing in academic. But I hope my sharing of how UTORG was built from scratch can be somehow helpful to students who are driven by the same passion.

Unlike serving in an already-built organization, where the task is more to do with keeping the ball rolling, or making a bigger ball to roll, starting a new group is to come up with a ball and keep pushing it until it rolls. Apparently, if the ball that you start off pushing is too big, the ball could end up not moving a single bit regardless how much time or efforts you put on it. Such possible waste of time/effort can be quite unbearable especially to graduate students who already feel short of time for their own research activities. Overall, UTORG was built following closely the idea of “lean startup”—we kept everything as simple as possible.

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If you’re reading this blog, you know what operations research is, and therefore, you must also know what industrial engineering is. Everyone in IE knows what IE is. Or so we like to tell ourselves. Industrial engineering is a relative newcomer to the world of engineering, and in its short existence—much like Darwin’s finches—the field has undergone rapid evolution and speciation into various specialties. Operations research is one of those specialties, as are human factors and information engineering.

The ascension of industrial engineering

Elevator

What does an IE have in common with an elevator? They both … I don’t know.

In the early 20th century, elevators were a key component to the proliferation of high-rise buildings across industrialized cities. As the buildings got taller though, elevator speeds remained the same, and eventually passengers began complaining that the wait for elevators during peak hours was unacceptably long. At one hotel in New York, the manager commissioned a study to improve elevator performance, but the study was unsuccessful in finding an economically reasonable method to make the elevators faster. However, one recent psychology graduate on staff at the hotel noticed that people were complaining about waiting only a few minutes. So, he put up mirrors in the elevator boarding areas so that the passengers would be happily entertained by their reflections while waiting. Magically (because psychology is black magic), the complaints stopped!

I heard that story during a keynote presentation at a conference several years ago, and learned from the speaker that the elevator experiment ended up being the first ever publication in the field of industrial engineering. The story is one of my favorite anecdotes to tell about IE, and I’ve retold it more than a few times over the years. However, in “researching” this blog entry, I haven’t been able to find any corroborating information that the elevator thing was ever published; I can’t even find an actual year in which the study took place. I wish I could remember the speaker’s name so I could tell him how upset and disillusioned I am. Or maybe I should just go stand in front of mirror for a few minutes until I feel better.

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It’s a cold, hearty Canadian morning and you need a coffee. So you saunter over to the coolest hipster coffee-shop in town, OR Café, and plop yourself down on one of the massively comfy couches. You barely get yourself comfortable when you feel a soft tap on your shoulder. Turning, you see Curtiss and Shefali, the friendly store managers.  You smile at them, but you quickly realize that all is not well. Continue reading

This week’s blog entry is written by Jenya Doudareva, an MASc student at Centre for Research in Healthcare Engineering and a literature, art, and music aficionado. You can reach her at  jenyadoudareva [at] gmail [dot] com.

The previous entries have set up OR to be serious business. With applications ranging from military, supply chain management to finance, and even to healthcare and medicine, it is clear that OR has a lot to offer to the world. Personally, I find such diversity of a discipline to be refreshing and alive…creative even. To see that a problem in healthcare and a completely different problem in finance can be modelled using the same underlying principles is astounding.

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