When Design Exploration created the World Cart they wanted all the pieces to come from one piece of plywood to make it easier to ship to developing countries and more affordable for the people that need it. Design Exploration also wanted the cart to be structurally sound and carry as much weight as possible. By using a standard optimization model and MatLab, Design Exploration was able to achieve it’s goal fairly easily.

While normal optimization methods work for something simple like the World Cart things get more expensive and time consuming when it comes to optimizing complex systems like the international space station or a power plant.

“I worked on designing an airfoil for a jet turbine with the air force. The airfoil would have reduced sonic shockwaves. We ended up scrapping the project because the optimization methods available would have taken three and a half years and 80 computers to run. It would have been way too expensive,” said Design Exploration's Braden Hancock.

This setback prompted Hancock to invent a better method. Traditional optimization methods give equal priority to all possible solutions. Hancock’s method gives priority to only the best solutions.

“With my method it would have taken us only about ten days to run the optimization and would have made designing the airfoil possible,” Hancock said.

Hancock’s method is called the smart normal constraint method and is a programming code that can work with MatLab and other software. Hancock expects his method can be used to create designs for cars, power plants, groundwater systems and even space stations that would have been unrealistic before.

"Solving the worlds challenges isn’t going to come from simple solutions. We’ve had simple solutions for thousands of years and we still have many of the same problems. To solve these challenges we need to explore the best possible solutions, said Dr. Mattson the faculty director for Design Exploration.

“I think we can make the world a better place if we teach computers to help us solve some of its problems,” said Hancock. 

Here's a link to one of our more recent publications on "Smart Optimization"