- Engineering Workflow Automation
- Design Space Exploration
Whatever game you’re trying to win, one thing is key: you need to make the right decisions at the right time to take the lead and stay ahead of your competitors. It’s pretty much the same for any engineering team working on a new product design. Typically, they’re all confronted with what is usually referred to as ‘a multiobjective optimization problem’. Such optimization challenge requires them to balance many (conflicting) design objectives, of which the ‘maximum performance against minimum cost’ equation is probably the most well known.
The only way to resolve conflicts between competing objectives, is accepting a tradeoff. Typically, all tradeoff solutions are located on the so-called Pareto front. Each Pareto optimized solution cannot be further improved with respect to a single objective without degrading one or more of the other objectives. Selecting the ultimate solution essentially remains a subjective decision, which requires the engineer to get the priorities straight: should performance take slight precedence over cost, or vice-versa? Or should another objective be taken into account?
When dealing with just two design objectives, the priority setting remains relatively straightforward. But in real-life product development you’re dealing with many design objectives and a lot of different design parameters. Then it gets a lot more difficult to interpret which Pareto optimized solution suits your specific requirements best. The exercise of balancing objectives can get really complex in this situation. Without the right tools, it’s extremely difficult to assess and understand the merits and demerits of all the different Pareto optimal configurations.
A capable design engineering process must therefor focus on mastering multiple design objectives simultaneously and retrieving the optimum balance between the targeted objectives.
That’s where it really gets difficult for the human brain: processing each Pareto optimum in detail and performing priorities-based comparison with all other Pareto optimal solutions is just too much. It is as if the decision maker operates independently from the optimizer, finding it rather difficult to make an informed choice among the many Pareto optimal solutions presented by the optimizer. Also because that human effort usually starts only after completing the design optimization process, during which a massive number of simulation experiments have been performed.
More human interaction during the process of multiobjective optimization could be the answer. Technologies supporting this would make proper decision making practically feasible for project teams within shorter development time frames. The human interaction would enable them to follow up on the ongoing engineering optimization process, and influence the balancing strategy based on the priorities they define.
That’s exactly what FINNOPT’s interactive multiobjective optimization is about. It allows engineering decision makers to interactively prioritize and submit additional information during the design optimization process. FINNOPT is a Finnish consulting and software services provider. The company says that their interactive multiobjective optimization approach allows the user to guide the solution process toward the most suitable optimal design configuration, without wasting computational resources on uninteresting solution configurations.
The ability to process multiple objectives in an interactive way, is what makes the FINNOPT software truly unique in the market. With FINNOPT, users can direct the process of generating the Pareto front of optimal configurations – while it’s actually happening. Information intermediately provided by FINNOPT allows engineers to provide extra input by tuning priorities and balancing design objectives. While steering the on-going multiobjective design optimization in certain directions, a reduced number of experiments needs to be executed. That means a faster path to identifying the optimum configuration that fits your specific application and situation.
The FINNOPT approach has already been applied successfully in many industry sectors. In process industries, the design of bilevel heat exchanger networks is synthesized using interactive multiobjective optimization. As a result, the optimized design enables heat recovery in a more cost efficient manner. Another example relates to the optimization of a wastewater treatment plant. In this case interactive multiobjective optimization was performed with five conflicting evaluation criteria, which led to meeting significantly tighter environmental and economical requirements.
Interactively balancing 5 conflicting objectives during the interactive optimization of a wastewater treatment plant using FINNOPT technology.
Sometimes, optimization is a matter of life and death. Think for instance about the use of brachytherapy to irradiate cancerous tumors in patients while not causing damage to healthy tissue. The interactive approach using FINNOPT enables treatment planners to identify the most preferred treatment plan, using their knowledge and preferences during the optimization process. That makes planning times shorter and improves the treatment plan’s quality - fulfilling the prescribed dose to the tumor, while minimizing the dose in each organ at risk.
Thanks to Noesis Solutions’ exclusive partnership with FINNOPT, Optimus users now also gain access to the world’s first commercial interactive multiobjective optimization algorithms – empowering engineering decision makers to interactively deal with a large number of conflicting objectives.
Based on the preferences entered by the decision maker, new tradeoff solutions are computed and introduced. Throughout the process, decision makers learn about the interdependencies between (conflicting) objectives. They receive feedback about what compromise solutions exist based on the preference information they have entered.
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