Early career honor for Wang

UTA research earns grant to dynamically optimize supply chain management

2023-03-22

(Press-News.org) A University of Texas at Arlington researcher is working to optimize supply chain management to allow for flexibility from forces outside the supply chain, such as policy changes that can cause major disruptions.

Linda Wang, assistant professor in the Mechanical and Aerospace Engineering Department at UTA, has earned a five-year, $503,000 Faculty Early Career Development Program (CAREER) grant from the National Science Foundation (NSF) for her research. CAREER awards are the NSF’s most prestigious honor for early-career faculty members.

Current supply chain analysis tools rely on static optimization and are inflexible when policy changes or other outside elements disrupt the chain. When an issue arises, the supply chain must be redone.

Wang hopes to introduce optimal network control concepts that allow dynamic management of the supply chain, enabling agile reaction to changes and effective decision-making. With static optimization, users assume all of the variables and try to control them; if the parameters change, they have to redo the process.

Wang is testing her dynamic system to see if it will allow users to adjust on the run to save time and money. The dynamic process also helps with sustainability, since it can connect multiple layers of the supply chain that appear separate but may share common segments.

“The main selling point is that the dynamic model bridges two worlds,” Wang said. “Supply chain management works with business, but it’s not engineered to be systematic. Optimal control will save lots of money in determining the correct portfolio and how to manage it. It can connect the producer with the receiver and accomplish both tasks more efficiently.

“People have realized that what they were doing doesn’t always work. When supply and demand or needs change, it can affect supply chain networks. People are thinking of alternatives, and we’re lucky to be in a position to show that our model works well enough to be applied in real life.”

Written by Jeremy Agor, College of Engineering END

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Early career honor for Wang

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