Linda Wang, assistant professor in the Mechanical and Aerospace Engineering Department at UTA
The University of Texas at Arlington (UTA) is proud to highlight the success of its faculty through programs like the Uta Early Career Program, which supports innovative research. Dr. Linda Wang, an assistant professor in the Mechanical and Aerospace Engineering Department, exemplifies this success, having been awarded a prestigious five-year, $503,000 Faculty Early Career Development Program (CAREER) grant from the National Science Foundation (NSF). This significant achievement underscores UTA’s commitment to fostering groundbreaking research and recognizing exceptional talent within its faculty. The NSF CAREER award is the most esteemed recognition granted by the NSF to early-career faculty members, marking Dr. Wang as a rising star in her field and a testament to the strength of the UTA early career program environment.
Dr. Wang’s research addresses a critical issue in modern supply chain management: the lack of flexibility in the face of unexpected disruptions. Current supply chain analysis tools predominantly rely on static optimization models. These models, while useful under stable conditions, become inadequate when external factors such as policy changes or unforeseen global events disrupt the established order. In such instances, the entire supply chain analysis process must be restarted, leading to significant delays and increased costs.
To counter this inflexibility, Dr. Wang is pioneering the introduction of optimal network control concepts into supply chain management. This innovative approach enables a dynamic management system, allowing for agile and effective responses to changes. Unlike static optimization, which assumes fixed variables and requires complete overhauls when parameters shift, Dr. Wang’s dynamic system allows users to make real-time adjustments, saving valuable time and resources.
The benefits of this dynamic approach extend beyond mere efficiency. By connecting seemingly disparate layers of the supply chain that may share common segments, Dr. Wang’s system also promotes sustainability within supply chain networks. This holistic view enables a more resource-conscious and resilient supply chain ecosystem.
“The dynamic model essentially bridges the gap between business practices and systematic engineering,” Dr. Wang explains. “Optimal control offers substantial cost savings by accurately determining the ideal portfolio and its management. It facilitates a more efficient connection between producers and receivers, streamlining the entire process.”
In a world increasingly characterized by volatility and rapid change, the limitations of static supply chain models have become apparent. Dr. Wang’s research, supported by the NSF CAREER award and nurtured within the UTA early career program, offers a timely and crucial alternative. Her dynamic model demonstrates considerable promise for real-world application, paving the way for more resilient, adaptable, and cost-effective supply chain solutions.
Written by Jeremy Agor, College of Engineering
