CAREER: A Multi-layer Dynamic Network Control for Agile, Optimized, and Sustainable Supply Chains

University Of Texas At Arlington, Arlington TX

Investigators

Abstract

This Faculty Early Career Development (CAREER) project will support an integrated research, education, and outreach program in supply chain network (SCN) management. The main aim is to broaden the focus of SCN management from economic efficiency to accuracy, agility, and sustainability through dynamic network modeling and data-driven decision-making methods. The current supply chain analysis tool, relying on static optimization, is insensitive to non-eligible changes such as policy changes that may cause economic turmoil. By bridging supply chain management with the optimal network control concepts, this work will interpret the processes of producing and distributing goods of an established SCN in the dynamic form to capture the non-negligible changes more rapidly and maximize the utility. In addition to ushering in the concept of dynamic modeling for agile reaction to changes and effective decision-making on network management, this work will further discuss supply chains involving recycling processes through a multi-layer network model. The broad applicability of this work will provide expert guidance to local industries for improving SCN efficiency, profit, or recycling and will further promote awareness of the current waste issues and facilitate the local community's engagement toward green manufacturing and sustainability. In addition, in the long run, this project will deliver reliable solutions for many other SCNs that are crucial for the economic developments of many companies in related fields. This study will also adopt dynamic planning concepts for decision-making related to STEM student recruitment, retention, and training, which will facilitate engineering education reform from experience-based management to data-driven strategic planning. The research will address problems related to the current poor management of SCNs that heavily rely on the power of computational devices by modeling supply chains as dynamic systems that capture the non-eligible changes and transforming SCN management from static optimization to model-based dynamic control. This model enables quantitative analysis of optimal strategies such as daily production and optimal distribution route, where a parallel computing scheme will be developed for computational efficiency. A demonstration metric on the feasibility and applicability of the proposed work, based on dynamic modeling, will be introduced for evaluation, which sets the foundation for the exploration of other complex dynamic networks. Furthermore, a multi-layer network will be adopted to improve the efficiency of SCNs, which will result in discussions on applications involving recycling processes for sustainability management. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.