Mario Ventresca, assistant professor of industrial engineering, received NSF funding for three years for his project, "Automated Methods for Modeling and Designing Resilient Complex Networks". 

ABSTRACT

Networks arise in numerous engineering contexts, including manufacturing, defense, energy, and transportation. Disturbances to part of a network can result in significant performance degradation or complete disruptions of functionality. This project will advance our ability to model and design networks capable of maintaining sufficient performance and functionality within a specified operational budget when subjected to various types of perturbations. Results of this research will be demonstrated in the context of supply chain networks, which are highly interconnected structures that arise as firms exchange goods to create final products. Ensuring supply chain functionality is critically important to industrial competitiveness and national defense, but many existing networks are based on over-simplified models that are susceptible to disruption and performance degradation. This research will improve the state of the art by enabling better-informed network design decisions for complex scenarios and demonstrate this through the design of improved supply chains. Outcomes of this work will include general mathematical techniques and specific design guidelines. The project will train students in this area of critical importance and produce new educational material. 

Accomplishing the project goals requires the development of efficient algorithms and advances in network science and game theory. In order to represent complex networks, an action-based system model will be utilized and specialized to the supply chain context. Model parameters will be found using multi-layer complex network input obtained from publicly available real-world supply networks and a multi-objective optimization algorithm that is guided by objectives relevant to supply chain networks. The ability to adequately and compactly represent a supply chain network will allow for tests requiring a null-model or to examine what-if scenarios. The second project goal is to devise efficient automated mechanism design algorithms for devising incentives for firms to create connections that inherently ensure network robustness to perturbations. These are termed complex network formation games, and theory will be derived to formalize them. The third project goal utilizes the modeling framework for centralized design, instead of modeling a specific network, and will compare the results obtained from the decentralized game- theory perspective in order to ascertain differences between the quality of resultant networks and to extract best design principles.