CAREER: Process Systems Engineering for Resilient Food Supply Chains

This CAREER project focuses on making food supply systems, especially those for fresh foods, more reliable. Disruptions in food supply systems lead to food waste, struggling businesses, and higher prices for consumers. This project will create math-based tools to help farmers, processors, distributors, and stores make better decisions, even during uncertain situations. These tools will consider how quickly food spoils, how people in the supply chain behave, and how the system can adapt to problems. The Wisconsin dairy industry will be used as an example because it is important to the state and involves perishable products. Overall, the project aims to improve scientific understanding, strengthen the economy, reduce food waste, and support education by developing courses, training teachers, and creating an interactive board game to help students learn about food systems. This project develops an integrated computational framework for modeling, optimizing, and analyzing resilience in food supply chains, with a particular focus on perishable products. It addresses a fundamental gap in supply chain science: the absence of unified models that simultaneously capture perishability dynamics, uncertainty, and decentralized stakeholder behavior. The first research thrust introduces a graph-based recourse-task-network representation that embeds spoilage dynamics and process constraints across all stages of the supply chain. Building on this foundation, the second thrust advances robust optimization under uncertainty through contextual uncertainty sets, enabling two-stage formulations and solution methods that connect long-term planning decisions with real-time operational adjustments. The third thrust develops multi-agent models and associated solution techniques to represent heterogeneous stakeholder objectives and interactions, supporting the analysis of cooperation, competition, and policy interventions. The Wisconsin dairy supply chain serves as a complex, data-rich testbed for model development and validation. The resulting framework is designed to improve supply chain design, enhance resilience to disruptions, reduce food waste, and provide interpretable decision support for small and mid-sized actors. By integrating mathematical optimization, simulation, and behavioral modeling, this work contributes generalizable methodologies applicable to other perishable goods systems, including pharmaceuticals and blood supply chains. Moreover, the project aligns with national priority areas such as artificial intelligence and advanced manufacturing by advancing data-driven decision-making, scalable optimization, and intelligent system design for complex, distributed production and logistics networks. 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. NSF Award ID: 2543289 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Styliani Avraamidou | Institution: University of Wisconsin-Madison, MADISON, WI | Award Amount: $555,444 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543289 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543289.html