Optimizing Resource Flows and Logistics in Hubs for Circularity
PhD candidate: Jiayun Wang
The transition to a circular economy requires industries to move from isolated production systems toward coordinated, resource-sharing networks. A Hub for Circularity (H4C) is an emerging model in which companies located in close proximity exchange by-products and waste both within the industrial cluster and with surrounding urban and rural communities. By transforming waste into valuable inputs, H4Cs aim to reduce environmental impact, generate economic value, and strengthen regional resilience.
In practice, operating such a hub is highly complex. My PhD research develops optimization-based decision-support models spanning the full life cycle of H4Cs. The framework encompasses three interconnected layers: a matchmaking model for configuring the exchange network, an investment model for evaluating infrastructure sizing and technology selection, and an operational model to dynamically schedule production, storage, and logistics using real-time data.
Methodologically, we model sequential decisions under uncertainty using Markov decision processes (MDPs). Because exact solutions become computationally intractable in large-scale networks, we develop approximate dynamic programming (ADP) techniques to derive scalable solution policies. In addition, a multi-objective optimization framework is integrated to explicitly represent trade-offs between economic performance and environmental impact.
