An Optimization Framework for Managing Resource Flows in Hubs for Circularity

Abstract The hub for circularity (H4C) is an emerging concept that extends the traditional boundaries of industrial symbiosis networks by facilitating resource exchanges not only within an industrial cluster but also with surrounding urban and rural areas. Unlocking the economic, environmental, and social benefits of such hubs requires efficiently exchanging different types of resources across space and time. This remains a major challenge as it involves cross-company coordination, managing trade-offs between economic and environmental sustainability goals, and accounting for uncertainties in waste availability, renewable energy generation, and market prices, among other. To address this challenge, in this study we first identify critical research and practical gaps through a comprehensive literature review on resource flow optimization in H4Cs and related concepts. Building on these insights, we propose a two-phase framework to model and optimize the H4C operations. The first phase involves data collection and synergy identification, which is illustrated in two real H4Cs in Spain and Türkiye. The second phase integrates predictive and prescriptive analytics to guide decision making in daily hub operations while accounting for uncertainties. Our study shows that different predictive and prescriptive methods are available to optimize resource flows in H4Cs. However, selecting and integrating the most appropriate techniques is non-trivial as it depends on the hub scale, data availability, uncertainty dynamics, and preferences of the decision maker. Thus, the proposed framework offers practical guidance for hub managers in choosing appropriate modeling and optimization tools, thereby supporting the implementation of efficient and sustainable H4C operations.