Join the first edition of the new ABW Research Webinar series with Professor Dr Andrew Trapp!

We are thrilled to inaugurate the Analytics for a Better World (ABW) Research Webinar Series—a new global platform connecting researchers who apply analytics, data science, and optimization to address real-world challenges faced by NGOs, NPOs, and mission-driven organizations working in low-middle income contexts. This inaugural edition will be on “S WAP-and-Route: Fast Resource Exchange and Collaborative Routing for NPOs,” delivered by Professor Dr Andrew Trapp from Worcester Polytechnic Institute.

The session will be online on Zoom. Please find the details below:

Nonprofit organizations (NPOs) provide essential resources and services in response to community-focused emergency needs. Yet, these NPOs often face challenges in quickly securing resources and coordinating with other organizations. To address these challenges, we introduce a novel framework to share and transport resources during emergencies, designed for nonprofit organizations operating under a strict time horizon. Extending the SWAP platform for resource sharing (Huang et al., 2023), the SWAP-and-Route auction mechanism allows nonprofits responding to emergencies to declare their needs and available resources and provides optimized exchange and routing plans. In particular, once a certain market saturation is reached, our framework activates and identifies optimal resource exchanges while assigning and scheduling pickup and delivery tasks. Our approach eliminates redundant trips by leveraging horizontal vehicle routing collaboration to optimize routes, improving service speed, ensuring efficient coordination among nonprofits, and increasing collective impact. We demonstrate the model’s scalability and effectiveness through real-world-inspired case studies, highlighting its potential for emergency response in the nonprofit sector.

Dr. Andrew Trapp is a Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute, with joint appointments in Data Science, Mathematical Sciences, and Computer Science. He earned his PhD in Industrial Engineering from the University of Pittsburgh. He focuses on developing optimization and machine learning methods that support better allocation of limited resources in systems serving underserved communities. A recurring theme in his work is translating rigorous analytics into practical decision support that improves access, equity, and outcomes. He recently served as President of the INFORMS Section on Public Sector Operations Research, and currently serves an Associate Editor for Operations Research in the Societal Impact area.