Kuangying Li, Assistant Professor, Wuhan University of Technology

Yifa Tang, Professor

Integrated Optimization and Learning-Based Framework for Volunteer-Driven Food Distribution

10:00-11:00 September 19(Friday), N602

This study develops an integrated framework for food distribution operations that combines optimization and learning-based methods. A mixed-integer linear model with Lagrangian relaxation and column generation is formulated to jointly address delivery assignment and routing under supply, demand, and capacity constraints. Complementing this, a graph neural network (GNN) framework with transfer learning is proposed to capture spatio-temporal patterns and enhance adaptability in dynamic, volunteer-driven environments. Applied to a case study in Wake County, NC, the methodology demonstrates strong performance compared with classical heuristics, highlighting its efficiency, flexibility, and scalability in addressing real-time food delivery challenges.

报告人简介：李匡郢，武汉理工大学助理教授。2025年5月毕业于北卡罗来纳州立大学，获运筹学博士学位。其研究方向聚焦于不确定性优化与人道主义物流，结合人工智能与传统优化方法，解决设施选址、资源分配和路径规划等问题。他曾参与美国国家科学基金会资助的疫苗分配与食物救济项目，研究成果发表在国际顶刊 Computer-Aided Civil and Infrastructure Engineering 等。