Learning to Balance: Equitable Districting and Routing in Last-Mile Logistics via Graph Neural Networks

Abstract

This work provides a data-driven, deep learning-based solution to the districting and routing problem. Related previous solution approaches focus on cost minimization and face limitations by yielding highly imbalanced districts. This imbalance can cause practical problems such as excessive service times, low customer satisfaction, and unfair workload distribution among deliverers. We propose a deep learning-based solution architecture based on Graph Neural Networks that integrates balance-awareness into the learning process. Evaluation on a large set of real-world cities demonstrates that our approach achieves a significant improvement in workload balance.