Uncertainty-Aware Decisioning for Robust Delivery Zone Partition in Last-Mile Logistics

In last-mile logistics, robust delivery zone partitioning is critical for ensuring timely service and maintaining a positive courier experience. However, existing network-structured spatial partitioning strategies often fail to address heterogeneous uncertainties in both delivery demand and courier supply. In particular, two challenges remain: (i) irregular demand patterns with weak periodicity, and (ii) stochastic courier availability. To this end, we propose the Prediction and Sampling Then Optimization (PSTO) paradigm, which integrates hybrid uncertainty modeling with Monte Carlo optimization. Specifically, we implement a model named RobustDZP, which combines time-series prediction with residual learning for demand estimation, a multi-agent RL sampler for supply estimation, and an iterative perturb-and-vote algorithm for partition optimization. Extensive experiments on a large real-world dataset from JD Logistics demonstrate that our approach improves delivery timeliness by over 16.3% and significantly outperforms baselines under stress tests. Furthermore, we present a real-world case study to validate the practical effectiveness of our framework and highlight key lessons for designing robust and well-received partition schemes.