Optimization of the last-mile delivery and first-mile pickup of parcels is an integral part of the broader logistics optimization pipeline as it entails both cost and resource efficiency as well as a heightened service quality. Such optimization requires accurate route and time prediction systems to adapt to different scenarios in advance. This work tackles the first building block, namely route prediction. This is done by introducing a novel Proximity Attention mechanism in an encoder-decoder architecture utilizing a Pointer Network in the decoding process (Proximity Attention Encoder and Pointer Network decoder: PAPN) to leverage the underlying connections between the different visitable pickup positions at each timestep. To this local attention process is coupled global context computing via a multi-head attention transformer encoder. The obtained global context is then mixed to an aggregated version of the local embedding thus achieving a mix of global and local attention for complete modeling of the problems. Proximity attention is also used in the decoding process to skew predictions towards the locations with the highest attention scores and thus using inter-connectivity of locations as a base for next-location prediction. This method is trained, validated and tested on a large industry-level dataset of real-world, large-scale last-mile delivery and first-mile pickup named LaDE[1]. This approach shows noticeable promise, outperforming all state-of-the-art supervised systems in terms of most metrics used for benchmarking methods on this dataset while still being competitive with the best-performing reinforcement learning method named DRL4Route[2].
View on arXiv@article{denis2025_2505.03776,
title={ PAPN: Proximity Attention Encoder and Pointer Network Decoder for Parcel Pickup Route Prediction },
author={ Hansi Denis and Siegfried Mercelis and Ngoc-Quang Luong },
journal={arXiv preprint arXiv:2505.03776},
year={ 2025 }
}