PAPN: Proximity Attention Encoder and Pointer Network Decoder for Parcel Pickup Route Prediction

TL;DR

This paper introduces PAPN, a novel route prediction model combining proximity attention and pointer networks, which outperforms existing methods on real-world last-mile parcel pickup data.

Contribution

It proposes a new proximity attention mechanism integrated with a transformer and pointer network for improved route prediction in logistics.

Findings

Outperforms all state-of-the-art supervised methods on LaDE dataset.

Achieves competitive results with reinforcement learning approaches.

Effectively models local and global context for route prediction.

Abstract

Optimization of the last-mile delivery and first-mile pickup of parcels is integral to the logistics optimization pipeline as it entails both cost and resource efficiency and 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. The novel Proximity Attention (PA) mechanism is coupled to a Pointer Network (PN) decoder to leverage the underlying connections between the different visitable pickup positions at each timestep of the parcel pickup process. This local attention is coupled with global context computing via a multi-head attention transformer encoder. Both attentions are then mixed for complete and comprehensive modeling of the problems. PA is also used in the decoding process to skew predictions towards the locations with the highest visit likeliness, thus using inter-connectivity of nodes for next-location prediction. This method is trained, validated and tested on a large industry-level dataset of real-world, last-mile delivery and first-mile pickup named LaDE (2024). This approach outperforms all state-of-the-art supervised methods in terms of most metrics used for benchmarking on this dataset while still being competitive with the best-performing reinforcement learning framework named DRL4Route (2023).