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arXiv · 2202.10937

An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems

Jinlei Zhang·Ergang Shan·Lixia Wu·Lixing Yang·Ziyou Gao·Haoyuan Hu
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Abstract

Express systems play important roles in modern major cities. Couriers serving for the express system pick up packages in certain areas of interest (AOI) during a specific time. However, future pick-up requests vary significantly with time. While the assignment results are generally static without changing with time. Using the historical pick-up request number to conduct AOI assignment (or pick-up request assignment) for couriers is thus unreasonable. Moreover, even we can first predict future pick-up requests and then use the prediction results to conduct the assignments, this kind of two-stage method is also impractical and trivial, and exists some drawbacks, such as the best prediction results might not ensure the best clustering results. To solve these problems, we put forward an intelligent end-to-end predict-then-optimize clustering method to simultaneously predict the future pick-up requests of AOIs and assign AOIs to couriers by clustering. At first, we propose a deep learning-based prediction model to predict order numbers on AOIs. Then a differential constrained K-means clustering method is introduced to cluster AOIs based on the prediction results. We finally propose a one-stage end-to-end predict-then-optimize clustering method to assign AOIs to couriers reasonably, dynamically, and intelligently. Results show that this kind of one-stage predict-then-optimize method is beneficial to improve the performance of optimization results, namely the clustering results. This study can provide critical experiences for predict-and-optimize related tasks and intelligent assignment problems in express systems.

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Jinlei Zhang, Ergang Shan, Lixia Wu, Lixing Yang, Ziyou Gao, Haoyuan Hu. 2022-02-18. An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems. https://arxiv.org/abs/2202.10937

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