Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows

TL;DR

This paper introduces a novel machine learning pipeline with a combinatorial optimization layer to efficiently solve dynamic vehicle routing problems with time windows, outperforming existing methods in a competitive setting.

Contribution

The paper presents a new ML pipeline that integrates combinatorial optimization to effectively address complex dynamic vehicle routing problems, demonstrating superior performance.

Findings

Ranked first in the NeurIPS 2022 competition

Outperformed existing approaches in dynamic vehicle routing

Showed robustness of the approach on unseen instances

Abstract

With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same day deliveries. Existing multi-stage stochastic optimization approaches that allow to solve the underlying dynamic vehicle routing problem are either computationally too expensive for an application in online settings, or -- in the case of reinforcement learning -- struggle to perform well on high-dimensional combinatorial problems. To mitigate these drawbacks, we propose a novel machine learning pipeline that incorporates a combinatorial optimization layer. We apply this general pipeline to a dynamic vehicle routing problem with dispatching waves, which was recently promoted in the EURO Meets NeurIPS Vehicle Routing Competition at NeurIPS 2022. Our methodology ranked first in this competition, outperforming all other approaches in solving the proposed dynamic vehicle routing problem. With this work, we provide a comprehensive numerical study that further highlights the efficacy and benefits of the proposed pipeline beyond the results achieved in the competition, e.g., by showcasing the robustness of the encoded policy against unseen instances and scenarios.