The online‐to‐offline (O2O) business model has facilitated millions of daily transactions on popular online food ordering platforms. Online food delivery route planning presents a complex multidepot vehicle routing problem (VRP) with capacity limits, pickup–delivery, and time‐window constraints. However, the vast volume of transactions and computational complexities of delivery routes pose significant challenges. This paper proposes a novel feature fusion attention‐based deep reinforcement learning model to address such constrained routing problems. The innovative encoding and masking scheme with a self‐attention‐guided order relocation operator efficiently handles multidepot and multiconstraint scenarios. Additionally, incorporating self‐attention with a graph neural network (GNN) framework extends existing research from static unit square environments to dynamic real road networks. Computational experiments demonstrate that our proposed route solver outperforms state‐of‐the‐art heuristics and reinforcement learning methods regarding solution quality and computation time across unit square environments and real road networks. Exploratory analysis using real‐world delivery data a illustrates the applicability of our approach to practical online ordering platforms.
Zou et al. (Thu,) studied this question.