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Data · dataset · 2026

Online Meal Delivery Simulator with Courier Behavior and Graph Neural Networks

Listed in IISH Dataverse

We consider the Meal Delivery Routing Problem (MDRP) faced by online food delivery platforms.

Description

In the MDRP, customers’ food orders arrive dynamically throughout the day and must be assigned to autonomous couriers. Each order is prepared at a merchant and must be delivered to a customer by a courier.

We develop a polynomial-time heuristic using a Graph Neural Network (GNN) that estimates future assignment costs in a Cost Function Approximation (CFA) framework. To test the performance of our CFA/GNN heuristic, we create an open-source discrete-event simulator for the MDRP, that simulates the delivery of thousands of orders within seconds. We validate our discrete-event simulator on real-world data from Meituan.

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We train our GNN using policy gradient method. The CFA/GNN heuristic achieves an average click-to-door time reduction of 9.8% to 14.6% compared to a myopic benchmark. Our heuristic handles courier behavior and uncertainty, using bundling and postponement mechanisms, enabling real-time decision-making for online food delivery platforms.

Dataset description: The repository contains C++ files for the compilation of the MeituanSimulator.exe, a discrete event simulator. This simulator can process real-world data from Meituan (see external/Meituan/) and it's output can be examined using a variety of supplied R scripts (see scripts/). We also supply pretrained Graph Neural Network weights (see results/weights/) to enable further research.

Detailed descriptions, installation instructions and usage examples can be found in the accompanying README file.

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