Publication Type

Journal Article

Version

acceptedVersion

Publication Date

8-2024

Abstract

With the rapid development and popularization of mobile and wireless communication technologies, on-demand food delivery (OFD) platforms have been able to connect restaurants, customers, and drivers in real time, drastically changing dining and food delivery services. Motivated by the critical need for supply and demand management in the on-demand food delivery market, we focus on the optimization of customer service area and driver dispatch area for on-demand food delivery services. Specifically, for each restaurant, the platform needs to decide the (1) customer service area (CSA), i.e., the surrounding area within which customers can see the restaurant’s information and order food from it; and (2) driver dispatch area (DDA), i.e., the surrounding area within which drivers can see the restaurant’s information and deliver orders from it. Hence, our focus is on the area sizing optimization problem that enables the platform to dynamically balance supply and demand by adjusting the radii of its customer service and driver dispatch areas. Leveraging a real dataset from a food delivery platform, we propose a data-driven optimization framework that combines discrete choice models for demand estimation, machine learning methods for order delivery time prediction, and mathematical programming for the optimization of CSA and DDA areas. The objective is to maximize the total number of orders served with a service level requirement on order delivery time. We integrate the model tree prediction model for delivery time prediction into our optimization model, resulting in a Mixed Integer Quadratically Constrained Program (MIQCP), that can be solved efficiently. Extensive experiments using real-world data demonstrate that the proposed framework outperforms several benchmarks in practice.

Keywords

On-demand food delivery, Customer service area, Driver dispatch area, Data-driven optimization

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Transportation Research Part C: Emerging Technologies

Volume

165

First Page

1

Last Page

30

ISSN

0968-090X

Identifier

10.1016/j.trc.2024.104653

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.trc.2024.104653

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