Optimization of customer service area and driver dispatch area for on-demand food delivery

• Main Authors: YANG, Jingfeng, LAU, Hoong Chuin, WANG, Hai
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: On-demand food delivery; Customer service area; Driver dispatch area; Data-driven optimization; Artificial Intelligence and Robotics; Numerical Analysis and Computation
• Online Access: https://ink.library.smu.edu.sg/sis_research/9923
• Institution: Singapore Management University

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.