Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times

• 成果类型: Article
• 署名作者: Zhao, Yanlu; Papier, Felix; Teo, Chung-Piaw
• 署名单位: Durham University; ESSEC Business School; National University of Singapore; National University of Singapore
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2021.0354
• 发表日期: 2024
• 关键词: online food delivery; market thickness; dynamic matching; bipartite min-cost matching

摘要

Problem definition: Online food delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts in consumer behavior during the COVID-19 pandemic. These platforms enable customers to order food conveniently from a diverse array of restaurants through their mobile phones. A core functionality of these platforms is the algorithmic matching of drivers to food orders, which is the focus of our study as we aim to optimize this driver -order matching process. Methodology/results: We formulate realtime matching algorithms that take into account uncertain food processing times to strategically delay the assignment of drivers to orders. This intentional delay is designed to create a thicker marketplace, increasing the availability of both drivers and orders. Our algorithms use machine learning techniques to predict food processing times, and the dispatching of drivers is subsequently determined by balancing costs for idle driver waiting and for late deliveries. In scenarios with a single order in isolation, we show that the optimal policy adopts a threshold structure. Building on this insight, we propose a new k -level thickening policy with driving time limits for the general case of multiple orders. This policy postpones the assignment of drivers until a maximum of k suitable matching options are available. We evaluate our policy using a simplified model and identify several analytical properties, including the quasi -convexity of total costs in relation to market thickness, indicating the optimality of an intermediate level of market thickness. Numerical experiments with real data from Meituan show that our policy can yield a 54% reduction in total costs compared with existing policies. Managerial implications: Our study reveals that incorporating food processing times into the dispatch algorithm remarkably improves the efficacy of driver assignment. Our policy enables the platform to control two vital market parameters of realtime matching decisions: the number of drivers available to pick up and deliver an order promptly, and their proximity to the restaurant. Based on these two parameters, our algorithm matches drivers with orders in real time, offering significant managerial implications.