{"title":"在线食品配送平台的市场厚度:食品加工时间的影响","authors":"Yanlu Zhao, Felix Papier, Chung-Piaw Teo","doi":"10.1287/msom.2021.0354","DOIUrl":null,"url":null,"abstract":"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 real-time 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 real-time 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.Funding: This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call [Award ref: MOE-2019-T3-1-010].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0354 .","PeriodicalId":501267,"journal":{"name":"Manufacturing & Service Operations Management","volume":"213 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times\",\"authors\":\"Yanlu Zhao, Felix Papier, Chung-Piaw Teo\",\"doi\":\"10.1287/msom.2021.0354\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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 real-time 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 real-time matching decisions: the number of drivers available to pick up and deliver an order promptly, and their proximity to the restaurant. 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引用次数: 0
摘要
问题定义:在线食品配送(OFD)平台在全球范围内迅速扩张,部分原因是 COVID-19 大流行期间消费者行为的转变。这些平台使顾客能够通过手机方便地从各种餐馆订餐。这些平台的一个核心功能是通过算法将司机与订餐进行匹配,这也是我们研究的重点,因为我们的目标是优化司机与订餐的匹配过程。方法/结果:我们制定了实时匹配算法,将不确定的食品加工时间考虑在内,战略性地 "延迟 "司机与订单的分配。这种有意的延迟旨在创造一个 "更厚 "的市场,增加司机和订单的可用性。我们的算法使用机器学习技术来预测食品加工时间,随后通过平衡司机空闲等待和延迟交货的成本来决定司机的调度。在单个订单孤立存在的情况下,我们发现最优策略采用了阈值结构。在此基础上,我们针对多订单的一般情况,提出了一种具有驾驶时间限制的 k 级加厚新策略。这种策略会推迟司机的分配,直到有最多 k 个合适的匹配选项。我们使用简化模型对政策进行了评估,并确定了一些分析特性,包括总成本与市场厚度的准凸性,这表明市场厚度的中间水平是最优的。利用美团网真实数据进行的数值实验表明,与现有政策相比,我们的政策能使总成本降低 54%。管理意义:我们的研究表明,将食品加工时间纳入调度算法可显著提高司机分配的效率。我们的策略使平台能够控制实时匹配决策的两个重要市场参数:可及时取送订单的司机数量及其与餐厅的距离。基于这两个参数,我们的算法可以实时匹配司机和订单,从而提供重要的管理意义:本研究得到了新加坡教育部 2019 年学术研究基金第 3 层资助[获奖编号:MOE-2019-T3-1-010]:在线附录见 https://doi.org/10.1287/msom.2021.0354 。
Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times
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 real-time 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 real-time 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.Funding: This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call [Award ref: MOE-2019-T3-1-010].Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0354 .