编者按

在本系列文章中，我们梳理了运筹管理顶刊Management Science、Operations Research与Manufacturing & Service Operations Management上发表的与外卖配送有关的16篇论文汇总，旨在帮助读者快速洞察学界发展脉络与最新动态。

文章1

● 题目：Shared Mobility for Last-Mile Delivery: Design, Operational Prescriptions, and Environmental Impact

最后一公里配送的共享物流：系统设计、运营策略与环境影响

● 期刊：Manufacturing & Service Operations Management

● 原文链接：https://doi.org/10.1287/msom.2017.0683

● 作者：Wei Qi, Lefei Li, Sheng Liu, Zuo-Jun Max Shen  

● 在线发表时间：2018/06/07

● 摘要：

Two socioeconomic transformations, namely, the booms in the sharing economy and retail e-commerce, lead to the prospect where shared mobility of passenger cars prevails throughout urban areas for home delivery services. Logistics service providers as well as local governments are in need of evaluating the potentially substantial impacts of this mode shift, given their economic objectives and environmental concerns. This paper addresses this need by providing new logistics planning models and managerial insights. These models characterize open-loop car routes, car drivers’ wage-response behavior, interplay with the ride-share market, and optimal sizes of service zones within which passenger vehicles pick up goods and fulfill the last-mile delivery. Based on theoretical analysis and empirical estimates in a realistic setting, the findings suggest that crowdsourcing shared mobility is not as scalable as the conventional truck-only system in terms of the operating cost. However, a transition to this paradigm has the potential for creating economic benefits by reducing the truck fleet size and exploiting additional operational flexibilities (e.g., avoiding high-demand areas and peak hours, adjusting vehicle loading capacities, etc.). These insights are insignificantly affected by the dynamic adjustment of wages and prices of the ride-share market. If entering into this paradigm, greenhouse gas emissions may increase because of prolonged car trip distance; on the other hand, even exclusively minimizing operating costs incurs only slightly more emissions than exclusively minimizing emissions.

共享经济与零售电子商务的蓬勃发展这两大社会经济变革，催生了乘用车共享出行在城市范围内广泛应用于送货上门服务的前景。鉴于其经济目标与环境关切，物流服务提供商和地方政府都需要评估这种模式转变可能带来的重大影响。本文通过提供新的物流规划模型和管理洞见来满足这一需求。这些模型刻画了开放式的车辆路线、司机的工资响应行为、与网约车市场的相互作用，以及乘用车取货和完成最后一公里配送的最优服务区域规模。基于现实场景中的理论分析和实证估计，研究结果表明，就运营成本而言，众包共享出行模式的可扩展性不及传统的纯卡车配送系统。然而，向这种模式转型有望通过减少卡车车队规模和利用额外的运营灵活性（例如，避开高需求区域和高峰时段、调整车辆装载能力等）创造经济效益。这些洞见受网约车市场工资和价格动态调整的影响较小。如果采用这种模式，由于汽车行驶距离延长，温室气体排放量可能会增加；但另一方面，即使完全以最小化运营成本为目标，所产生的排放量也仅比完全以最小化排放量为目标时略多。

文章2

● 题目：On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors

准时达最后一公里配送：结合行程时间预测因子的订单分配

● 期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2020.3741

● 作者：Sheng Liu, Long He, Zuo-Jun Max Shen 

● 在线发表时间：2020/11/03

● 摘要：

We study how delivery data can be applied to improve the on-time performance of last-mile delivery services. Motivated by the delivery operations and data of a food delivery service provider, we discuss a framework that integrates travel-time predictors with order-assignment optimization. Such integration enables us to capture the driver’s routing behavior in practice as the driver’s decision-making process is often unobservable or intricate to model. Focusing on the order-assignment problem as an example, we discuss the classes of tractable predictors and prediction models that are highly compatible with the existing stochastic and robust optimization tools. We further provide reformulations of the integrated models, which can be efficiently solved with the proposed branch-and-price algorithm. Moreover, we propose two simple heuristics for the multiperiod order-assignment problem, and they are built upon single-period solutions. Using the delivery data, our numerical experiments on a real-world application not only demonstrate the superior performance of our proposed order-assignment models with travel-time predictors, but also highlight the importance of learning behavioral aspects from operational data. We find that a large sample size does not necessarily compensate for the misspecification of the driver’s routing behavior.

我们研究了如何运用配送数据提升最后一公里配送服务的准时性表现。受某餐饮配送服务商的配送运营及数据启发，我们提出了一个整合行程时间预测因子与订单分配优化的框架。这种整合能够捕捉骑手在实际中的路径选择行为，因为骑手的决策过程往往难以观测或建模复杂。以订单分配问题为研究对象，我们探讨了易于处理的预测因子类别和预测模型，这些模型与现有的随机优化和鲁棒优化工具具有高度兼容性。我们进一步对整合模型进行了重构，使其能够通过所提出的分支定价算法高效求解。此外，我们针对多时段订单分配问题提出了两种简单的启发式算法，它们均以单时段解决方案为基础。基于配送数据，我们在实际应用场景中进行了数值实验。结果不仅证明了所提出的结合行程时间预测因子的订单分配模型具有更优表现，还凸显了从运营数据中学习行为特征的重要性。我们发现，大样本量未必能弥补对骑手路径选择行为建模不当的缺陷。

文章3

● 题目：Crowdsourcing Last-Mile Deliveries

众包最后一公里配送

● 期刊：Manufacturing & Service Operations Management

● 原文链接：https://doi.org/10.1287/msom.2021.0973

● 作者：Soraya Fatehi, Michael R. Wagner 

● 在线发表时间：2021/05/24

● 摘要：

Problem definition: Because of the emergence and development of e-commerce, customers demand faster and cheaper delivery services. However, many retailers find it challenging to efficiently provide fast and on-time delivery services to their customers. Academic/practical relevance: Amazon and Walmart are among the retailers that are relying on independent crowd drivers to cope with on-demand delivery expectations. Methodology: We propose a novel robust crowdsourcing optimization model to study labor planning and pricing for crowdsourced last-mile delivery systems that are utilized for satisfying on-demand orders with guaranteed delivery time windows. We develop our model by combining crowdsourcing, robust queueing, and robust routing theories. We show the value of the robust optimization approach by analytically studying how to provide fast and guaranteed delivery services utilizing independent crowd drivers under uncertainties in customer demands, crowd availability, service times, and traffic patterns; we also allow for trend and seasonality in these uncertainties. Results: For a given delivery time window and an on-time delivery guarantee level, our model allows us to analytically derive the optimal delivery assignments to available independent crowd drivers and their optimal hourly wage. Our results show that crowdsourcing can help firms decrease their delivery costs significantly while keeping the promise of on-time delivery to their customers. Managerial implications: We provide extensive managerial insights and guidelines for how such a system should be implemented in practice.

问题定义：随着电子商务的兴起与发展，客户对配送服务的速度和成本提出了更高要求——既追求更快配送，又希望价格更低。然而，许多零售商发现，要高效地为客户提供快速且准时的配送服务面临诸多挑战。学术与实践相关性：亚马逊（Amazon）和沃尔玛（Walmart）等零售商正依赖独立的众包司机来满足按需配送的需求。研究方法：我们提出了一种新颖的稳健众包优化模型，用于研究众包最后一公里配送系统的人力规划与定价问题。该系统旨在满足带有 Guaranteed 配送时间窗的按需订单需求。我们的模型融合了众包、稳健排队理论和稳健路径规划理论。通过分析研究如何在客户需求、众包运力可用性、服务时间和交通模式存在不确定性（且这些不确定性存在趋势性和季节性）的情况下，利用独立众包司机提供快速且有保障的配送服务，我们论证了稳健优化方法的价值。研究结果：在给定配送时间窗和准时配送保障水平的前提下，我们的模型能够通过解析推导出针对可用独立众包司机的最优配送分配方案及其最优时薪。结果表明，众包模式可帮助企业大幅降低配送成本，同时兑现对客户的准时配送承诺。管理启示：我们提供了丰富的管理洞见和实践指南，指导此类系统在实际中的落地实施。

文章4

● 题目：Food Delivery Service and Restaurant: Friend or Foe?

外卖服务与餐厅：伙伴还是对手？

●期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2021.4245

● 作者：Manlu Chen, Ming Hu, Jianfu Wang 

● 在线发表时间：2022/02/08

● 摘要：

With food delivery services, customers can hire delivery workers to pick up food on their behalf. To investigate the long-term impact of food delivery services on the restaurant industry, we model a restaurant serving food to customers as a stylized single-server queue with two streams of customers. One stream consists of tech-savvy customers who have access to a food delivery service platform. The other stream consists of traditional customers who are not able to use a food delivery service and only walk in by themselves. We study a Stackelberg game, in which the restaurant first sets the food price; the food delivery platform then sets the delivery fee; and, last, rational customers decide whether to walk in, balk, or use a food delivery service if they have access to one. If the restaurant has a sufficiently large established base of traditional customers, we show that the food delivery platform does not necessarily increase demand but may just change the composition of customers, as the segment of tech-savvy customers grows. Hence, paying the platform for bringing in customers may hurt the restaurant’s profitability. We demonstrate that either a one-way revenue-sharing contract with a price ceiling or a two-way revenue-sharing contract can coordinate the system and create a win-win situation. Furthermore, under conditions of no coordination between the restaurant and the platform, we show, somewhat surprisingly, that more customers having access to a food delivery service may hurt the platform itself and the society, when the food delivery service is sufficiently convenient, and the delivery-worker pool is large enough. This is because the restaurant can become a delivery-only kitchen and raise its food price by focusing on food-delivery customers only, leaving little surplus for the platform. This implies that limiting the number of delivery workers can provide a simple yet effective means for the platform to improve its own profitability while benefiting social welfare.

有了外卖服务后，顾客可以雇佣配送员代取餐食。为探究外卖服务对餐饮业的长期影响，我们构建了一个简化模型：一家餐厅为顾客提供餐食，可抽象为单服务台排队系统，涉及两类顾客流。一类是精通技术的顾客，他们能使用外卖服务平台；另一类是传统顾客，他们不会使用外卖服务，只能到店消费。我们研究了一个斯塔克尔伯格博弈：餐厅首先设定食品价格；接着，外卖平台设定配送费；最后，理性顾客决定是到店消费、放弃消费，还是（若能使用外卖服务）选择外卖服务。研究发现，若餐厅拥有足够庞大且稳定的传统顾客基础，那么随着精通技术的顾客群体扩大，外卖平台未必会增加整体需求，而可能只是改变顾客构成。因此，餐厅为平台带来的顾客支付费用，可能会损害自身盈利能力。我们证明，无论是带有价格上限的单向收益分成合同，还是双向收益分成合同，都能实现系统协调并创造双赢局面。此外，在餐厅与平台缺乏协调的情况下，一个略显意外的发现是：当外卖服务足够便捷且配送员群体足够大时，更多顾客能使用外卖服务可能会损害平台自身及社会福利。这是因为餐厅可能转型为纯外卖厨房，专注服务外卖顾客并抬高食品价格，从而几乎没给平台留下剩余收益。这意味着，限制配送员数量可为平台提供一种简单而有效的方式——既能提高自身盈利能力，又能增进社会福利。

文章5

● 题目：On-Demand Meal Delivery Platforms: Operational Level Data and Research Opportunities

按需餐饮配送平台：运营层面数据与研究机遇

●期刊：Manufacturing & Service Operations Management

● 原文链接：https://doi.org/10.1287/msom.2022.1112

● 作者：Wenzheng Mao, Liu Ming, Ying Rong, Christopher S. Tang, Huan Zheng

● 在线发表时间：2022/05/09

● 摘要：

This paper describes the operations of most on-demand meal delivery platforms and discusses how empirical research can improve the operational performance of these platforms. To support and encourage more studies on the operations of on-demand delivery platforms, we provide a unique data set obtained from a meal delivery platform in China. This data set contains operational level data sampled from July 1 to August 31, 2015, in Hangzhou, China. The data set includes information about order placements, order deliveries, restaurants, drivers, weather and traffic conditions, and so on. We also review recent studies on meal delivery platforms and suggest research opportunities for improving delivery performance.

本文阐述了大多数按需餐饮配送平台的运营模式，并探讨了实证研究如何提升这些平台的运营绩效。为支持和鼓励更多关于按需配送平台运营的研究，我们提供了一个源自中国某餐饮配送平台的独特数据集。该数据集包含2015年7月1日至8月31日期间在中国杭州采集的运营层面数据，涵盖订单下单、订单配送、餐厅、骑手、天气及交通状况等信息。我们还回顾了近期关于餐饮配送平台的研究，并提出了旨在提升配送绩效的研究方向。

文章6

● 题目：Managing Relationships Between Restaurants and Food Delivery Platforms: Conflict, Contracts, and Coordination

餐厅与外卖平台的关系管理：冲突、契约与协调

●期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2022.4390

● 作者：Pnina Feldman, Andrew E. Frazelle, Robert Swinney 

● 在线发表时间：2022/03/28

● 摘要：

Restaurant delivery platforms collect customer orders via the Internet, transmit them to restaurants, and deliver the orders to customers. They provide value to restaurants by expanding their markets, but critics claim they destroy restaurant profits by taking a percentage of revenues and generating congestion that negatively impacts dine-in customers. We consider these tensions using a model of a restaurant as a congested service system. We find that the predominant industry contract, in which the platform takes a percentage cut of each delivery order (a “commission”), fails to coordinate the system because the platform does not internalize its effect on dine-in revenues; this leads to prices that are too low, reducing the restaurant’s margins and leaving money on the table for both firms. Two commonly proposed remedies to this problem (commission caps and allowing the restaurant to set a price floor on the platform) can increase restaurant revenue but do not solve the coordination issue. We thus propose an alternative, practical coordinating contract that is a variation of the current industry standard: for each delivery order, the platform pays the restaurant a percentage revenue share and a fixed fee. We show that this contract, appropriately designed, coordinates the system, protects restaurant margins by ensuring a lower bound on its revenue per delivery order, and allocates revenue between the restaurant and the platform with a high degree of flexibility.

餐厅配送平台通过互联网接收顾客订单，将订单传至餐厅，再将餐品配送给顾客。这些平台通过扩大市场为餐厅创造价值，但批评者认为，它们会抽取一定比例的收入，还会造成拥挤从而对到店顾客产生负面影响，进而损害餐厅利润。我们将餐厅视为一个存在拥挤问题的服务系统，通过这一模型来分析这些矛盾。研究发现，行业中主流的合同模式（即平台从每笔配送订单中抽取一定比例的佣金）无法实现系统协调，因为平台并未将其对到店收入的影响纳入考量。这导致定价过低，既压缩了餐厅的利润空间，也使双方都错失了潜在收益。针对这一问题，两种常见的提议解决方案（佣金上限制以及允许餐厅在平台上设定价格下限）虽能增加餐厅收入，但无法解决协调问题。因此，我们提出了一种替代性的、切实可行的协调合同，它是对当前行业标准合同的一种改进：对于每笔配送订单，平台向餐厅支付一定比例的收入分成以及一笔固定费用。我们的研究表明，经过合理设计的这种合同能够实现系统协调，通过确保餐厅每笔配送订单的收入存在下限来保护其利润空间，并且能高度灵活地在餐厅与平台之间进行收入分配。

文章7

● 题目：On-Demand Delivery from Stores: Dynamic Dispatching and Routing with Random Demand

门店按需配送：随机需求下的动态调度与路径规划

●期刊：Manufacturing & Service Operations Management

● 原文链接：https://doi.org/10.1287/msom.2022.1171

● 作者：Sheng Liu, Zhixing Luo 

● 在线发表时间：2022/12/23

● 摘要：

Problem definition: On-demand delivery has become increasingly popular around the world. Motivated by a large grocery chain store who offers fast on-demand delivery services, we model and solve a stochastic dynamic driver dispatching and routing problem for last-mile delivery systems where on-time performance is the main target. The system operator needs to dispatch a set of drivers and specify their delivery routes facing random demand that arrives over a fixed number of periods. The resulting stochastic dynamic program is challenging to solve because of the curse of dimensionality. Methodology/results: We propose a novel structured approximation framework to approximate the value function via a parametrized dispatching and routing policy. We analyze the structural properties of the approximation framework and establish its performance guarantee under large-demand scenarios. We then develop efficient exact algorithms for the approximation problem based on Benders decomposition and column generation, which deliver verifiably optimal solutions within minutes. Managerial implications: The evaluation results on a real-world data set show that our framework outperforms the current policy of the company by 36.53% on average in terms of delivery time. We also perform several policy experiments to understand the value of dynamic dispatching and routing with varying fleet sizes and dispatch frequencies.

问题定义：按需配送在全球范围内日益普及。受一家提供快速按需配送服务的大型连锁杂货店的启发，我们针对以准时送达为主要目标的最后一公里配送系统，构建并求解了一个随机动态的骑手调度与路径规划问题。系统运营商需要在固定时段内应对随机到达的需求，调度一组骑手并规划他们的配送路线。由于维度灾难的存在，由此产生的随机动态规划问题求解难度较大。研究方法与结果：我们提出了一种新颖的结构化近似框架，通过参数化的调度与路径策略来近似价值函数。我们分析了该近似框架的结构性质，并在高需求场景下确立了其性能保证。随后，我们基于邦德分解和列生成法开发了求解该近似问题的高效精确算法，能够在几分钟内得出可验证的最优解。管理启示：基于真实数据集的评估结果显示，我们的框架在配送时间方面平均比该公司当前采用的策略优36.53%。我们还进行了多项策略实验，以探究在车队规模和调度频率变化的情况下，动态调度与路径规划的价值。

文章8

● 题目：Courier Dispatch in On-Demand Delivery

按需配送中的骑手调度

●期刊：Management Science

原文链接：https://doi.org/10.1287/mnsc.2023.4858

● 作者：Mingliu Chen, Ming Hu  

● 在线发表时间：2023/07/21

● 摘要：

We study a courier dispatching problem in an on-demand delivery system in which customers are sensitive to delay. Specifically, we evaluate the effect of temporal pooling by comparing systems using the dedicated strategy, with which only one order is delivered per trip, versus the pooling strategy, with which a batch of consecutive orders is delivered on each trip. We capture the courier delivery system’s spatial dimension by assuming that, following a Poisson process, demand arises at a uniformly generated point within a service region. With the same objective of revenue maximization, we find that the dispatching strategy depends critically on customers’ patience level, the size of the service region, and whether the firm can endogenize the demand. We obtain concise but informative results with a single courier and assuming that customers’ underlying arrival rate is large enough, meaning a crowded market, such as rush hour delivery. In particular, when the firm has a growth target and needs to achieve an exogenously given demand rate, using the pooling strategy is optimal if the service area is large enough to fully exploit the pooling efficiency in delivery. Otherwise, using the dedicated strategy is optimal. In contrast, if the firm can endogenize the demand rate by varying the delivery fee, using the dedicated strategy is optimal for a large service area. The reason is that it is optimal for the firm to sustain a relatively low demand rate by charging a high fee for a large service radius: within this large area, the pooling strategy leads to a long wait because it takes a long time for multiple orders to accumulate. Moreover, with an exogenous demand rate to meet, customers’ patience level has no impact on the dispatch strategy. However, when the demand rate can be endogenized, the dedicated strategy is preferable if customers are impatient. Furthermore, we extend our model to account for social welfare maximization, a hybrid contingent delivery policy, a general arrival rate that does not have to be large, a nonuniform distribution of orders in the service region, and multiple couriers. We also conduct numerical analysis and simulations to complement our main results and find that most insights in our base model still hold in these extensions and numerical studies.

我们研究按需配送系统中的骑手调度问题，其中顾客对延迟较为敏感。具体而言，我们通过对比两种策略来评估时间聚合（temporal pooling）的效果：一是专属策略，即每次行程仅配送一个订单；二是聚合策略，即每次行程配送一批连续订单。为捕捉骑手配送系统的空间维度，我们假设需求按照泊松过程在服务区域内的随机均匀点产生。在以收入最大化为共同目标的前提下，我们发现调度策略主要取决于顾客的耐心程度、服务区域的规模，以及企业是否能内生决定需求。我们在单一骑手的场景下得出了简洁且信息丰富的结论，假设顾客的潜在到达率足够高（即市场处于拥挤状态，例如高峰时段的配送）。具体而言：当企业有增长目标且需要满足外生给定的需求率时，若服务区域的规模足以充分发挥配送聚合的效率，则聚合策略为最优；否则，专属策略更优；相反，若企业可通过调整配送费来内生决定需求率，那么对于大面积服务区域，专属策略为最优。原因在于：对于大服务半径，企业通过收取高额费用维持相对较低的需求率是最优选择——在这样的大面积区域内，聚合策略会因积累多个订单需要较长时间而导致等待时间过长。此外，在需满足外生需求率的情况下，顾客的耐心程度对调度策略无影响；但当需求率可内生决定时，若顾客耐心较低，则专属策略更受青睐。我们还扩展了模型，将社会福利最大化、混合应急配送策略、不必过高的一般到达率、服务区域内非均匀的订单分布以及多骑手场景纳入考量。通过数值分析与模拟对主要结果进行补充后发现，基础模型中的大部分结论在这些扩展场景和数值研究中仍然成立。

文章9

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

在线外卖平台的市场厚度：食品加工时间的影响

●期刊：Manufacturing & Service Operations Management

● 原文链接：https://doi.org/10.1287/msom.2021.0354

● 作者：Yanlu Zhao, Felix Papier, Chung-Piaw Teo 

● 在线发表时间：2024/02/27

● 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 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.

问题定义：在线外卖（OFD）平台在全球范围内迅速扩张，这在一定程度上得益于新冠疫情期间消费者行为的转变。这些平台让消费者能够通过手机便捷地从各类餐厅订餐。平台的核心功能之一是通过算法将配送员与外卖订单进行匹配，我们的研究聚焦于这一功能，旨在优化配送员 - 订单匹配流程。研究方法与结果：我们设计了实时匹配算法，该算法考虑到不确定的食品加工时间，有策略地“延迟”配送员与订单的分配。这种有意的延迟旨在打造一个“更厚”的市场，增加可用配送员和订单的数量。我们的算法运用机器学习技术预测食品加工时间，随后通过平衡配送员的闲置等待成本和订单延迟送达成本来决定配送员的调度。在单一订单的场景中，我们发现最优策略呈现出阈值结构。基于这一见解，我们针对多订单的一般情况，提出了一种新的带有行驶时间限制的k级增厚策略。该策略将配送员的分配延迟到最多有k个合适的匹配选项出现时再进行。我们通过一个简化模型对该策略进行评估，并确定了若干分析性质，包括总成本相对于市场厚度的拟凸性，这表明存在一个最优的中间市场厚度水平。利用美团的真实数据进行的数值实验显示，与现有策略相比，我们的策略可使总成本降低54%。管理启示：我们的研究表明，将食品加工时间纳入调度算法能显著提升配送员分配的效率。我们的策略使平台能够控制实时匹配决策中的两个关键市场参数：可及时取餐和送餐的配送员数量，以及这些配送员与餐厅的距离。基于这两个参数，我们的算法实现了配送员与订单的实时匹配，具有重要的管理意义。

文章10

● 题目：Don’t Fake It If You Can’t Make It: Driver Misconduct in Last-Mile Delivery

若无法完成，请勿造假：最后一公里配送中的骑手不当行为

●期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2023.01829

● 作者：Srishti Arora, Vivek Choudhary, Pavel Kireyev 

● 在线发表时间：2024/08/20

● 摘要：

In the last two decades, last-mile delivery (LMD) firms have seen immense growth fueled by the success of e-commerce, leading to faster and cheaper deliveries. Operating on thin margins, LMD firms strive for successful first-time deliveries to avoid the financial and reputational costs of reattempts. Delivery agents (DAs) are integral to LMD efficiency, influencing customer experience, delivery success, and productivity. However, most LMD performance enhancement research focuses on process, technology, and incentives, which presume workers will conform to procedures and monitoring tools will function flawlessly. Nevertheless, in practice, DAs deviate from expected behaviors, that is, indulge in misconduct, negatively affecting delivery efficiency, often resulting in returned parcels. One of the major forms of misconduct is entering fake remarks about deliveries, wherein DAs intentionally do not deliver the parcels and provide fake reasons for it. For instance, even without reaching a delivery address, a DA remarks “customer unavailable” and records a delivery failure. In this study, we collaborated with a leading Indian LMD firm and, using instrumental variable regression, found that such misconduct leads to a spillover productivity loss. This effect reduces the next day’s successful deliveries by 1.60% and first-time-right deliveries by 1.86%. We discuss misconduct’s correlation with factors such as task complexity and offer novel insights into how opportunistic circumstances can influence worker behavior.

在过去二十年中，受电子商务蓬勃发展的推动，最后一公里配送（LMD）企业实现了巨大增长，配送速度不断加快，成本也持续降低。由于利润率微薄，最后一公里配送企业致力于实现首次配送成功，以避免二次配送带来的财务损失和声誉风险。配送员（DA）是提升最后一公里配送效率的核心要素，他们的表现会影响客户体验、配送成功率和整体生产力。然而，大多数关于提升最后一公里配送绩效的研究都聚焦于流程、技术和激励机制，这些研究默认员工会遵守流程，且监控工具能完美运行。但在实际操作中，配送员常会偏离预期行为，即出现不当行为，这会对配送效率产生负面影响，还常常导致包裹被退回。其中一种主要的不当行为是伪造配送备注——配送员故意不配送包裹，却编造虚假理由。例如，有些配送员甚至未到达配送地址，就标注“客户不在”并记录配送失败。在本研究中，我们与印度一家领先的最后一公里配送企业合作，通过工具变量回归法发现，此类不当行为会导致溢出性的生产力损失：它会使次日的成功配送量减少1.60%，首次正确配送量减少1.86%。我们还探讨了不当行为与任务复杂度等因素的相关性，并就机会主义环境如何影响员工行为提出了新的见解。

文章11

● 题目：On-Demand Delivery Platforms and Restaurant Sales

按需配送平台与餐厅销售额

●期刊：Management Science

原文链接：https://doi.org/10.1287/mnsc.2021.01010

● 作者：Zhuoxin Li, Gang Wang

● 在线发表时间：2024/10/16

● 摘要：

Restaurants are increasingly relying on on-demand delivery platforms (e.g., DoorDash, Grubhub, and Uber Eats) to reach customers and fulfill takeout orders. Although on-demand delivery is a valuable option for consumers, whether restaurants benefit from or are being hurt by partnering with these platforms remains unclear. This paper investigates whether and to what extent the platform delivery channel substitutes restaurants’ own takeout/dine-in channels and the net impact on restaurant revenue. Empirical analyses show that restaurants overall benefit from on-demand delivery platforms—these platforms increase restaurants’ total takeout sales while creating positive spillovers to customer dine-in visits. However, the platform effects are substantially heterogeneous, depending on the type of restaurants (independent versus chain) and the type of customer channels (takeout versus dine-in). The overall positive effect on fast-food chains is four times as large as that on independent restaurants. For takeout, delivery platforms substitute independent restaurants’ but complement chain restaurants’ own takeout sales. For dine-in, delivery platforms increase both independent and chain restaurants’ dine-in visits by a similar magnitude. Therefore, the value of delivery platforms to independent restaurants mostly comes from the increase in dine-in visits, whereas the value to chain restaurants primarily comes from the gain in takeout sales. Further, the platform delivery channel facilitates price competition and reduces the opportunity for independent restaurants to differentiate with premium services and dine-in experience, which may explain why independent restaurants do not benefit as much from on-demand delivery platforms.

餐厅正越来越依赖按需配送平台（如DoorDash、Grubhub和Uber Eats）来触达顾客并完成外卖订单。尽管按需配送对消费者而言是一项有价值的选择，但餐厅与这些平台合作究竟是受益还是受损，目前尚无定论。本文研究了平台配送渠道是否会替代餐厅自身的外卖/堂食渠道，以及这种替代对餐厅收入的净影响程度。实证分析表明，总体而言，餐厅从按需配送平台中受益——这些平台在增加餐厅总外卖销售额的同时，还对顾客的堂食到访产生了积极的溢出效应。然而，平台的影响存在显著异质性，这取决于餐厅类型（独立餐厅与连锁餐厅）和顾客渠道类型（外卖与堂食）。快餐连锁餐厅从平台获得的总体积极影响是独立餐厅的四倍。在外卖方面，配送平台对独立餐厅的自有外卖销售产生替代效应，而对连锁餐厅的自有外卖销售则产生互补效应。在堂食方面，配送平台对独立餐厅和连锁餐厅的堂食到访量均有促进作用，且影响幅度相近。因此，配送平台对独立餐厅的价值主要来自堂食到访量的增加，而对连锁餐厅的价值则主要来自外卖销售额的增长。此外，平台配送渠道加剧了价格竞争，减少了独立餐厅通过高端服务和堂食体验实现差异化的机会，这或许可以解释为何独立餐厅从按需配送平台中获益相对较少。

文章12

● 题目：Robust Workforce Management with Crowdsourced Delivery

众包配送中的稳健型人力管理

●期刊：Operations Research

● 原文链接：https://doi.org/10.1287/opre.2023.0125

● 作者：Chun Cheng, Melvyn Sim, Yue Zhao 

● 在线发表时间：2024/11/04

● 摘要：

We investigate how crowdsourced delivery platforms with both contracted and ad hoc couriers can effectively manage their workforce to meet delivery demands amidst uncertainties. Our objective is to minimize the hiring costs of contracted couriers and the crowdsourcing costs of ad hoc couriers, while considering the uncertain availability and behavior of the latter. Because of the complication of calibrating these uncertainties through data-driven approaches, we instead introduce a basic reduced information model to estimate the upper bound of the crowdsourcing cost and a generalized reduced information model to obtain a tighter bound. Subsequently, we formulate a robust satisficing model associated with the generalized reduced information model and show that a binary search algorithm can tackle the model exactly by solving a modest number of convex optimization problems. Our numerical tests using Solomon’s data sets show that reduced information models provide decent approximations for practical delivery scenarios. Simulation tests further demonstrate that the robust satisficing model has better out-of-sample performance than the empirical optimization model that minimizes the total cost under historical scenarios.

我们研究了同时拥有签约骑手和临时骑手的众包配送平台，如何在不确定性环境中有效管理人力以满足配送需求。我们的目标是在考虑临时骑手可用性及行为不确定性的前提下，最小化签约骑手的雇佣成本和临时骑手的众包成本。由于通过数据驱动方法校准这些不确定性存在复杂性，我们转而引入一个基础简化信息模型来估计众包成本的上限，并通过一个广义简化信息模型获得更精确的边界。随后，我们构建了一个与广义简化信息模型相关的稳健满意模型，并证明二进制搜索算法可通过求解一定数量的凸优化问题来精确处理该模型。利用Solomon数据集进行的数值测试表明，简化信息模型能为实际配送场景提供合理的近似结果。模拟测试进一步显示，与在历史场景下最小化总成本的经验优化模型相比，稳健满意模型具有更优的样本外表现。

文章13

● 题目：How Should Time Estimates Be Structured to Increase Customer Satisfaction?

如何构建时间预估以提升客户满意度？

●期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2023.00137

● 作者：Beidi Hu, Celia Gaertig, Berkeley J. Dietvorst 

● 在线发表时间：2024/12/20

● 摘要：

Businesses across industries, such as food delivery apps and GPS navigation systems, routinely provide customers with time estimates in inherently uncertain contexts. How does the format of these time estimates affect customers’ satisfaction? In particular, should companies provide customers with a point estimate representing the best estimate, or should they communicate the inherent uncertainty in outcomes by providing a range estimate? In eight preregistered experiments (N = 5,323), participants observed time estimates provided by an app, and we manipulated whether the app presented the time estimates as a point estimate (e.g., “Your food will arrive in 45 minutes.”) or a range (e.g., “Your food will arrive in 40–50 minutes.”). After participants learned about the app’s prediction performance by sampling a set of past outcomes, we measured participants’ evaluation of the app. We find that participants judged the app more positively when it provided a range rather than a point estimate. These results held across different domains, different time durations, different underlying outcome distributions, and an incentive-compatible design. We also find that this preference is not simply due to people’s dislike of late outcomes, as participants also rated ranges more positively than conservative point estimates corresponding to the upper (i.e., later) bound of the range. These findings suggest that companies can increase customer satisfaction with realized time estimates by communicating the uncertainty inherent in these time estimates.

各行各业的企业（如外卖应用和GPS导航系统）通常会在存在固有不确定性的场景中向客户提供时间预估。这些时间预估的呈现形式会如何影响客户满意度？具体而言，企业应该向客户提供代表最佳估计的点估计，还是通过提供区间估计来传达结果中存在的固有不确定性？在八项预先注册的实验中（样本量N=5,323），参与者查看某应用提供的时间预估，我们则操纵该应用呈现时间预估的方式——是点估计（例如，“您的餐品将在45分钟后送达”）还是区间估计（例如，“您的餐品将在40-50分钟后送达”）。在参与者通过抽样一组过往结果了解该应用的预测表现后，我们测量了他们对该应用的评价。研究发现，当应用提供区间估计而非点估计时，参与者对其评价更为积极。这一结果在不同领域、不同时长、不同潜在结果分布以及激励兼容设计中均成立。我们还发现，这种偏好并非单纯源于人们对延迟结果的厌恶，因为与区间上限（即较晚时间）对应的保守点估计相比，参与者对区间估计的评价依然更高。这些发现表明，企业可以通过传达时间预估中固有的不确定性，提升客户对实际时间预估的满意度。

文章14

● 题目：Structural Estimation of Attrition in a Last-Mile Delivery Platform: The Role of Driver Heterogeneity, Compensation, and Experience

最后一公里配送平台中流失率的结构估计：骑手异质性、薪酬与经验的作用

●期刊：Manufacturing & Service Operations Management

● 原文链接：https://doi.org/10.1287/msom.2021.0367

● 作者：Lina Wang, Scott Webster, Elliot Rabinovich 

● 在线发表时间：2025/01/13

● 摘要：

Problem definition: We examine how to manage turnover among drivers delivering parcels for last-mile platforms. Although driver attrition in these platforms is both commonplace and costly, there is little understanding of the processes responsible for this phenomenon. Methodology/results: We collaborate with a platform to build a structural model to estimate the effects of key predictors of drivers’ decisions to leave or remain at the platform. For this estimation, we apply a dynamic discrete-choice framework in a two-step procedure that accounts for unobserved heterogeneity among drivers while circumventing the use of approximation or reduction methods commonly used to solve dynamic choice problems in the operations management domain. Drivers are compensated using a combination of regular payments that reward their productivity and subsidy payments that support them as they gain experience on the job. We find that regular pay has a greater effect on drivers’ retention. Furthermore, the marginal effects of both regular and subsidy pay diminish with drivers’ tenure at the platform, but the latter diminishes faster than the former. Additionally, we find significant heterogeneity among drivers in their unobserved nonpecuniary taste for the jobs at the platform and a significantly greater probability of retention among drivers with greater taste for these jobs. Managerial implications: Platforms can leverage our results to improve driver retention and design more profitable payment policies. We perform counterfactual analyses and develop a modeling framework to guide platforms toward this goal.

问题定义：我们研究如何管理最后一公里配送平台中快递员的流失问题。尽管这些平台上快递员的流失现象普遍存在且代价高昂，但人们对导致这一现象的过程知之甚少。研究方法与结果：我们与某平台合作，构建了一个结构模型，用于估计影响快递员决定离职或留任的关键因素的效应。在估计过程中，我们采用了动态离散选择框架，并通过两步法来处理快递员之间未被观测到的异质性，同时避免使用运营管理领域中常用于解决动态选择问题的近似或简化方法。快递员的薪酬由两部分构成：一部分是奖励其工作效率的常规报酬，另一部分是在他们积累工作经验过程中为其提供支持的补贴。我们发现，常规报酬对快递员的留任意愿影响更大。此外，常规报酬和补贴的边际效应都会随着快递员在平台的任职时间增长而减弱，但补贴边际效应的减弱速度快于常规报酬。另外，我们还发现，快递员对平台工作的未被观测到的非金钱偏好存在显著异质性，且对这份工作偏好程度越高的快递员，其留任概率显著越大。管理启示：平台可以利用我们的研究结果来提高快递员的留存率，并设计出更有利可图的薪酬政策。我们进行了反事实分析，并开发了一个建模框架，以指导平台实现这一目标。

文章15

● 题目：Integrated Fleet and Demand Control for On-Demand Meal Delivery Platforms

按需餐饮配送平台的车队与需求整合管控

●期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2022.02039

● 作者：Florentin D. Hildebrandt, Žiga Lesjak, Arne Strauss, Marlin W. Ulmer 

● 在线发表时间：2025/05/20

● 摘要：

We show how integrated fleet and demand control can be effectively used to benefit all stakeholders in on-demand restaurant meal delivery. Fleet control—that is, the assignment of orders to couriers—is the main control mechanism to steer delivery operations. Another, mostly overlooked, control mechanism is demand control via display optimization—that is, the ordering of restaurants’ display positions on the meal delivery platform. Based on historical customer interactions with a meal delivery platform, we reveal that display positions have a major effect on customers’ restaurant choices. We then leverage this effect by proposing an integrated, scalable reinforcement learning approach that simultaneously optimizes fleet and demand control. We employ our solution method on simulations of large-scale on-demand meal delivery operations with endogenous customer behavior to derive managerial insights on the value of integrated fleet and demand control. Our results demonstrate that integrated fleet and demand control reduces delays experienced by customers, allows for more services per driver, decreases total travel time per driver, guarantees fresher meals, and provides equal opportunities for all participating restaurants. Our results further highlight that selling display positions may cause operational inflexibility and, therefore, may cause significant delays in the fulfillment process. Finally, we show that careful display optimization not only improves service quality, but also platform revenue.

我们展示了通过整合车队与需求管控，如何有效实现按需餐饮配送中所有利益相关方的共赢。车队管控——即订单与骑手的分配——是主导配送运营的核心调控机制。而另一种常被忽视的调控机制是通过展示优化实现的需求管控，具体指外卖平台上餐厅展示位置的排序。基于顾客与某外卖平台的历史交互数据，我们发现展示位置对顾客的餐厅选择存在显著影响。据此，我们提出一种整合化、可扩展的强化学习方法，通过同时优化车队管控与需求管控来充分利用这一影响。我们将该解决方法应用于具有内生顾客行为的大规模按需餐饮配送模拟场景，以提炼关于车队与需求整合管控价值的管理洞见。研究结果表明，车队与需求的整合管控能够减少顾客遭遇的配送延迟，提高骑手的单量服务能力，缩短骑手的总行驶时间，保障餐品更新鲜，并为所有参与餐厅提供平等机会。结果进一步显示，出售展示位置可能导致运营灵活性下降，进而造成订单履约过程中的显著延迟。最后，我们发现精心设计的展示优化不仅能提升服务质量，还能增加平台收入。

文章16

● 题目：Food Ordering and Delivery: How Platforms and Restaurants Should Split the Pie

在线餐饮外卖：平台与餐厅应如何分食 “蛋糕”

●期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2023.00435

● 作者：Jaelynn Oh, Chloe Kim Glaeser, Xuanming Su 

● 在线发表时间：2025/07/02

● 摘要：

Food ordering and delivery platforms generate online demand for restaurants and deliver food to customers. In return, restaurants pay platforms a commission, typically a percentage of the order amount. Platforms offer partner restaurants the choice of a range of commission rates, rewarding higher commission payments with featured display slots and discounted delivery fees, both of which stimulate demand. Unfortunately, the current environment is grim: Platforms scurry to cover delivery costs, whereas restaurants gripe about excessive commissions. To understand current practice, we develop a game-theoretic model with a platform and multiple restaurants. Our modeling results highlight two existing problems. (1) Platforms, on their apps/websites, feature restaurants that are located too far away. Because these restaurants do not internalize the platform’s delivery costs, they are willing to choose aggressively high commissions to earn featured display. (2) Platforms charge delivery fees that are too high and set delivery boundaries that are too narrow. This is because they bear the entire burden of delivery but earn only a fraction of food revenues. To solve these problems, we propose a simple fix to existing commission contracts: beyond sharing food revenue (currently done but at high commission rates), platforms and restaurants can also split delivery costs and fees (currently not done). We show that our method attains first-best, that is, maximizes the total pie shared by platforms and restaurants. Using data on a representative city, we numerically show that, on average, our coordinating contract lowers commission rates by 33.3%, lowers delivery fees by 40.4%, increases restaurant profit by 25.0%, increases platform profit in 30.9% of the markets, and increases total profit by 13.3%. We discuss the characteristics of markets that enable our coordinating contract to yield a winning outcome for all parties.

在线餐饮外卖平台为餐厅创造线上需求，并为顾客提供送餐服务。作为回报，餐厅需向平台支付佣金，通常为订单金额的一定比例。平台为合作餐厅提供多种佣金率选择，佣金支付越高，餐厅可获得的推荐展示位越优质、配送费折扣越大，而这两者都能刺激需求。然而，当前行业环境不容乐观：平台急于覆盖配送成本，而餐厅则抱怨佣金过高。为理解现有运营模式，我们构建了一个包含平台与多家餐厅的博弈论模型。模型结果揭示了两个现存问题：（1）平台在其应用程序/网站上推荐的餐厅地理位置过远。由于这些餐厅无需承担平台的配送成本，它们愿意选择极高的佣金率以获取推荐展示位。（2）平台收取的配送费过高，且设置的配送范围过窄。这是因为平台承担了全部配送成本，却只能从食品收入中获得一部分收益。为解决这些问题，我们对现有佣金合同提出一项简单修正：除了分摊食品收入（目前已实行，但佣金率较高），平台与餐厅还应共同分担配送成本与配送费（目前尚未实行）。我们证明，这种方法能实现最优结果，即最大化平台与餐厅共享的“总收益蛋糕”。基于某代表性城市的数据，我们通过数值模拟发现：平均而言，这种协调合同能使佣金率降低33.3%，配送费降低40.4%，餐厅利润增加25.0%，在30.9%的市场中提升平台利润，且总利润增长13.3%。我们还探讨了那些能使该协调合同为所有参与方带来共赢结果的市场特征。