Since the early 2010s, the meal delivery business went through a veritable revolution due to online food delivery platforms. By allowing customers to quickly order from a wide range of restaurants and outsourcing currently available couriers using their vehicles (crowdsourcing), this typology of service dynamically bridges demand and supply. The main goal of online food delivery platforms consists of matching couriers to meal orders within short time intervals to provide an efficient, reliable, and sustainable service. A way to increase efficiency consists of consolidating orders into batches, such that the same courier can serve several orders in multiple pickup and drop-off routes. Since such an assignment-batching problem becomes computationally prohibitively costly in real-world scenarios characterized by a large number of customer orders as well as uncertain demand and supply, heuristic solution methods come into play. This study proposes an order batching and assignment algorithm that leverages a graph-based approach after decomposing the original problem into more tractable sub-problems employing clustering. The solution is improved by local search moves and re-optimization procedures and integrated with advanced policies to improve solutions over time. An ‘Insertion policy’ aimed at increasing batch size, and a ‘Swap policy’ aimed at identifying more efficient assignments, are implemented and compared to a ‘Myopic policy’ that does not involve any re-optimization over time. An agent-based simulation framework is developed to implement dynamic policies where couriers’ operations and movements are realistically reproduced. The performance of the developed solution approach is tested through experiments based on a real-world case study. Results show that the algorithm allows for high-quality solutions in several configurations characterized by different demand and supply patterns (e.g., density levels, couriers availability) and problem sizes.In particular, the two advanced policies investigated considerably improve the solutions.

自 2010 年代初以来，由于在线送餐平台的出现，送餐业务经历了一场名副其实的革命。通过允许客户从范围广泛的餐厅快速订购和使用他们的车辆外包当前可用的快递员（众包），这种服务类型动态地连接需求和供应。在线食品配送平台的主要目标包括在短时间内将快递员与餐单相匹配，以提供高效、可靠和可持续的服务。提高效率的一种方法是将订单合并成批次，这样同一个快递员就可以在多个取货和投递路线上为多个订单提供服务。由于在以大量客户订单以及不确定的需求和供应为特征的现实场景中，此类分配批处理问题的计算成本过高，因此启发式解决方法开始发挥作用。本研究提出了一种订单批处理和分配算法，该算法在使用聚类将原始问题分解为更易于处理的子问题后，利用基于图的方法。该解决方案通过本地搜索移动和重新优化程序得到改进，并与高级策略集成以随着时间的推移改进解决方案。旨在增加批量大小的“插入策略”和旨在识别更有效分配的“交换策略”被实施，并与不涉及随时间进行任何重新优化的“近视策略”进行比较。开发了一个基于代理的模拟框架来实施动态策略，其中快递员的操作和移动被逼真地再现。开发的解决方案方法的性能通过基于真实案例研究的实验进行测试。结果表明，该算法允许在以不同的需求和供应模式（例如，密度水平、快递员可用性）和问题规模为特征的多种配置中提供高质量的解决方案。特别是，研究的两个高级策略大大改进了解决方案。