Optimization of Rider Scheduling for a Food Delivery Service in O2O Business
Optimization of Rider Scheduling for a Food Delivery Service in O2O Business
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DOI：
10.1155/2021/5515909
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发表时间：
2021-05
影响因子：
2.3
通讯作者：
Guiqin Xue;Z. Wang;Guangwei Wang
中科院分区：
文献类型：
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作者：
Guiqin Xue;Z. Wang;Guangwei Wang
美团和优步外卖等服务彻底改变了顾客寻找和点餐的方式。许多独立餐厅都在争夺顾客通过网上订餐平台下的订单。近年来，通过智能手机应用点外卖变得越来越普遍。外卖食品服务提供商必须应对一些运营方面的挑战，例如，客户需求随时间和地区而波动。从这个意义上说，服务提供商有时会忽略这样一个事实，即在某些地区，一些骑手可能会在几个时间段内闲置，而在其他情况下，可能会出现骑手短缺。为了解决这一问题，我们引入了一个两阶段模型来优化速食配送骑手的调度。服务提供商平台期望在预期到达时间内安排最少数量的乘客交付，以满足不同地区和时间段的客户需求。本文介绍了一种采用混合整数规划（MIP）方法的两阶段模型，描述了该方案的相关方面，并提出了一种优化算法来调度乘客。我们还根据粒度将交付服务区域和时间划分为更小的部分。通过数值实验对大邻域搜索算法进行了验证，结果表明该算法能够满足设计目标。进一步研究表明，优化骑手资源有利于降低配送总成本。外卖服务平台决定骑手的班次安排（开始时间和持续时间），以最小的成本实现服务水平目标。还讨论了其他敏感性分析，例如与订单和骑手对交付地区的熟悉程度相关的订单时间窗口的紧密性
Services such as Meituan and Uber Eats have revolutionized the way the customer can find and order from restaurants. Numerous independent restaurants are competing for orders placed by customers via online food ordering platforms. Ordering takeout food on smartphone apps has become more and more prevalent in recent years. There are some operational challenges that takeout food service providers have to deal with, e.g., customer demand fluctuates over time and region. In this sense, the service providers sometimes ignore the fact that some riders may be idle in several periods in regions, while, in contrast, there may be a shortage of riders in other situations. In order to address this problem, we introduce a two-stage model to optimize scheduling of riders for instant food deliveries. A service provider platform expectantly schedules the least quantity of riders to deliver within expected arrival time to satisfy customer demand in different regions and time periods. We introduce a two-stage model that adopts the method of mixed-integer programming (MIP), characterize relevant aspects of the scenario, and propose an optimization algorithm for scheduling riders. We also divide the delivery service region and time into smaller parts in terms of granularity. The large neighborhood search algorithm is validated through numerical experiments and is shown to meet the design objectives. Furthermore, this study reveals that the optimization of rider resource is beneficial to reduce overall cost of the delivery. Takeout food service platforms decide scheduling shifts (start time and duration) of the riders to achieve a service level target at minimum cost. Additional sensitivity analyses, such as the tightness of the order time windows associated with the orders and riders’ familiarity with delivery regions, are also discussed