Optimizing driver menus under stochastic selection behavior for ridesharing and crowdsourced delivery
Optimizing driver menus under stochastic selection behavior for ridesharing and crowdsourced delivery
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DOI：
10.1016/j.tre.2021.102419
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发表时间：
2021-09
影响因子：
10.6
通讯作者：
Hannah Horner;Jennifer A. Pazour;J. Mitchell
中科院分区：
文献类型：
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作者：
Hannah Horner;Jennifer A. Pazour;J. Mitchell
P2P物流平台协调独立司机，以满足最后一英里的送货和拼车请求。为了平衡需求侧性能和司机自主性，一种新的随机方法为司机提供了一个小但个性化的请求菜单供选择。这创造了一个斯塔克尔伯格游戏，在游戏中，平台通过决定发送给司机的请求菜单来领先，司机随后从收到的菜单中选择他们愿意完成的请求(S)。由于平台对司机的请求偏好了解有限，因此确定最佳菜单、菜单大小和菜单中的请求重叠是复杂的。利用驾驶员发出参与意愿时的问题结构，我们将问题转化为等价的单层混合整数线性规划(MILP)，并应用样本平均近似(SAA)方法。计算测试推荐用于输入SAA场景的训练样本大小和用于完成性能分析的测试样本大小。我们的随机优化方法比现有的方法以及确定性优化方法性能更好。一个简化的公式忽略了接受请求但不匹配的“不愉快的司机”，用一小部分的运行时间产生了类似的目标值。芝加哥地区交通网络的拼车案例研究为希望通过菜单创建提供司机自动驾驶的平台提供了见解。只要司机得到很好的补偿，建议的方法就能实现高需求性能(例如，即使允许司机拒绝请求，当80%的车费流向司机时，平均超过90%的请求得到满足；当只有40%的车费流向司机时，这一比例降至60%以下)。因此，平台和司机都不能从低司机补偿中受益，这是因为司机参与度低，因此要求满足率也低。最后，对于测试的案例，建议最大菜单大小为5，因为它可以产生高质量的平台解决方案，而不需要太多的驱动程序选择时间。
Peer-to-peer logistics platforms coordinate independent drivers to fulfill requests for last mile delivery and ridesharing. To balance demand-side performance with driver autonomy, a new stochastic methodology provides drivers with a small but personalized menu of requests to choose from. This creates a Stackelberg game, in which the platform leads by deciding what menu of requests to send to drivers, and the drivers follow by selecting which request(s) they are willing to fulfill from their received menus. Determining optimal menus, menu size, and request overlaps in menus is complex as the platform has limited knowledge of drivers’ request preferences. Exploiting the problem structure when drivers signal willingness to participate, we reformulate our problem as an equivalent single-level Mixed Integer Linear Program (MILP) and apply the Sample Average Approximation (SAA) method. Computational tests recommend a training sample size for inputted SAA scenarios and a test sample size for completing performance analysis. Our stochastic optimization approach performs better than current approaches, as well as deterministic optimization alternatives. A simplified formulation ignoring ‘unhappy drivers’ who accept requests but are not matched is shown to produce similar objective values with a fraction of the runtime. A ridesharing case study of the Chicago Regional transportation network provides insights for a platform wanting to provide driver autonomy via menu creation. The proposed methods achieved high demand performance as long as the drivers are well compensated (e.g., even when drivers are allowed to reject requests, on average over 90% of requests are fulfilled when 80% of the fare goes to drivers; this drops to below 60% when only 40% of the fare goes to drivers). Thus, neither the platform nor the drivers benefit from low driver compensation due to its resulting low driver participation and thus low request fulfillment. Finally, for the cases tested, a maximum menu size of 5 is recommended as it produces good quality platform solutions without requiring much driver selection time.