将司机与乘客匹配：一种两阶段鲁棒方法
Matching Drivers to Riders: A Two-stage Robust Approach
AI总结：
针对网约车即时匹配问题，提出两阶段鲁棒优化框架，以最小化当前匹配成本与未来最坏情景成本，证明NP难并给出常数近似算法，深圳出租车数据验证显著优于短视方法。
摘要：
高效地将需求（乘客）与供给（司机）进行匹配是网约车平台的一个基本问题，平台需要在请求到达时（几乎）立即将乘客与司机匹配，而对未来的乘车请求仅有部分了解。一种短视的方法，即为当前请求计算最优匹配而忽略未来的不确定性，可能会高度次优。在本文中，我们针对该匹配问题考虑了一个两阶段鲁棒优化框架，其中未来的需求不确定性通过一组需求情景（显式或隐式指定）来建模。目标是（在第一阶段）将当前请求匹配给司机，使得第一阶段匹配的成本与第二阶段匹配在所有情景下的最坏情况成本之和最小化。我们证明了在各种成本函数下，两阶段鲁棒匹配问题是NP难的，并针对我们两阶段问题的不同设置提出了常数近似算法。此外，我们在来自深圳市的真实出租车数据上测试了我们的算法，并表明它们显著优于短视解，并减少了第二阶段乘客的最大等待时间。
英文摘要：
Matching demand (riders) to supply (drivers) efficiently is a fundamental problem for ride-sharing platforms who need to match the riders (almost) as soon as the request arrives with only partial knowledge about future ride requests. A myopic approach that computes an optimal matching for current requests ignoring future uncertainty can be highly sub-optimal. In this paper, we consider a two-stage robust optimization framework for this matching problem where future demand uncertainty is modeled using a set of demand scenarios (specified explicitly or implicitly). The goal is to match the current request to drivers (in the first stage) so that the cost of first-stage matching and the worst-case cost over all scenarios for the second-stage matching is minimized. We show that the two-stage robust matching is NP-hard under various cost functions and present constant approximation algorithms for different settings of our two-stage problem. Furthermore, we test our algorithms on real-life taxi data from the city of Shenzhen and show that they substantially improve upon myopic solutions and reduce the maximum wait time of the second-stage riders.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
