Mitigating Fairness and Efficiency Tradeoff in Vehicle-Dispatch Problems
Mitigating Fairness and Efficiency Tradeoff in Vehicle-Dispatch Problems
复制标题
减轻车辆调度问题中的公平性和效率权衡
DOI：
10.1007/978-3-031-18192-4_25
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Noda Itsuki
中科院分区：
文献类型：
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作者：
Ota Masato;Sakurai Yuko;Guo Mingyu;Noda Itsuki
我们提出了一种车辆和乘客之间的公平分配算法，以减轻按需乘车平台的效率和公平性权衡。网约车平台将乘客和司机真实的连接起来。虽然大多数研究都集中在开发一种最佳效率的分配方法，以最大限度地提高平台的利润，最佳效率可能会导致司机的利润不平等。因此，公平分配算法已经开始引起人工智能研究人员的注意。虽然已经提出了基于最大-最小公平性的公平分配算法，这是公平性的代表性概念，但是当多次进行分配时，驾驶员之间的收益不平等仍然存在。为了解决这种不平等，我们开发了一个公平的分配算法称为优先级分配算法PA（k），优先考虑司机与低累积利润，然后产生一个最佳的有效分配剩余的司机和乘客。我们还开发了一种方法，动态地确定在每个任务的优先级的数量。通过一个真实数据集的实验，证明了PA（k）算法在供给过剩的情况下，在效率和公平性上都优于现有的公平分配算法.
We propose a fair-assignment algorithm between vehicles and passengers to mitigate the efficiency and fairness tradeoff for on-demand ride-hailing platforms. Ride-hailing platforms connect passengers and drivers in real time. While most studies focused on developing an optimally efficient assignment method for maximizing the profit of the platform, optimal efficiency may lead to profit inequality for drivers. Therefore, fair-assignment algorithms have begun to attract attention from artificial-intelligence researchers. While a fair-assignment algorithm based on max-min fairness, which is a representative concept of fairness, has been proposed, profit inequality among drivers still remains when assignments are made multiple times. To address such inequality, we develop a fair-assignment algorithm called the priority assignment algorithmPA(k) to give priority to drivers with low cumulative profit then generate an optimally efficient assignment for the remaining drivers and passengers. We also develop a method of dynamically determining the number of priorities at each assignment. We experimentally demonstrated thatPA(k) outperforms the existing fair assignment algorithms in both efficiency and fairness in the case of excess supply by using a real-world dataset.