论文标题
带有战略服务器的乘车平台的接近最佳控制
Near Optimal Control in Ride Hailing Platforms with Strategic Servers
论文作者
论文摘要
在乘车骑行等在线市场中的应用中，我们研究了战略服务器如何影响系统性能。我们考虑了一个离散的时间过程，在该过程中，异质类型的客户和服务器到达。每个客户都加入他们的类型队列，而服务器可能会根据系统运营商发布的价格和不便成本加入其他类型的队列。然后，系统运算符被兼容图约束，决定匹配。目的是设计一个最佳控制（定价和匹配方案），以最大程度地提高利润，以减去预期的等待时间。我们开发一个通用框架，使我们能够分析广泛的战略行为。特别是，我们在正确定义的\ emph {成本函数}中编码服务器的行为，该行为可以针对各种设置量身定制。使用此一般成本函数，我们引入了一个新型的概率流体问题。概率流体模型为可实现的净利润提供了上限。然后，我们在大型市场制度下研究该系统，在该制度中，到达率通过$η$扩展，并提出了概率的两位数政策和最大重量匹配的政策，该政策最多可产生$ O（η^{1/3}）$的净利润。此外，根据广泛的客户定价政策，我们表明，任何匹配的策略的净利润损失至少为$ω（η^{1/3}）$。为了展示我们的框架的一般性，我们向模型和分析提供了多个扩展。我们通过介绍比较不同的成本模型并分析提出的定价和匹配策略的性能的数值模拟来结束讨论。
Motivated by applications in online marketplaces such as ride-hailing, we study how strategic servers impact the system performance. We consider a discrete-time process in which, heterogeneous types of customers and servers arrive. Each customer joins their type's queue, while servers might join a different type's queue depending on the prices posted by the system operator and an inconvenience cost. Then the system operator, constrained by a compatibility graph, decides the matching. The objective is to design an optimal control (pricing and matching scheme) to maximize the profit minus the expected waiting times. We develop a general framework that enables us to analyze a broad range of strategic behaviors. In particular, we encode servers' behavior in a properly defined \emph{cost function} that can be tailored to various settings. Using this general cost function, we introduce a novel probabilistic fluid problem. The probabilistic fluid model provides an upper bound on the achievable net profit. We then study the system under a large market regime in which the arrival rates are scaled by $η$ and present a probabilistic two-price policy and a max-weight matching policy which results in a net profit-loss of at most $O(η^{1/3})$. In addition, under a broad class of customer pricing policies, we show that any matching policy has net profit-loss of at least $Ω(η^{1/3})$. To show generality of our framework, we present multiple extensions to our model and analysis. We conclude the discussion by presenting numerical simulations comparing different cost models and analyzing performance of the proposed pricing and matching policies.
