Optimizing Long-Term Efficiency and Fairness in Ride-Hailing Under Budget Constraint via Joint Order Dispatching and Driver Repositioning
Jiahui Sun, Haiming Jin, Zhaoxing Yang, Lü Su
Abstract
Ride-hailing platforms (e.g., Uber and Didi Chuxing) have become increasingly popular in recent years. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Efficiency</i> has always been an important metric for such platforms. However, only focusing on efficiency inevitably ignores the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fairness</i> of driver incomes, which could impair the sustainability of ride-hailing systems. To optimize such two essential objectives, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">order dispatching</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">driver repositioning</i> play an important role, as they impact not only the immediate, but also the future order-serving outcomes of drivers. In practice, the platform offers monetary incentives to drivers for completing the repositioning and has a budget for the repositioning cost. Therefore, in this paper, we aim to exploit joint order dispatching and driver repositioning to optimize both long-term efficiency and fairness in ride-hailing under the budget constraint. To this end, we propose JDRCL, a novel multi-agent reinforcement learning framework, which integrates a group-based action representation that copes with the variable action space, and a primal-dual iterative training algorithm to learn a constraint-satisfying policy that maximizes both the worst and the overall incomes of drivers. Furthermore, we prove the asymptotic convergence rate of our training algorithm. Extensive experiments based on three real-world ride-hailing order datasets show that JDRCL outperforms state-of-the-art baselines on both efficiency and fairness.