Large-scale ride-hailing systems often combine real-time routing at the individual request level with a macroscopic Model Predictive Control (MPC) optimization for dynamic pricing and vehicle relocation. The MPC relies on a demand forecast and optimizes over a longer time horizon to compensate for the myopic nature of the routing optimization. However, the longer horizon increases computational complexity and forces the MPC to operate at coarser spatial-temporal granularity, degrading the quality of its decisions. This paper addresses these computational challenges by learning the MPC optimization. The resulting machine-learning model then serves as the optimization proxy and predicts its optimal solutions. This makes it possible to use the MPC at higher spatial-temporal fidelity, since the optimizations can be solved and learned offline. Experimental results show that the proposed approach improves quality of service on challenging instances from the New York City dataset.
翻译:大型搭车系统往往将个人请求层面的实时路由与大型模型预测控制优化(MPC)相结合,以优化动态定价和车辆搬迁。MPC依靠需求预测,并在更长的时间内优化,以弥补路由优化的短视性质。但是,较长的地平线增加了计算复杂性,迫使MPC以粗略的空间时空颗粒度运作,降低了其决定的质量。本文件通过学习MPC优化来应对这些计算挑战。随后产生的机器学习模式成为优化代用品,并预测最佳解决方案。这使得在更高的空间-时间忠贞度上使用MPC成为可能,因为优化可以解决,并学习离线。实验结果表明,拟议方法提高了纽约市数据集挑战性案例的服务质量。